refactor(rl): move grpcio guards to runtime entry points

# Conflicts:
#	src/lerobot/policies/factory.py

docs/source/hilserl.mdx

@@ -820,10 +820,10 @@ The LeRobot system uses a distributed actor-learner architecture for training. T
 
 Create a training configuration file (example available [here](https://huggingface.co/datasets/lerobot/config_examples/resolve/main/rl/train_config.json)). The training config is based on the main `TrainRLServerPipelineConfig` class in `lerobot/configs/train.py`.
 
-1. Configure the policy settings (`type="sac"`, `device`, etc.)
+1. Configure the policy settings (`type="gaussian_actor"`, `device`, etc.)
 2. Set `dataset` to your cropped dataset
 3. Configure environment settings with crop parameters
-4. Check the other parameters related to SAC in [configuration_sac.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/sac/configuration_sac.py#L79).
+4. Check the other parameters related to the Gaussian Actor in [configuration_gaussian_actor.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/gaussian_actor/configuration_gaussian_actor.py#L79).
 5. Verify that the `policy` config is correct with the right `input_features` and `output_features` for your task.
 
 **Starting the Learner**
@@ -926,7 +926,7 @@ The ideal behaviour is that your intervention rate should drop gradually during
 
 Some configuration values have a disproportionate impact on training stability and speed:
 
-- **`temperature_init`** (`policy.temperature_init`) – initial entropy temperature in SAC. Higher values encourage more exploration; lower values make the policy more deterministic early on. A good starting point is `1e-2`. We observed that setting it too high can make human interventions ineffective and slow down learning.
+- **`temperature_init`** (`algorithm.temperature_init`) – initial entropy temperature in SAC. Higher values encourage more exploration; lower values make the policy more deterministic early on. A good starting point is `1e-2`. We observed that setting it too high can make human interventions ineffective and slow down learning.
 - **`policy_parameters_push_frequency`** (`policy.actor_learner_config.policy_parameters_push_frequency`) – interval in _seconds_ between two weight pushes from the learner to the actor. The default is `4 s`. Decrease to **1-2 s** to provide fresher weights (at the cost of more network traffic); increase only if your connection is slow, as this will reduce sample efficiency.
 - **`storage_device`** (`policy.storage_device`) – device on which the learner keeps the policy parameters. If you have spare GPU memory, set this to `"cuda"` (instead of the default `"cpu"`). Keeping the weights on-GPU removes CPU→GPU transfer overhead and can significantly increase the number of learner updates per second.
 

examples/tutorial/rl/hilserl_example.py

@@ -4,13 +4,13 @@ from pathlib import Path
 from queue import Empty, Full
 
 import torch
-import torch.optim as optim
 
 from lerobot.datasets import LeRobotDataset
 from lerobot.envs.configs import HILSerlProcessorConfig, HILSerlRobotEnvConfig
-from lerobot.policies import SACConfig
-from lerobot.policies.sac.modeling_sac import SACPolicy
+from lerobot.policies import GaussianActorConfig
+from lerobot.policies.gaussian_actor.modeling_gaussian_actor import GaussianActorPolicy
 from lerobot.rewards.classifier.modeling_classifier import Classifier
+from lerobot.rl.algorithms.sac import SACAlgorithm, SACAlgorithmConfig
 from lerobot.rl.buffer import ReplayBuffer
 from lerobot.rl.gym_manipulator import make_robot_env
 from lerobot.robots.so_follower import SO100FollowerConfig
@@ -28,7 +28,7 @@ def run_learner(
     transitions_queue: mp.Queue,
     parameters_queue: mp.Queue,
     shutdown_event: mp.Event,
-    policy_learner: SACPolicy,
+    policy_learner: GaussianActorPolicy,
     online_buffer: ReplayBuffer,
     offline_buffer: ReplayBuffer,
     lr: float = 3e-4,
@@ -40,8 +40,9 @@ def run_learner(
     policy_learner.train()
     policy_learner.to(device)
 
-    # Create Adam optimizer from scratch - simple and clean
-    optimizer = optim.Adam(policy_learner.parameters(), lr=lr)
+    algo_config = SACAlgorithmConfig.from_policy_config(policy_learner.config)
+    algorithm = SACAlgorithm(policy=policy_learner, config=algo_config)
+    algorithm.make_optimizers_and_scheduler()
 
     print(f"[LEARNER] Online buffer capacity: {online_buffer.capacity}")
     print(f"[LEARNER] Offline buffer capacity: {offline_buffer.capacity}")
@@ -83,24 +84,26 @@ def run_learner(
                 else:
                     batch[key] = online_batch[key]
 
-            loss, _ = policy_learner.forward(batch)
+            def batch_iter(b=batch):
+                while True:
+                    yield b
 
-            optimizer.zero_grad()
-            loss.backward()
-            optimizer.step()
+            stats = algorithm.update(batch_iter())
             training_step += 1
 
             if training_step % LOG_EVERY == 0:
+                log_dict = stats.to_log_dict()
                 print(
-                    f"[LEARNER] Training step {training_step}, Loss: {loss.item():.4f}, "
+                    f"[LEARNER] Training step {training_step}, "
+                    f"critic_loss: {log_dict.get('critic', 'N/A'):.4f}, "
                     f"Buffers: Online={len(online_buffer)}, Offline={len(offline_buffer)}"
                 )
 
             # Send updated parameters to actor every 10 training steps
             if training_step % SEND_EVERY == 0:
                 try:
-                    state_dict = {k: v.cpu() for k, v in policy_learner.state_dict().items()}
-                    parameters_queue.put_nowait(state_dict)
+                    weights = algorithm.get_weights()
+                    parameters_queue.put_nowait(weights)
                     print("[LEARNER] Sent updated parameters to actor")
                 except Full:
                     # Missing write due to queue not being consumed (should happen rarely)
@@ -113,7 +116,7 @@ def run_actor(
     transitions_queue: mp.Queue,
     parameters_queue: mp.Queue,
     shutdown_event: mp.Event,
-    policy_actor: SACPolicy,
+    policy_actor: GaussianActorPolicy,
     reward_classifier: Classifier,
     env_cfg: HILSerlRobotEnvConfig,
     device: torch.device = "mps",
@@ -144,15 +147,15 @@ def run_actor(
 
             while step < MAX_STEPS_PER_EPISODE and not shutdown_event.is_set():
                 try:
-                    new_params = parameters_queue.get_nowait()
-                    policy_actor.load_state_dict(new_params)
+                    new_weights = parameters_queue.get_nowait()
+                    policy_actor.load_state_dict(new_weights)
                     print("[ACTOR] Updated policy parameters from learner")
                 except Empty:  # No new updated parameters available from learner, waiting
                     pass
 
-                # Get action from policy
+                # Get action from policy (returns full action: continuous + discrete)
                 policy_obs = make_policy_obs(obs, device=device)
-                action_tensor = policy_actor.select_action(policy_obs)  # predicts a single action
+                action_tensor = policy_actor.select_action(policy_obs)
                 action = action_tensor.squeeze(0).cpu().numpy()
 
                 # Step environment
@@ -261,14 +264,14 @@ def main():
     action_features = hw_to_dataset_features(env.robot.action_features, "action")
 
     # Create SAC policy for action selection
-    policy_cfg = SACConfig(
+    policy_cfg = GaussianActorConfig(
         device=device,
         input_features=obs_features,
         output_features=action_features,
     )
 
-    policy_actor = SACPolicy(policy_cfg)
-    policy_learner = SACPolicy(policy_cfg)
+    policy_actor = GaussianActorPolicy(policy_cfg)
+    policy_learner = GaussianActorPolicy(policy_cfg)
 
     demonstrations_repo_id = "lerobot/example_hil_serl_dataset"
     offline_dataset = LeRobotDataset(repo_id=demonstrations_repo_id)

src/lerobot/common/train_utils.py

@@ -99,6 +99,7 @@ def save_checkpoint(
         optimizer (Optimizer | None, optional): The optimizer to save the state from. Defaults to None.
         scheduler (LRScheduler | None, optional): The scheduler to save the state from. Defaults to None.
         preprocessor: The preprocessor/pipeline to save. Defaults to None.
+        postprocessor: The postprocessor/pipeline to save. Defaults to None.
     """
     pretrained_dir = checkpoint_dir / PRETRAINED_MODEL_DIR
     policy.save_pretrained(pretrained_dir)

src/lerobot/configs/train.py

@@ -267,10 +267,3 @@ class TrainPipelineConfig(HubMixin):
 
         with draccus.config_type("json"):
             return draccus.parse(cls, config_file, args=cli_args)
-
-
-@dataclass(kw_only=True)
-class TrainRLServerPipelineConfig(TrainPipelineConfig):
-    # NOTE: In RL, we don't need an offline dataset
-    # TODO: Make `TrainPipelineConfig.dataset` optional
-    dataset: DatasetConfig | None = None  # type: ignore[assignment] # because the parent class has made it's type non-optional

src/lerobot/policies/__init__.py

@@ -18,13 +18,13 @@ from .act.configuration_act import ACTConfig as ACTConfig
 from .diffusion.configuration_diffusion import DiffusionConfig as DiffusionConfig
 from .eo1.configuration_eo1 import EO1Config as EO1Config
 from .factory import get_policy_class, make_policy, make_policy_config, make_pre_post_processors
+from .gaussian_actor.configuration_gaussian_actor import GaussianActorConfig as GaussianActorConfig
 from .groot.configuration_groot import GrootConfig as GrootConfig
 from .multi_task_dit.configuration_multi_task_dit import MultiTaskDiTConfig as MultiTaskDiTConfig
 from .pi0.configuration_pi0 import PI0Config as PI0Config
 from .pi0_fast.configuration_pi0_fast import PI0FastConfig as PI0FastConfig
 from .pi05.configuration_pi05 import PI05Config as PI05Config
 from .pretrained import PreTrainedPolicy as PreTrainedPolicy
-from .sac.configuration_sac import SACConfig as SACConfig
 from .smolvla.configuration_smolvla import SmolVLAConfig as SmolVLAConfig
 from .tdmpc.configuration_tdmpc import TDMPCConfig as TDMPCConfig
 from .utils import make_robot_action, prepare_observation_for_inference
@@ -32,21 +32,21 @@ from .vqbet.configuration_vqbet import VQBeTConfig as VQBeTConfig
 from .wall_x.configuration_wall_x import WallXConfig as WallXConfig
 from .xvla.configuration_xvla import XVLAConfig as XVLAConfig
 
-# NOTE: Policy modeling classes (e.g., SACPolicy) are intentionally NOT re-exported here.
+# NOTE: Policy modeling classes (e.g., GaussianActorPolicy) are intentionally NOT re-exported here.
 # They have heavy optional dependencies and are loaded lazily via get_policy_class().
-# Import directly: ``from lerobot.policies.sac.modeling_sac import SACPolicy``
+# Import directly: ``from lerobot.policies.gaussian_actor.modeling_gaussian_actor import GaussianActorPolicy``
 
 __all__ = [
     # Configuration classes
     "ACTConfig",
     "DiffusionConfig",
+    "EO1Config",
+    "GaussianActorConfig",
     "GrootConfig",
     "MultiTaskDiTConfig",
-    "EO1Config",
     "PI0Config",
     "PI0FastConfig",
     "PI05Config",
-    "SACConfig",
     "SmolVLAConfig",
     "TDMPCConfig",
     "VQBeTConfig",

src/lerobot/policies/factory.py

@@ -47,12 +47,12 @@ from lerobot.utils.feature_utils import dataset_to_policy_features
 from .act.configuration_act import ACTConfig
 from .diffusion.configuration_diffusion import DiffusionConfig
 from .eo1.configuration_eo1 import EO1Config
+from .gaussian_actor.configuration_gaussian_actor import GaussianActorConfig
 from .groot.configuration_groot import GrootConfig
 from .multi_task_dit.configuration_multi_task_dit import MultiTaskDiTConfig
 from .pi0.configuration_pi0 import PI0Config
 from .pi05.configuration_pi05 import PI05Config
 from .pretrained import PreTrainedPolicy
-from .sac.configuration_sac import SACConfig
 from .smolvla.configuration_smolvla import SmolVLAConfig
 from .tdmpc.configuration_tdmpc import TDMPCConfig
 from .utils import validate_visual_features_consistency
@@ -88,7 +88,7 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:
 
     Args:
         name: The name of the policy. Supported names are "tdmpc", "diffusion", "act",
-            "multi_task_dit", "vqbet", "pi0", "pi05", "sac", "smolvla", "wall_x".
+            "multi_task_dit", "vqbet", "pi0", "pi05", "gaussian_actor", "smolvla", "wall_x".
     Returns:
         The policy class corresponding to the given name.
 
@@ -127,10 +127,10 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:
         from .pi05.modeling_pi05 import PI05Policy
 
         return PI05Policy
-    elif name == "sac":
-        from .sac.modeling_sac import SACPolicy
+    elif name == "gaussian_actor":
+        from .gaussian_actor.modeling_gaussian_actor import GaussianActorPolicy
 
-        return SACPolicy
+        return GaussianActorPolicy
     elif name == "smolvla":
         from .smolvla.modeling_smolvla import SmolVLAPolicy
 
@@ -167,7 +167,7 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
 
     Args:
         policy_type: The type of the policy. Supported types include "tdmpc",
-                     "multi_task_dit", "diffusion", "act", "vqbet", "pi0", "pi05", "sac",
+                     "multi_task_dit", "diffusion", "act", "vqbet", "pi0", "pi05", "gaussian_actor",
                      "smolvla", "wall_x".
         **kwargs: Keyword arguments to be passed to the configuration class constructor.
 
@@ -191,8 +191,8 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
         return PI0Config(**kwargs)
     elif policy_type == "pi05":
         return PI05Config(**kwargs)
-    elif policy_type == "sac":
-        return SACConfig(**kwargs)
+    elif policy_type == "gaussian_actor":
+        return GaussianActorConfig(**kwargs)
     elif policy_type == "smolvla":
         return SmolVLAConfig(**kwargs)
     elif policy_type == "groot":
@@ -365,10 +365,10 @@ def make_pre_post_processors(
             dataset_stats=kwargs.get("dataset_stats"),
         )
 
-    elif isinstance(policy_cfg, SACConfig):
-        from .sac.processor_sac import make_sac_pre_post_processors
+    elif isinstance(policy_cfg, GaussianActorConfig):
+        from .gaussian_actor.processor_gaussian_actor import make_gaussian_actor_pre_post_processors
 
-        processors = make_sac_pre_post_processors(
+        processors = make_gaussian_actor_pre_post_processors(
             config=policy_cfg,
             dataset_stats=kwargs.get("dataset_stats"),
         )

src/lerobot/policies/gaussian_actor/__init__.py

@@ -12,8 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .configuration_sac import SACConfig
-from .modeling_sac import SACPolicy
-from .processor_sac import make_sac_pre_post_processors
+from .configuration_gaussian_actor import GaussianActorConfig
+from .modeling_gaussian_actor import GaussianActorPolicy
+from .processor_gaussian_actor import make_gaussian_actor_pre_post_processors
 
-__all__ = ["SACConfig", "SACPolicy", "make_sac_pre_post_processors"]
+__all__ = ["GaussianActorConfig", "GaussianActorPolicy", "make_gaussian_actor_pre_post_processors"]

src/lerobot/policies/gaussian_actor/configuration_gaussian_actor.py

@@ -1,4 +1,4 @@
-# !/usr/bin/env python
+#!/usr/bin/env python
 
 # Copyright 2025 The HuggingFace Inc. team.
 # All rights reserved.
@@ -75,18 +75,19 @@ class PolicyConfig:
     init_final: float = 0.05
 
 
-@PreTrainedConfig.register_subclass("sac")
+@PreTrainedConfig.register_subclass("gaussian_actor")
 @dataclass
-class SACConfig(PreTrainedConfig):
-    """Soft Actor-Critic (SAC) configuration.
+class GaussianActorConfig(PreTrainedConfig):
+    """Gaussian actor configuration.
 
-    SAC is an off-policy actor-critic deep RL algorithm based on the maximum entropy
-    reinforcement learning framework. It learns a policy and a Q-function simultaneously
-    using experience collected from the environment.
+    This configures the policy-side (actor + observation encoder) of a Gaussian
+    policy, as used by SAC and related maximum-entropy continuous-control algorithms.
+    By default the actor output is a tanh-squashed diagonal Gaussian
+    (``TanhMultivariateNormalDiag``); the tanh squashing can be disabled via
+    ``policy_kwargs.use_tanh_squash``. The critics, temperature, and Bellman-update
+    logic live on the algorithm side (see ``lerobot.rl.algorithms.sac``).
 
-    This configuration class contains all the parameters needed to define a SAC agent,
-    including network architectures, optimization settings, and algorithm-specific
-    hyperparameters.
+    CLI: ``--policy.type=gaussian_actor``.
     """
 
     # Mapping of feature types to normalization modes
@@ -122,7 +123,7 @@ class SACConfig(PreTrainedConfig):
     device: str = "cpu"
     # Device to store the model on
     storage_device: str = "cpu"
-    # Name of the vision encoder model (Set to "helper2424/resnet10" for hil serl resnet10)
+    # Name of the vision encoder model (Set to "lerobot/resnet10" for hil serl resnet10)
     vision_encoder_name: str | None = None
     # Whether to freeze the vision encoder during training
     freeze_vision_encoder: bool = True
@@ -135,7 +136,13 @@ class SACConfig(PreTrainedConfig):
     # Dimension of the image embedding pooling
     image_embedding_pooling_dim: int = 8
 
-    # Training parameter
+    # Encoder architecture
+    # Hidden dimension size for the state encoder
+    state_encoder_hidden_dim: int = 256
+    # Dimension of the latent space
+    latent_dim: int = 256
+
+    # Online training (TODO(Khalil): relocate to TrainRLServerPipelineConfig)
     # Number of steps for online training
     online_steps: int = 1000000
     # Capacity of the online replay buffer
@@ -146,67 +153,38 @@ class SACConfig(PreTrainedConfig):
     async_prefetch: bool = False
     # Number of steps before learning starts
     online_step_before_learning: int = 100
-    # Frequency of policy updates
-    policy_update_freq: int = 1
 
-    # SAC algorithm parameters
-    # Discount factor for the SAC algorithm
-    discount: float = 0.99
-    # Initial temperature value
-    temperature_init: float = 1.0
-    # Number of critics in the ensemble
-    num_critics: int = 2
-    # Number of subsampled critics for training
-    num_subsample_critics: int | None = None
-    # Learning rate for the critic network
-    critic_lr: float = 3e-4
-    # Learning rate for the actor network
-    actor_lr: float = 3e-4
-    # Learning rate for the temperature parameter
-    temperature_lr: float = 3e-4
-    # Weight for the critic target update
-    critic_target_update_weight: float = 0.005
-    # Update-to-data ratio for the UTD algorithm (If you want enable utd_ratio, you need to set it to >1)
-    utd_ratio: int = 1
-    # Hidden dimension size for the state encoder
-    state_encoder_hidden_dim: int = 256
-    # Dimension of the latent space
-    latent_dim: int = 256
-    # Target entropy for the SAC algorithm
-    target_entropy: float | None = None
-    # Whether to use backup entropy for the SAC algorithm
-    use_backup_entropy: bool = True
-    # Gradient clipping norm for the SAC algorithm
-    grad_clip_norm: float = 40.0
-
-    # Network configuration
-    # Configuration for the critic network architecture
-    critic_network_kwargs: CriticNetworkConfig = field(default_factory=CriticNetworkConfig)
-    # Configuration for the actor network architecture
-    actor_network_kwargs: ActorNetworkConfig = field(default_factory=ActorNetworkConfig)
-    # Configuration for the policy parameters
-    policy_kwargs: PolicyConfig = field(default_factory=PolicyConfig)
-    # Configuration for the discrete critic network
-    discrete_critic_network_kwargs: CriticNetworkConfig = field(default_factory=CriticNetworkConfig)
+    # Actor-learner transport (TODO(Khalil): relocate to TrainRLServerPipelineConfig).
     # Configuration for actor-learner architecture
     actor_learner_config: ActorLearnerConfig = field(default_factory=ActorLearnerConfig)
     # Configuration for concurrency settings (you can use threads or processes for the actor and learner)
     concurrency: ConcurrencyConfig = field(default_factory=ConcurrencyConfig)
 
-    # Optimizations
-    use_torch_compile: bool = True
+    # Network architecture
+    # Configuration for the actor network architecture
+    actor_network_kwargs: ActorNetworkConfig = field(default_factory=ActorNetworkConfig)
+    # Configuration for the policy parameters (Gaussian head)
+    policy_kwargs: PolicyConfig = field(default_factory=PolicyConfig)
+    # Configuration for the discrete critic network
+    discrete_critic_network_kwargs: CriticNetworkConfig = field(default_factory=CriticNetworkConfig)
 
     def __post_init__(self):
         super().__post_init__()
-        # Any validation specific to SAC configuration
+        # Any validation specific to GaussianActor configuration
 
     def get_optimizer_preset(self) -> MultiAdamConfig:
+        # Default learning rate used to satisfy the abstract ``get_optimizer_preset()``
+        # contract from ``PreTrainedConfig``. The actual optimizers used during RL
+        # training are built by ``SACAlgorithm.make_optimizers_and_scheduler()`` from
+        # ``SACAlgorithmConfig.{actor_lr,critic_lr,temperature_lr}`` and fully bypass
+        # this preset.
+        default_lr = 3e-4
         return MultiAdamConfig(
             weight_decay=0.0,
             optimizer_groups={
-                "actor": {"lr": self.actor_lr},
-                "critic": {"lr": self.critic_lr},
-                "temperature": {"lr": self.temperature_lr},
+                "actor": {"lr": default_lr},
+                "critic": {"lr": default_lr},
+                "temperature": {"lr": default_lr},
             },
         )
 

src/lerobot/policies/gaussian_actor/modeling_gaussian_actor.py

@@ -15,37 +15,34 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import math
 from collections.abc import Callable
 from dataclasses import asdict
-from typing import Literal
+from typing import Any
 
-import einops
-import numpy as np
 import torch
 import torch.nn as nn
-import torch.nn.functional as F  # noqa: N812
 from torch import Tensor
 from torch.distributions import MultivariateNormal, TanhTransform, Transform, TransformedDistribution
 
 from lerobot.utils.constants import ACTION, OBS_ENV_STATE, OBS_STATE
+from lerobot.utils.transition import move_state_dict_to_device
 
 from ..pretrained import PreTrainedPolicy
 from ..utils import get_device_from_parameters
-from .configuration_sac import SACConfig, is_image_feature
+from .configuration_gaussian_actor import GaussianActorConfig, is_image_feature
 
 DISCRETE_DIMENSION_INDEX = -1  # Gripper is always the last dimension
 
 
-class SACPolicy(
+class GaussianActorPolicy(
     PreTrainedPolicy,
 ):
-    config_class = SACConfig
-    name = "sac"
+    config_class = GaussianActorConfig
+    name = "gaussian_actor"
 
     def __init__(
         self,
-        config: SACConfig | None = None,
+        config: GaussianActorConfig | None = None,
     ):
         super().__init__(config)
         config.validate_features()
@@ -54,9 +51,8 @@ class SACPolicy(
         # Determine action dimension and initialize all components
         continuous_action_dim = config.output_features[ACTION].shape[0]
         self._init_encoders()
-        self._init_critics(continuous_action_dim)
         self._init_actor(continuous_action_dim)
-        self._init_temperature()
+        self._init_discrete_critic()
 
     def get_optim_params(self) -> dict:
         optim_params = {
@@ -65,11 +61,7 @@ class SACPolicy(
                 for n, p in self.actor.named_parameters()
                 if not n.startswith("encoder") or not self.shared_encoder
             ],
-            "critic": self.critic_ensemble.parameters(),
-            "temperature": self.log_alpha,
         }
-        if self.config.num_discrete_actions is not None:
-            optim_params["discrete_critic"] = self.discrete_critic.parameters()
         return optim_params
 
     def reset(self):
@@ -79,7 +71,9 @@ class SACPolicy(
     @torch.no_grad()
     def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor:
         """Predict a chunk of actions given environment observations."""
-        raise NotImplementedError("SACPolicy does not support action chunking. It returns single actions!")
+        raise NotImplementedError(
+            "GaussianActorPolicy does not support action chunking. It returns single actions!"
+        )
 
     @torch.no_grad()
     def select_action(self, batch: dict[str, Tensor]) -> Tensor:
@@ -92,360 +86,53 @@ class SACPolicy(
         actions, _, _ = self.actor(batch, observations_features)
 
         if self.config.num_discrete_actions is not None:
-            discrete_action_value = self.discrete_critic(batch, observations_features)
-            discrete_action = torch.argmax(discrete_action_value, dim=-1, keepdim=True)
+            if self.discrete_critic is not None:
+                discrete_action_value = self.discrete_critic(batch, observations_features)
+                discrete_action = torch.argmax(discrete_action_value, dim=-1, keepdim=True)
+            else:
+                discrete_action = torch.ones(
+                    (*actions.shape[:-1], 1), device=actions.device, dtype=actions.dtype
+                )
             actions = torch.cat([actions, discrete_action], dim=-1)
 
         return actions
 
-    def critic_forward(
-        self,
-        observations: dict[str, Tensor],
-        actions: Tensor,
-        use_target: bool = False,
-        observation_features: Tensor | None = None,
-    ) -> Tensor:
-        """Forward pass through a critic network ensemble
+    def forward(self, batch: dict[str, Tensor | dict[str, Tensor]]) -> dict[str, Tensor]:
+        """Actor forward pass: sample actions and return log-probabilities.
 
         Args:
-            observations: Dictionary of observations
-            actions: Action tensor
-            use_target: If True, use target critics, otherwise use ensemble critics
+            batch: A flat observation dict, or a training dict containing
+                ``"state"`` (observations) and optionally ``"observation_feature"``
+                (pre-computed encoder features).
 
         Returns:
-            Tensor of Q-values from all critics
+            Dict with ``"action"``, ``"log_prob"``, and ``"action_mean"`` tensors.
         """
+        observations = batch.get("state", batch)
+        observation_features = batch.get("observation_feature") if isinstance(batch, dict) else None
+        actions, log_probs, means = self.actor(observations, observation_features)
+        return {"action": actions, "log_prob": log_probs, "action_mean": means}
 
-        critics = self.critic_target if use_target else self.critic_ensemble
-        q_values = critics(observations, actions, observation_features)
-        return q_values
+    def load_actor_weights(self, state_dicts: dict[str, Any], device: str | torch.device = "cpu") -> None:
+        actor_state_dict = move_state_dict_to_device(state_dicts["policy"], device=device)
+        self.actor.load_state_dict(actor_state_dict)
 
-    def discrete_critic_forward(
-        self, observations, use_target=False, observation_features=None
-    ) -> torch.Tensor:
-        """Forward pass through a discrete critic network
-
-        Args:
-            observations: Dictionary of observations
-            use_target: If True, use target critics, otherwise use ensemble critics
-            observation_features: Optional pre-computed observation features to avoid recomputing encoder output
-
-        Returns:
-            Tensor of Q-values from the discrete critic network
-        """
-        discrete_critic = self.discrete_critic_target if use_target else self.discrete_critic
-        q_values = discrete_critic(observations, observation_features)
-        return q_values
-
-    def forward(
-        self,
-        batch: dict[str, Tensor | dict[str, Tensor]],
-        model: Literal["actor", "critic", "temperature", "discrete_critic"] = "critic",
-    ) -> dict[str, Tensor]:
-        """Compute the loss for the given model
-
-        Args:
-            batch: Dictionary containing:
-                - action: Action tensor
-                - reward: Reward tensor
-                - state: Observations tensor dict
-                - next_state: Next observations tensor dict
-                - done: Done mask tensor
-                - observation_feature: Optional pre-computed observation features
-                - next_observation_feature: Optional pre-computed next observation features
-            model: Which model to compute the loss for ("actor", "critic", "discrete_critic", or "temperature")
-
-        Returns:
-            The computed loss tensor
-        """
-        # Extract common components from batch
-        actions: Tensor = batch[ACTION]
-        observations: dict[str, Tensor] = batch["state"]
-        observation_features: Tensor = batch.get("observation_feature")
-
-        if model == "critic":
-            # Extract critic-specific components
-            rewards: Tensor = batch["reward"]
-            next_observations: dict[str, Tensor] = batch["next_state"]
-            done: Tensor = batch["done"]
-            next_observation_features: Tensor = batch.get("next_observation_feature")
-
-            loss_critic = self.compute_loss_critic(
-                observations=observations,
-                actions=actions,
-                rewards=rewards,
-                next_observations=next_observations,
-                done=done,
-                observation_features=observation_features,
-                next_observation_features=next_observation_features,
+        if "discrete_critic" in state_dicts and self.discrete_critic is not None:
+            discrete_critic_state_dict = move_state_dict_to_device(
+                state_dicts["discrete_critic"], device=device
             )
-
-            return {"loss_critic": loss_critic}
-
-        if model == "discrete_critic" and self.config.num_discrete_actions is not None:
-            # Extract critic-specific components
-            rewards: Tensor = batch["reward"]
-            next_observations: dict[str, Tensor] = batch["next_state"]
-            done: Tensor = batch["done"]
-            next_observation_features: Tensor = batch.get("next_observation_feature")
-            complementary_info = batch.get("complementary_info")
-            loss_discrete_critic = self.compute_loss_discrete_critic(
-                observations=observations,
-                actions=actions,
-                rewards=rewards,
-                next_observations=next_observations,
-                done=done,
-                observation_features=observation_features,
-                next_observation_features=next_observation_features,
-                complementary_info=complementary_info,
-            )
-            return {"loss_discrete_critic": loss_discrete_critic}
-        if model == "actor":
-            return {
-                "loss_actor": self.compute_loss_actor(
-                    observations=observations,
-                    observation_features=observation_features,
-                )
-            }
-
-        if model == "temperature":
-            return {
-                "loss_temperature": self.compute_loss_temperature(
-                    observations=observations,
-                    observation_features=observation_features,
-                )
-            }
-
-        raise ValueError(f"Unknown model type: {model}")
-
-    def update_target_networks(self):
-        """Update target networks with exponential moving average"""
-        for target_param, param in zip(
-            self.critic_target.parameters(),
-            self.critic_ensemble.parameters(),
-            strict=True,
-        ):
-            target_param.data.copy_(
-                param.data * self.config.critic_target_update_weight
-                + target_param.data * (1.0 - self.config.critic_target_update_weight)
-            )
-        if self.config.num_discrete_actions is not None:
-            for target_param, param in zip(
-                self.discrete_critic_target.parameters(),
-                self.discrete_critic.parameters(),
-                strict=True,
-            ):
-                target_param.data.copy_(
-                    param.data * self.config.critic_target_update_weight
-                    + target_param.data * (1.0 - self.config.critic_target_update_weight)
-                )
-
-    @property
-    def temperature(self) -> float:
-        """Return the current temperature value, always in sync with log_alpha."""
-        return self.log_alpha.exp().item()
-
-    def compute_loss_critic(
-        self,
-        observations,
-        actions,
-        rewards,
-        next_observations,
-        done,
-        observation_features: Tensor | None = None,
-        next_observation_features: Tensor | None = None,
-    ) -> Tensor:
-        with torch.no_grad():
-            next_action_preds, next_log_probs, _ = self.actor(next_observations, next_observation_features)
-
-            # 2- compute q targets
-            q_targets = self.critic_forward(
-                observations=next_observations,
-                actions=next_action_preds,
-                use_target=True,
-                observation_features=next_observation_features,
-            )
-
-            # subsample critics to prevent overfitting if use high UTD (update to date)
-            # TODO: Get indices before forward pass to avoid unnecessary computation
-            if self.config.num_subsample_critics is not None:
-                indices = torch.randperm(self.config.num_critics)
-                indices = indices[: self.config.num_subsample_critics]
-                q_targets = q_targets[indices]
-
-            # critics subsample size
-            min_q, _ = q_targets.min(dim=0)  # Get values from min operation
-            if self.config.use_backup_entropy:
-                min_q = min_q - (self.temperature * next_log_probs)
-
-            td_target = rewards + (1 - done) * self.config.discount * min_q
-
-        # 3- compute predicted qs
-        if self.config.num_discrete_actions is not None:
-            # NOTE: We only want to keep the continuous action part
-            # In the buffer we have the full action space (continuous + discrete)
-            # We need to split them before concatenating them in the critic forward
-            actions: Tensor = actions[:, :DISCRETE_DIMENSION_INDEX]
-        q_preds = self.critic_forward(
-            observations=observations,
-            actions=actions,
-            use_target=False,
-            observation_features=observation_features,
-        )
-
-        # 4- Calculate loss
-        # Compute state-action value loss (TD loss) for all of the Q functions in the ensemble.
-        td_target_duplicate = einops.repeat(td_target, "b -> e b", e=q_preds.shape[0])
-        # You compute the mean loss of the batch for each critic and then to compute the final loss you sum them up
-        critics_loss = (
-            F.mse_loss(
-                input=q_preds,
-                target=td_target_duplicate,
-                reduction="none",
-            ).mean(dim=1)
-        ).sum()
-        return critics_loss
-
-    def compute_loss_discrete_critic(
-        self,
-        observations,
-        actions,
-        rewards,
-        next_observations,
-        done,
-        observation_features=None,
-        next_observation_features=None,
-        complementary_info=None,
-    ):
-        # NOTE: We only want to keep the discrete action part
-        # In the buffer we have the full action space (continuous + discrete)
-        # We need to split them before concatenating them in the critic forward
-        actions_discrete: Tensor = actions[:, DISCRETE_DIMENSION_INDEX:].clone()
-        actions_discrete = torch.round(actions_discrete)
-        actions_discrete = actions_discrete.long()
-
-        discrete_penalties: Tensor | None = None
-        if complementary_info is not None:
-            discrete_penalties: Tensor | None = complementary_info.get("discrete_penalty")
-
-        with torch.no_grad():
-            # For DQN, select actions using online network, evaluate with target network
-            next_discrete_qs = self.discrete_critic_forward(
-                next_observations, use_target=False, observation_features=next_observation_features
-            )
-            best_next_discrete_action = torch.argmax(next_discrete_qs, dim=-1, keepdim=True)
-
-            # Get target Q-values from target network
-            target_next_discrete_qs = self.discrete_critic_forward(
-                observations=next_observations,
-                use_target=True,
-                observation_features=next_observation_features,
-            )
-
-            # Use gather to select Q-values for best actions
-            target_next_discrete_q = torch.gather(
-                target_next_discrete_qs, dim=1, index=best_next_discrete_action
-            ).squeeze(-1)
-
-            # Compute target Q-value with Bellman equation
-            rewards_discrete = rewards
-            if discrete_penalties is not None:
-                rewards_discrete = rewards + discrete_penalties
-            target_discrete_q = rewards_discrete + (1 - done) * self.config.discount * target_next_discrete_q
-
-        # Get predicted Q-values for current observations
-        predicted_discrete_qs = self.discrete_critic_forward(
-            observations=observations, use_target=False, observation_features=observation_features
-        )
-
-        # Use gather to select Q-values for taken actions
-        predicted_discrete_q = torch.gather(predicted_discrete_qs, dim=1, index=actions_discrete).squeeze(-1)
-
-        # Compute MSE loss between predicted and target Q-values
-        discrete_critic_loss = F.mse_loss(input=predicted_discrete_q, target=target_discrete_q)
-        return discrete_critic_loss
-
-    def compute_loss_temperature(self, observations, observation_features: Tensor | None = None) -> Tensor:
-        """Compute the temperature loss"""
-        # calculate temperature loss
-        with torch.no_grad():
-            _, log_probs, _ = self.actor(observations, observation_features)
-        temperature_loss = (-self.log_alpha.exp() * (log_probs + self.target_entropy)).mean()
-        return temperature_loss
-
-    def compute_loss_actor(
-        self,
-        observations,
-        observation_features: Tensor | None = None,
-    ) -> Tensor:
-        actions_pi, log_probs, _ = self.actor(observations, observation_features)
-
-        q_preds = self.critic_forward(
-            observations=observations,
-            actions=actions_pi,
-            use_target=False,
-            observation_features=observation_features,
-        )
-        min_q_preds = q_preds.min(dim=0)[0]
-
-        actor_loss = ((self.temperature * log_probs) - min_q_preds).mean()
-        return actor_loss
+            self.discrete_critic.load_state_dict(discrete_critic_state_dict)
 
     def _init_encoders(self):
         """Initialize shared or separate encoders for actor and critic."""
         self.shared_encoder = self.config.shared_encoder
-        self.encoder_critic = SACObservationEncoder(self.config)
+        self.encoder_critic = GaussianActorObservationEncoder(self.config)
         self.encoder_actor = (
-            self.encoder_critic if self.shared_encoder else SACObservationEncoder(self.config)
+            self.encoder_critic if self.shared_encoder else GaussianActorObservationEncoder(self.config)
         )
 
-    def _init_critics(self, continuous_action_dim):
-        """Build critic ensemble, targets, and optional discrete critic."""
-        heads = [
-            CriticHead(
-                input_dim=self.encoder_critic.output_dim + continuous_action_dim,
-                **asdict(self.config.critic_network_kwargs),
-            )
-            for _ in range(self.config.num_critics)
-        ]
-        self.critic_ensemble = CriticEnsemble(encoder=self.encoder_critic, ensemble=heads)
-        target_heads = [
-            CriticHead(
-                input_dim=self.encoder_critic.output_dim + continuous_action_dim,
-                **asdict(self.config.critic_network_kwargs),
-            )
-            for _ in range(self.config.num_critics)
-        ]
-        self.critic_target = CriticEnsemble(encoder=self.encoder_critic, ensemble=target_heads)
-        self.critic_target.load_state_dict(self.critic_ensemble.state_dict())
-
-        if self.config.use_torch_compile:
-            self.critic_ensemble = torch.compile(self.critic_ensemble)
-            self.critic_target = torch.compile(self.critic_target)
-
-        if self.config.num_discrete_actions is not None:
-            self._init_discrete_critics()
-
-    def _init_discrete_critics(self):
-        """Build discrete discrete critic ensemble and target networks."""
-        self.discrete_critic = DiscreteCritic(
-            encoder=self.encoder_critic,
-            input_dim=self.encoder_critic.output_dim,
-            output_dim=self.config.num_discrete_actions,
-            **asdict(self.config.discrete_critic_network_kwargs),
-        )
-        self.discrete_critic_target = DiscreteCritic(
-            encoder=self.encoder_critic,
-            input_dim=self.encoder_critic.output_dim,
-            output_dim=self.config.num_discrete_actions,
-            **asdict(self.config.discrete_critic_network_kwargs),
-        )
-
-        # TODO: (maractingi, azouitine) Compile the discrete critic
-        self.discrete_critic_target.load_state_dict(self.discrete_critic.state_dict())
-
     def _init_actor(self, continuous_action_dim):
-        """Initialize policy actor network and default target entropy."""
+        """Initialize policy actor network."""
         # NOTE: The actor select only the continuous action part
         self.actor = Policy(
             encoder=self.encoder_actor,
@@ -455,21 +142,25 @@ class SACPolicy(
             **asdict(self.config.policy_kwargs),
         )
 
-        self.target_entropy = self.config.target_entropy
-        if self.target_entropy is None:
-            dim = continuous_action_dim + (1 if self.config.num_discrete_actions is not None else 0)
-            self.target_entropy = -np.prod(dim) / 2
+    def _init_discrete_critic(self) -> None:
+        """Initialize discrete critic network."""
+        if self.config.num_discrete_actions is None:
+            self.discrete_critic = None
+            return
 
-    def _init_temperature(self) -> None:
-        """Set up temperature parameter (log_alpha)."""
-        temp_init = self.config.temperature_init
-        self.log_alpha = nn.Parameter(torch.tensor([math.log(temp_init)]))
+        # TODO(Khalil): Compile the discrete critic
+        self.discrete_critic = DiscreteCritic(
+            encoder=self.encoder_critic,
+            input_dim=self.encoder_critic.output_dim,
+            output_dim=self.config.num_discrete_actions,
+            **asdict(self.config.discrete_critic_network_kwargs),
+        )
 
 
-class SACObservationEncoder(nn.Module):
+class GaussianActorObservationEncoder(nn.Module):
     """Encode image and/or state vector observations."""
 
-    def __init__(self, config: SACConfig) -> None:
+    def __init__(self, config: GaussianActorConfig) -> None:
         super().__init__()
         self.config = config
         self._init_image_layers()
@@ -677,84 +368,6 @@ class MLP(nn.Module):
         return self.net(x)
 
 
-class CriticHead(nn.Module):
-    def __init__(
-        self,
-        input_dim: int,
-        hidden_dims: list[int],
-        activations: Callable[[torch.Tensor], torch.Tensor] | str = nn.SiLU(),
-        activate_final: bool = False,
-        dropout_rate: float | None = None,
-        init_final: float | None = None,
-        final_activation: Callable[[torch.Tensor], torch.Tensor] | str | None = None,
-    ):
-        super().__init__()
-        self.net = MLP(
-            input_dim=input_dim,
-            hidden_dims=hidden_dims,
-            activations=activations,
-            activate_final=activate_final,
-            dropout_rate=dropout_rate,
-            final_activation=final_activation,
-        )
-        self.output_layer = nn.Linear(in_features=hidden_dims[-1], out_features=1)
-        if init_final is not None:
-            nn.init.uniform_(self.output_layer.weight, -init_final, init_final)
-            nn.init.uniform_(self.output_layer.bias, -init_final, init_final)
-        else:
-            orthogonal_init()(self.output_layer.weight)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return self.output_layer(self.net(x))
-
-
-class CriticEnsemble(nn.Module):
-    """
-    CriticEnsemble wraps multiple CriticHead modules into an ensemble.
-
-    Args:
-        encoder (SACObservationEncoder): encoder for observations.
-        ensemble (List[CriticHead]): list of critic heads.
-        init_final (float | None): optional initializer scale for final layers.
-
-    Forward returns a tensor of shape (num_critics, batch_size) containing Q-values.
-    """
-
-    def __init__(
-        self,
-        encoder: SACObservationEncoder,
-        ensemble: list[CriticHead],
-        init_final: float | None = None,
-    ):
-        super().__init__()
-        self.encoder = encoder
-        self.init_final = init_final
-        self.critics = nn.ModuleList(ensemble)
-
-    def forward(
-        self,
-        observations: dict[str, torch.Tensor],
-        actions: torch.Tensor,
-        observation_features: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        device = get_device_from_parameters(self)
-        # Move each tensor in observations to device
-        observations = {k: v.to(device) for k, v in observations.items()}
-
-        obs_enc = self.encoder(observations, cache=observation_features)
-
-        inputs = torch.cat([obs_enc, actions], dim=-1)
-
-        # Loop through critics and collect outputs
-        q_values = []
-        for critic in self.critics:
-            q_values.append(critic(inputs))
-
-        # Stack outputs to match expected shape [num_critics, batch_size]
-        q_values = torch.stack([q.squeeze(-1) for q in q_values], dim=0)
-        return q_values
-
-
 class DiscreteCritic(nn.Module):
     def __init__(
         self,
@@ -800,7 +413,7 @@ class DiscreteCritic(nn.Module):
 class Policy(nn.Module):
     def __init__(
         self,
-        encoder: SACObservationEncoder,
+        encoder: GaussianActorObservationEncoder,
         network: nn.Module,
         action_dim: int,
         std_min: float = -5,
@@ -811,7 +424,7 @@ class Policy(nn.Module):
         encoder_is_shared: bool = False,
     ):
         super().__init__()
-        self.encoder: SACObservationEncoder = encoder
+        self.encoder: GaussianActorObservationEncoder = encoder
         self.network = network
         self.action_dim = action_dim
         self.std_min = std_min
@@ -885,7 +498,7 @@ class Policy(nn.Module):
 
 
 class DefaultImageEncoder(nn.Module):
-    def __init__(self, config: SACConfig):
+    def __init__(self, config: GaussianActorConfig):
         super().__init__()
         image_key = next(key for key in config.input_features if is_image_feature(key))
         self.image_enc_layers = nn.Sequential(
@@ -931,12 +544,12 @@ def freeze_image_encoder(image_encoder: nn.Module):
 
 
 class PretrainedImageEncoder(nn.Module):
-    def __init__(self, config: SACConfig):
+    def __init__(self, config: GaussianActorConfig):
         super().__init__()
 
         self.image_enc_layers, self.image_enc_out_shape = self._load_pretrained_vision_encoder(config)
 
-    def _load_pretrained_vision_encoder(self, config: SACConfig):
+    def _load_pretrained_vision_encoder(self, config: GaussianActorConfig):
         """Set up CNN encoder"""
         from transformers import AutoModel
 

src/lerobot/policies/gaussian_actor/processor_gaussian_actor.py

@@ -32,18 +32,18 @@ from lerobot.processor import (
 )
 from lerobot.utils.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME
 
-from .configuration_sac import SACConfig
+from .configuration_gaussian_actor import GaussianActorConfig
 
 
-def make_sac_pre_post_processors(
-    config: SACConfig,
+def make_gaussian_actor_pre_post_processors(
+    config: GaussianActorConfig,
     dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
 ) -> tuple[
     PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
     PolicyProcessorPipeline[PolicyAction, PolicyAction],
 ]:
     """
-    Constructs pre-processor and post-processor pipelines for the SAC policy.
+    Constructs pre-processor and post-processor pipelines for the Gaussian actor policy.
 
     The pre-processing pipeline prepares input data for the model by:
     1. Renaming features to match pretrained configurations.
@@ -56,7 +56,7 @@ def make_sac_pre_post_processors(
     2. Unnormalizing the output features to their original scale.
 
     Args:
-        config: The configuration object for the SAC policy.
+        config: The configuration object for the tanh-Gaussian policy.
         dataset_stats: A dictionary of statistics for normalization.
 
     Returns:

src/lerobot/processor/hil_processor.py

@@ -4,7 +4,6 @@
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
-# You may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
@@ -321,6 +320,7 @@ class GymHILAdapterProcessorStep(ProcessorStep):
     This step normalizes the `transition` object by:
     1. Copying `teleop_action` from `info` to `complementary_data`.
     2. Copying `is_intervention` from `info` (using the string key) to `info` (using the enum key).
+    3. Copying `discrete_penalty` from `info` to `complementary_data`.
     """
 
     def __call__(self, transition: EnvTransition) -> EnvTransition:
@@ -330,6 +330,9 @@ class GymHILAdapterProcessorStep(ProcessorStep):
         if TELEOP_ACTION_KEY in info:
             complementary_data[TELEOP_ACTION_KEY] = info[TELEOP_ACTION_KEY]
 
+        if DISCRETE_PENALTY_KEY in info:
+            complementary_data[DISCRETE_PENALTY_KEY] = info[DISCRETE_PENALTY_KEY]
+
         if "is_intervention" in info:
             info[TeleopEvents.IS_INTERVENTION] = info["is_intervention"]
 
@@ -348,18 +351,24 @@ class GymHILAdapterProcessorStep(ProcessorStep):
 @ProcessorStepRegistry.register("gripper_penalty_processor")
 class GripperPenaltyProcessorStep(ProcessorStep):
     """
-    Applies a penalty for inefficient gripper usage.
+    Applies a small per-transition cost on the discrete gripper action.
 
-    This step penalizes actions that attempt to close an already closed gripper or
-    open an already open one, based on position thresholds.
+    Fires only when the commanded action would actually transition the gripper
+    from one extreme to the other (close-while-open or open-while-closed).
+    This discourages gripper oscillation while leaving "stay" and saturating-further
+    commands unpenalized.
 
     Attributes:
         penalty: The negative reward value to apply.
         max_gripper_pos: The maximum position value for the gripper, used for normalization.
+        open_threshold: Normalized state below which the gripper is considered "open".
+        closed_threshold: Normalized state above which the gripper is considered "closed".
     """
 
-    penalty: float = -0.01
+    penalty: float = -0.02
     max_gripper_pos: float = 30.0
+    open_threshold: float = 0.1
+    closed_threshold: float = 0.9
 
     def __call__(self, transition: EnvTransition) -> EnvTransition:
         """
@@ -379,11 +388,15 @@ class GripperPenaltyProcessorStep(ProcessorStep):
         if raw_joint_positions is None:
             return new_transition
 
-        current_gripper_pos = raw_joint_positions.get(GRIPPER_KEY, None)
+        current_gripper_pos = raw_joint_positions.get(f"{GRIPPER_KEY}.pos", None)
         if current_gripper_pos is None:
             return new_transition
 
-        # Gripper action is a PolicyAction at this stage
+        # During reset, the transition may not carry any action yet.
+        if action is None:
+            return new_transition
+
+        # Gripper action is expected as the last action dimension.
         gripper_action = action[-1].item()
         gripper_action_normalized = gripper_action / self.max_gripper_pos
 
@@ -391,9 +404,13 @@ class GripperPenaltyProcessorStep(ProcessorStep):
         gripper_state_normalized = current_gripper_pos / self.max_gripper_pos
 
         # Calculate penalty boolean as in original
-        gripper_penalty_bool = (gripper_state_normalized < 0.5 and gripper_action_normalized > 0.5) or (
-            gripper_state_normalized > 0.75 and gripper_action_normalized < 0.5
-        )
+        #   - currently open  AND target is closed  -> close transition
+        #   - currently closed AND target is open   -> open transition
+        is_open = gripper_state_normalized < self.open_threshold
+        is_closed = gripper_state_normalized > self.closed_threshold
+        cmd_close = gripper_action_normalized > self.closed_threshold
+        cmd_open = gripper_action_normalized < self.open_threshold
+        gripper_penalty_bool = (is_open and cmd_close) or (is_closed and cmd_open)
 
         gripper_penalty = self.penalty * int(gripper_penalty_bool)
 
@@ -409,11 +426,14 @@ class GripperPenaltyProcessorStep(ProcessorStep):
         Returns the configuration of the step for serialization.
 
         Returns:
-            A dictionary containing the penalty value and max gripper position.
+            A dictionary containing the penalty value, max gripper position,
+            and the open/closed thresholds.
         """
         return {
             "penalty": self.penalty,
             "max_gripper_pos": self.max_gripper_pos,
+            "open_threshold": self.open_threshold,
+            "closed_threshold": self.closed_threshold,
         }
 
     def reset(self) -> None:

src/lerobot/processor/normalize_processor.py

@@ -134,6 +134,24 @@ class _NormalizationMixin:
         if self.dtype is None:
             self.dtype = torch.float32
         self._tensor_stats = to_tensor(self.stats, device=self.device, dtype=self.dtype)
+        self._reshape_visual_stats()
+
+    def _reshape_visual_stats(self) -> None:
+        """Reshape flat ``(C,)`` visual stats to ``(C, 1, 1)`` for image broadcasting.
+
+        No-op for stats from :func:`~lerobot.datasets.compute_stats.compute_stats`
+        (already ``(C, 1, 1)``). Needed by RL training, which can start without
+        a dataset and supplies stats manually via JSON config.
+        """
+        for key, feature in self.features.items():
+            if feature.type != FeatureType.VISUAL:
+                continue
+            if key not in self._tensor_stats:
+                continue
+            for stat_name, stat_tensor in self._tensor_stats[key].items():
+                if not isinstance(stat_tensor, Tensor) or stat_tensor.ndim != 1:
+                    continue
+                self._tensor_stats[key][stat_name] = stat_tensor.reshape(-1, 1, 1)
 
     def to(
         self, device: torch.device | str | None = None, dtype: torch.dtype | None = None
@@ -152,6 +170,7 @@ class _NormalizationMixin:
         if dtype is not None:
             self.dtype = dtype
         self._tensor_stats = to_tensor(self.stats, device=self.device, dtype=self.dtype)
+        self._reshape_visual_stats()
         return self
 
     def state_dict(self) -> dict[str, Tensor]:
@@ -201,6 +220,7 @@ class _NormalizationMixin:
             # Don't load from state_dict, keep the explicitly provided stats
             # But ensure _tensor_stats is properly initialized
             self._tensor_stats = to_tensor(self.stats, device=self.device, dtype=self.dtype)  # type: ignore[assignment]
+            self._reshape_visual_stats()
             return
 
         # Normal behavior: load stats from state_dict
@@ -211,6 +231,7 @@ class _NormalizationMixin:
             self._tensor_stats.setdefault(key, {})[stat_name] = tensor.to(
                 dtype=torch.float32, device=self.device
             )
+        self._reshape_visual_stats()
 
         # Reconstruct the original stats dict from tensor stats for compatibility with to() method
         # and other functions that rely on self.stats

src/lerobot/rewards/classifier/configuration_classifier.py

@@ -30,7 +30,7 @@ class RewardClassifierConfig(RewardModelConfig):
     latent_dim: int = 256
     image_embedding_pooling_dim: int = 8
     dropout_rate: float = 0.1
-    model_name: str = "helper2424/resnet10"  # TODO: This needs to be updated. The model on the Hub doesn't call self.post_init() in its __init__, which is required by transformers v5 to set all_tied_weights_keys. The from_pretrained call fails when it tries to access this attribute during _finalize_model_loading.
+    model_name: str = "lerobot/resnet10"
     device: str = "cpu"
     model_type: str = "cnn"  # "transformer" or "cnn"
     num_cameras: int = 2

src/lerobot/rewards/classifier/modeling_classifier.py

@@ -105,6 +105,7 @@ class Classifier(PreTrainedRewardModel):
     def __init__(
         self,
         config: RewardClassifierConfig,
+        **kwargs,
     ):
         from transformers import AutoModel
 

src/lerobot/rl/__init__.py

@@ -12,23 +12,33 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""
-Reinforcement learning modules.
+"""Reinforcement learning modules.
 
-Requires: ``pip install 'lerobot[hilserl]'``
-
-Available modules (import directly)::
-
-    from lerobot.rl.actor import ...
-    from lerobot.rl.learner import ...
-    from lerobot.rl.learner_service import ...
-    from lerobot.rl.buffer import ...
-    from lerobot.rl.eval_policy import ...
-    from lerobot.rl.gym_manipulator import ...
+Distributed actor / learner entry points (``actor``, ``learner``,
+``learner_service``) require ``pip install 'lerobot[hilserl]'``. Algorithms,
+buffer, data sources and trainer are gRPC-free and usable standalone.
 """
 
-from lerobot.utils.import_utils import require_package
+from .algorithms.base import RLAlgorithm as RLAlgorithm
+from .algorithms.configs import RLAlgorithmConfig as RLAlgorithmConfig, TrainingStats as TrainingStats
+from .algorithms.factory import (
+    make_algorithm as make_algorithm,
+    make_algorithm_config as make_algorithm_config,
+)
+from .algorithms.sac.configuration_sac import SACAlgorithmConfig as SACAlgorithmConfig
+from .buffer import ReplayBuffer as ReplayBuffer
+from .data_sources import DataMixer as DataMixer, OnlineOfflineMixer as OnlineOfflineMixer
+from .trainer import RLTrainer as RLTrainer
 
-require_package("grpcio", extra="hilserl", import_name="grpc")
-
-__all__: list[str] = []
+__all__ = [
+    "RLAlgorithm",
+    "RLAlgorithmConfig",
+    "TrainingStats",
+    "make_algorithm",
+    "make_algorithm_config",
+    "SACAlgorithmConfig",
+    "RLTrainer",
+    "ReplayBuffer",
+    "DataMixer",
+    "OnlineOfflineMixer",
+]

src/lerobot/rl/actor.py

@@ -46,42 +46,33 @@ For more details on the complete HILSerl training workflow, see:
 https://github.com/michel-aractingi/lerobot-hilserl-guide
 """
 
+from __future__ import annotations
+
 import logging
 import os
 import time
 from functools import lru_cache
 from queue import Empty
+from typing import TYPE_CHECKING, Any
 
-import grpc
 import torch
 from torch import nn
-from torch.multiprocessing import Event, Queue
+from torch.multiprocessing import Queue
 
 from lerobot.cameras import opencv  # noqa: F401
 from lerobot.configs import parser
-from lerobot.configs.train import TrainRLServerPipelineConfig
-from lerobot.policies import make_policy
-from lerobot.policies.sac.modeling_sac import SACPolicy
+from lerobot.policies import PreTrainedPolicy, make_policy, make_pre_post_processors
+from lerobot.processor import TransitionKey
 from lerobot.robots import so_follower  # noqa: F401
 from lerobot.teleoperators import gamepad, so_leader  # noqa: F401
 from lerobot.teleoperators.utils import TeleopEvents
-from lerobot.transport import services_pb2, services_pb2_grpc
-from lerobot.transport.utils import (
-    bytes_to_state_dict,
-    grpc_channel_options,
-    python_object_to_bytes,
-    receive_bytes_in_chunks,
-    send_bytes_in_chunks,
-    transitions_to_bytes,
-)
-from lerobot.types import TransitionKey
 from lerobot.utils.device_utils import get_safe_torch_device
+from lerobot.utils.import_utils import _grpc_available, require_package
 from lerobot.utils.process import ProcessSignalHandler
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.transition import (
     Transition,
-    move_state_dict_to_device,
     move_transition_to_device,
 )
 from lerobot.utils.utils import (
@@ -89,19 +80,44 @@ from lerobot.utils.utils import (
     init_logging,
 )
 
+if TYPE_CHECKING or _grpc_available:
+    import grpc
+
+    from lerobot.transport import services_pb2, services_pb2_grpc
+    from lerobot.transport.utils import (
+        bytes_to_state_dict,
+        grpc_channel_options,
+        python_object_to_bytes,
+        receive_bytes_in_chunks,
+        send_bytes_in_chunks,
+        transitions_to_bytes,
+    )
+else:
+    grpc = None
+    services_pb2 = None
+    services_pb2_grpc = None
+    bytes_to_state_dict = None
+    grpc_channel_options = None
+    python_object_to_bytes = None
+    receive_bytes_in_chunks = None
+    send_bytes_in_chunks = None
+    transitions_to_bytes = None
+
 from .gym_manipulator import (
-    create_transition,
     make_processors,
     make_robot_env,
+    reset_and_build_transition,
     step_env_and_process_transition,
 )
 from .queue import get_last_item_from_queue
+from .train_rl import TrainRLServerPipelineConfig
 
 # Main entry point
 
 
 @parser.wrap()
 def actor_cli(cfg: TrainRLServerPipelineConfig):
+    require_package("grpcio", extra="hilserl", import_name="grpc")
     cfg.validate()
     display_pid = False
     if not use_threads(cfg):
@@ -212,7 +228,7 @@ def actor_cli(cfg: TrainRLServerPipelineConfig):
 
 def act_with_policy(
     cfg: TrainRLServerPipelineConfig,
-    shutdown_event: any,  # Event,
+    shutdown_event: Any,  # Event
     parameters_queue: Queue,
     transitions_queue: Queue,
     interactions_queue: Queue,
@@ -252,22 +268,21 @@ def act_with_policy(
     logging.info("make_policy")
 
     ### Instantiate the policy in both the actor and learner processes
-    ### To avoid sending a SACPolicy object through the port, we create a policy instance
+    ### To avoid sending a policy object through the port, we create a policy instance
     ### on both sides, the learner sends the updated parameters every n steps to update the actor's parameters
-    policy: SACPolicy = make_policy(
+    policy = make_policy(
         cfg=cfg.policy,
         env_cfg=cfg.env,
     )
-    policy = policy.eval()
+    policy = policy.to(device).eval()
     assert isinstance(policy, nn.Module)
 
-    obs, info = online_env.reset()
-    env_processor.reset()
-    action_processor.reset()
+    preprocessor, postprocessor = make_pre_post_processors(
+        policy_cfg=cfg.policy,
+        dataset_stats=cfg.policy.dataset_stats,
+    )
 
-    # Process initial observation
-    transition = create_transition(observation=obs, info=info)
-    transition = env_processor(transition)
+    transition = reset_and_build_transition(online_env, env_processor, action_processor)
 
     # NOTE: For the moment we will solely handle the case of a single environment
     sum_reward_episode = 0
@@ -291,8 +306,17 @@ def act_with_policy(
 
         # Time policy inference and check if it meets FPS requirement
         with policy_timer:
-            # Extract observation from transition for policy
-            action = policy.select_action(batch=observation)
+            normalized_observation = preprocessor.process_observation(observation)
+            action = policy.select_action(batch=normalized_observation)
+            # Unnormalize only the continuous part.
+            if cfg.policy.num_discrete_actions is not None:
+                continuous_action = postprocessor.process_action(action[..., :-1])
+                discrete_action = action[..., -1:].to(
+                    device=continuous_action.device, dtype=continuous_action.dtype
+                )
+                action = torch.cat([continuous_action, discrete_action], dim=-1)
+            else:
+                action = postprocessor.process_action(action)
         policy_fps = policy_timer.fps_last
 
         log_policy_frequency_issue(policy_fps=policy_fps, cfg=cfg, interaction_step=interaction_step)
@@ -326,7 +350,8 @@ def act_with_policy(
 
         # Check for intervention from transition info
         intervention_info = new_transition[TransitionKey.INFO]
-        if intervention_info.get(TeleopEvents.IS_INTERVENTION, False):
+        is_intervention = bool(intervention_info.get(TeleopEvents.IS_INTERVENTION, False))
+        if is_intervention:
             episode_intervention = True
             episode_intervention_steps += 1
 
@@ -334,6 +359,7 @@ def act_with_policy(
             "discrete_penalty": torch.tensor(
                 [new_transition[TransitionKey.COMPLEMENTARY_DATA].get("discrete_penalty", 0.0)]
             ),
+            TeleopEvents.IS_INTERVENTION.value: is_intervention,
         }
         # Create transition for learner (convert to old format)
         list_transition_to_send_to_learner.append(
@@ -390,14 +416,7 @@ def act_with_policy(
             episode_intervention_steps = 0
             episode_total_steps = 0
 
-            # Reset environment and processors
-            obs, info = online_env.reset()
-            env_processor.reset()
-            action_processor.reset()
-
-            # Process initial observation
-            transition = create_transition(observation=obs, info=info)
-            transition = env_processor(transition)
+            transition = reset_and_build_transition(online_env, env_processor, action_processor)
 
         if cfg.env.fps is not None:
             dt_time = time.perf_counter() - start_time
@@ -409,7 +428,7 @@ def act_with_policy(
 
 def establish_learner_connection(
     stub: services_pb2_grpc.LearnerServiceStub,
-    shutdown_event: Event,  # type: ignore
+    shutdown_event: Any,  # Event
     attempts: int = 30,
 ):
     """Establish a connection with the learner.
@@ -461,7 +480,7 @@ def learner_service_client(
 def receive_policy(
     cfg: TrainRLServerPipelineConfig,
     parameters_queue: Queue,
-    shutdown_event: Event,  # type: ignore
+    shutdown_event: Any,  # Event
     learner_client: services_pb2_grpc.LearnerServiceStub | None = None,
     grpc_channel: grpc.Channel | None = None,
 ):
@@ -513,7 +532,7 @@ def receive_policy(
 def send_transitions(
     cfg: TrainRLServerPipelineConfig,
     transitions_queue: Queue,
-    shutdown_event: any,  # Event,
+    shutdown_event: Any,  # Event
     learner_client: services_pb2_grpc.LearnerServiceStub | None = None,
     grpc_channel: grpc.Channel | None = None,
 ) -> services_pb2.Empty:
@@ -563,7 +582,7 @@ def send_transitions(
 def send_interactions(
     cfg: TrainRLServerPipelineConfig,
     interactions_queue: Queue,
-    shutdown_event: Event,  # type: ignore
+    shutdown_event: Any,  # Event
     learner_client: services_pb2_grpc.LearnerServiceStub | None = None,
     grpc_channel: grpc.Channel | None = None,
 ) -> services_pb2.Empty:
@@ -613,7 +632,11 @@ def send_interactions(
     logging.info("[ACTOR] Interactions process stopped")
 
 
-def transitions_stream(shutdown_event: Event, transitions_queue: Queue, timeout: float) -> services_pb2.Empty:  # type: ignore
+def transitions_stream(
+    shutdown_event: Any,  # Event
+    transitions_queue: Queue,
+    timeout: float,
+) -> services_pb2.Empty:
     while not shutdown_event.is_set():
         try:
             message = transitions_queue.get(block=True, timeout=timeout)
@@ -629,9 +652,9 @@ def transitions_stream(shutdown_event: Event, transitions_queue: Queue, timeout:
 
 
 def interactions_stream(
-    shutdown_event: Event,
+    shutdown_event: Any,  # Event
     interactions_queue: Queue,
-    timeout: float,  # type: ignore
+    timeout: float,
 ) -> services_pb2.Empty:
     while not shutdown_event.is_set():
         try:
@@ -652,7 +675,7 @@ def interactions_stream(
 #  Policy functions
 
 
-def update_policy_parameters(policy: SACPolicy, parameters_queue: Queue, device):
+def update_policy_parameters(policy: PreTrainedPolicy, parameters_queue: Queue, device):
     bytes_state_dict = get_last_item_from_queue(parameters_queue, block=False)
     if bytes_state_dict is not None:
         logging.info("[ACTOR] Load new parameters from Learner.")
@@ -667,18 +690,7 @@ def update_policy_parameters(policy: SACPolicy, parameters_queue: Queue, device)
         # - Send critic's encoder state when shared_encoder=True
         # - Skip encoder params entirely when freeze_vision_encoder=True
         # - Ensure discrete_critic gets correct encoder state (currently uses encoder_critic)
-
-        # Load actor state dict
-        actor_state_dict = move_state_dict_to_device(state_dicts["policy"], device=device)
-        policy.actor.load_state_dict(actor_state_dict)
-
-        # Load discrete critic if present
-        if hasattr(policy, "discrete_critic") and "discrete_critic" in state_dicts:
-            discrete_critic_state_dict = move_state_dict_to_device(
-                state_dicts["discrete_critic"], device=device
-            )
-            policy.discrete_critic.load_state_dict(discrete_critic_state_dict)
-            logging.info("[ACTOR] Loaded discrete critic parameters from Learner.")
+        policy.load_actor_weights(state_dicts, device=device)
 
 
 #  Utilities functions

src/lerobot/rl/algorithms/__init__.py

@@ -0,0 +1,20 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .sac import SACAlgorithm, SACAlgorithmConfig
+
+__all__ = [
+    "SACAlgorithm",
+    "SACAlgorithmConfig",
+]

src/lerobot/rl/algorithms/base.py

@@ -0,0 +1,100 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import abc
+from collections.abc import Iterator
+from typing import TYPE_CHECKING, Any
+
+import torch
+from torch.optim import Optimizer
+
+from lerobot.types import BatchType
+
+from .configs import RLAlgorithmConfig, TrainingStats
+
+if TYPE_CHECKING:
+    from ..data_sources.data_mixer import DataMixer
+
+
+class RLAlgorithm(abc.ABC):
+    """Base for all RL algorithms."""
+
+    config_class: type[RLAlgorithmConfig]
+    name: str
+
+    @abc.abstractmethod
+    def update(self, batch_iterator: Iterator[BatchType]) -> TrainingStats:
+        """One complete training step.
+
+        The algorithm calls ``next(batch_iterator)`` as many times as it
+        needs (e.g. ``utd_ratio`` times for SAC) to obtain fresh batches.
+        The iterator is owned by the trainer; the algorithm just consumes
+        from it.
+        """
+        raise NotImplementedError
+
+    def configure_data_iterator(
+        self,
+        data_mixer: DataMixer,
+        batch_size: int,
+        *,
+        async_prefetch: bool = True,
+        queue_size: int = 2,
+    ) -> Iterator[BatchType]:
+        """Create the data iterator this algorithm needs.
+
+        The default implementation uses the standard ``data_mixer.get_iterator()``.
+        Algorithms that need specialised sampling should override this method.
+        """
+        return data_mixer.get_iterator(
+            batch_size=batch_size,
+            async_prefetch=async_prefetch,
+            queue_size=queue_size,
+        )
+
+    @abc.abstractmethod
+    def make_optimizers_and_scheduler(self) -> dict[str, Optimizer]:
+        """Build and return the optimizers used during training.
+
+        Called once on the learner side after construction.
+        """
+        raise NotImplementedError
+
+    def get_optimizers(self) -> dict[str, Optimizer]:
+        """Return optimizers for checkpointing / external scheduling."""
+        return {}
+
+    @property
+    def optimization_step(self) -> int:
+        """Current learner optimization step.
+
+        Part of the stable contract for checkpoint/resume. Algorithms can
+        either use this default storage or override for custom behavior.
+        """
+        return getattr(self, "_optimization_step", 0)
+
+    @optimization_step.setter
+    def optimization_step(self, value: int) -> None:
+        self._optimization_step = int(value)
+
+    def get_weights(self) -> dict[str, Any]:
+        """Policy state-dict to push to actors."""
+        return {}
+
+    @abc.abstractmethod
+    def load_weights(self, weights: dict[str, Any], device: str | torch.device = "cpu") -> None:
+        """Load policy state-dict received from the learner."""
+        raise NotImplementedError

src/lerobot/rl/algorithms/configs.py

@@ -0,0 +1,64 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import abc
+from dataclasses import dataclass, field
+from typing import Any
+
+import draccus
+
+
+@dataclass
+class TrainingStats:
+    """Returned by ``algorithm.update()`` for logging and checkpointing."""
+
+    losses: dict[str, float] = field(default_factory=dict)
+    grad_norms: dict[str, float] = field(default_factory=dict)
+    extra: dict[str, float] = field(default_factory=dict)
+
+    def to_log_dict(self) -> dict[str, float]:
+        """Flatten all stats into a single dict for logging."""
+
+        d: dict[str, float] = {}
+        for name, val in self.losses.items():
+            d[name] = val
+        for name, val in self.grad_norms.items():
+            d[f"{name}_grad_norm"] = val
+        for name, val in self.extra.items():
+            d[name] = val
+        return d
+
+
+@dataclass
+class RLAlgorithmConfig(draccus.ChoiceRegistry, abc.ABC):
+    """Registry for algorithm configs."""
+
+    @property
+    def type(self) -> str:
+        """Registered name of this algorithm config (e.g. ``"sac"``)."""
+        choice_name = self.get_choice_name(self.__class__)
+        if not isinstance(choice_name, str):
+            raise TypeError(f"Expected string from get_choice_name, got {type(choice_name)}")
+        return choice_name
+
+    @classmethod
+    @abc.abstractmethod
+    def from_policy_config(cls, policy_cfg: Any) -> RLAlgorithmConfig:
+        """Build an algorithm config from a policy config.
+
+        Must be overridden by every registered config subclass.
+        """
+        raise NotImplementedError(f"{cls.__name__} must implement from_policy_config()")

src/lerobot/rl/algorithms/factory.py

@@ -0,0 +1,99 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import torch
+
+from .base import RLAlgorithm
+from .configs import RLAlgorithmConfig
+
+
+def make_algorithm_config(algorithm_type: str, **kwargs) -> RLAlgorithmConfig:
+    """Instantiate an `RLAlgorithmConfig` from its registered type name.
+
+    Args:
+        algorithm_type: Registry key of the algorithm (e.g. ``"sac"``).
+        **kwargs: Keyword arguments forwarded to the config class constructor.
+
+    Returns:
+        An instance of the matching ``RLAlgorithmConfig`` subclass.
+
+    Raises:
+        ValueError: If ``algorithm_type`` is not registered.
+    """
+    try:
+        cls = RLAlgorithmConfig.get_choice_class(algorithm_type)
+    except KeyError as err:
+        raise ValueError(
+            f"Algorithm type '{algorithm_type}' is not registered. "
+            f"Available: {list(RLAlgorithmConfig.get_known_choices().keys())}"
+        ) from err
+    return cls(**kwargs)
+
+
+def get_algorithm_class(name: str) -> type[RLAlgorithm]:
+    """
+    Retrieves an RL algorithm class by its registered name.
+
+    This function uses dynamic imports to avoid loading all algorithm classes into
+    memory at once, improving startup time and reducing dependencies.
+
+    Args:
+        name: The name of the algorithm. Supported names are "sac".
+
+    Returns:
+        The algorithm class corresponding to the given name.
+
+    Raises:
+        ValueError: If the algorithm name is not recognized.
+    """
+    if name == "sac":
+        from .sac.sac_algorithm import SACAlgorithm
+
+        return SACAlgorithm
+    raise ValueError(
+        f"Algorithm type '{name}' is not available. "
+        f"Known: {list(RLAlgorithmConfig.get_known_choices().keys())}"
+    )
+
+
+def make_algorithm(cfg: RLAlgorithmConfig, policy: torch.nn.Module) -> RLAlgorithm:
+    """
+    Instantiate an RL algorithm.
+
+    This factory function looks up the :class:`RLAlgorithm` subclass that matches
+    ``cfg.type`` and instantiates it with the provided policy. It also enforces
+    that ``cfg.policy_config`` has been populated before construction (this is
+    normally handled by :meth:`TrainRLServerPipelineConfig.validate`).
+
+    Args:
+        cfg: The algorithm configuration. Must have ``policy_config`` set.
+        policy: The policy module the algorithm will train.
+
+    Returns:
+        An instantiated :class:`RLAlgorithm`.
+
+    Raises:
+        ValueError: If ``cfg.policy_config`` is ``None`` or ``cfg.type`` is not
+            registered.
+    """
+    if getattr(cfg, "policy_config", None) is None:
+        raise ValueError(
+            f"{type(cfg).__name__}.policy_config is None. "
+            "It must be populated (typically by TrainRLServerPipelineConfig.validate) "
+            "before calling make_algorithm()."
+        )
+    cls = get_algorithm_class(cfg.type)
+    return cls(policy=policy, config=cfg)

src/lerobot/rl/algorithms/sac/__init__.py

@@ -0,0 +1,18 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .configuration_sac import SACAlgorithmConfig
+from .sac_algorithm import SACAlgorithm
+
+__all__ = ["SACAlgorithm", "SACAlgorithmConfig"]

src/lerobot/rl/algorithms/sac/configuration_sac.py

@@ -0,0 +1,98 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+from lerobot.policies.gaussian_actor.configuration_gaussian_actor import (
+    CriticNetworkConfig,
+    GaussianActorConfig,
+)
+
+from ..configs import RLAlgorithmConfig
+
+
+@RLAlgorithmConfig.register_subclass("sac")
+@dataclass
+class SACAlgorithmConfig(RLAlgorithmConfig):
+    """Soft Actor-Critic (SAC) algorithm configuration.
+
+    SAC is an off-policy actor-critic deep RL algorithm based on the maximum
+    entropy reinforcement learning framework. It learns a policy and a Q-function
+    simultaneously using experience collected from the environment.
+
+    This configuration class contains the algorithm-side hyperparameters: critic
+    ensemble, target networks, temperature / entropy tuning, and the Bellman
+    update loop. The policy-side (actor + observation encoder) lives in
+    :class:`~lerobot.policies.gaussian_actor.GaussianActorConfig` and is
+    referenced via :attr:`policy_config`.
+    """
+
+    # Optimizer learning rates
+    # Learning rate for the actor network
+    actor_lr: float = 3e-4
+    # Learning rate for the critic network
+    critic_lr: float = 3e-4
+    # Learning rate for the temperature parameter
+    temperature_lr: float = 3e-4
+
+    # Bellman update
+    # Discount factor for the SAC algorithm
+    discount: float = 0.99
+    # Whether to use backup entropy for the SAC algorithm
+    use_backup_entropy: bool = True
+    # Weight for the critic target update
+    critic_target_update_weight: float = 0.005
+
+    # Critic ensemble
+    # Number of critics in the ensemble
+    num_critics: int = 2
+    # Number of subsampled critics for training
+    num_subsample_critics: int | None = None
+    # Configuration for the critic network architecture
+    critic_network_kwargs: CriticNetworkConfig = field(default_factory=CriticNetworkConfig)
+    # Configuration for the discrete critic network
+    discrete_critic_network_kwargs: CriticNetworkConfig = field(default_factory=CriticNetworkConfig)
+
+    # Temperature / entropy
+    # Initial temperature value
+    temperature_init: float = 1.0
+    # Target entropy for automatic temperature tuning. If ``None``, defaults to
+    # ``-|A|/2`` where ``|A|`` is the total action dimension (continuous + 1 if
+    # there is a discrete action head).
+    target_entropy: float | None = None
+
+    # Update loop
+    # Update-to-data ratio. Set to >1 to enable extra critic updates per env step.
+    utd_ratio: int = 1
+    # Frequency of policy updates
+    policy_update_freq: int = 1
+    # Gradient clipping norm for the SAC algorithm
+    grad_clip_norm: float = 40.0
+
+    # Optimizations
+    # torch.compile is currently disabled by default
+    use_torch_compile: bool = False
+
+    # Policy config
+    policy_config: GaussianActorConfig | None = None
+
+    @classmethod
+    def from_policy_config(cls, policy_cfg: GaussianActorConfig) -> SACAlgorithmConfig:
+        """Build an algorithm config with default hyperparameters for a given policy."""
+        return cls(
+            policy_config=policy_cfg,
+            discrete_critic_network_kwargs=policy_cfg.discrete_critic_network_kwargs,
+        )

src/lerobot/rl/algorithms/sac/sac_algorithm.py

@@ -0,0 +1,618 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import math
+from collections.abc import Callable, Iterator
+from dataclasses import asdict
+from typing import Any
+
+import einops
+import torch
+import torch.nn as nn
+import torch.nn.functional as F  # noqa: N812
+from torch import Tensor
+from torch.optim import Optimizer
+
+from lerobot.policies.gaussian_actor.modeling_gaussian_actor import (
+    DISCRETE_DIMENSION_INDEX,
+    MLP,
+    DiscreteCritic,
+    GaussianActorObservationEncoder,
+    GaussianActorPolicy,
+    orthogonal_init,
+)
+from lerobot.policies.utils import get_device_from_parameters
+from lerobot.types import BatchType
+from lerobot.utils.constants import ACTION
+from lerobot.utils.transition import move_state_dict_to_device
+
+from ..base import RLAlgorithm
+from ..configs import TrainingStats
+from .configuration_sac import SACAlgorithmConfig
+
+
+class SACAlgorithm(RLAlgorithm):
+    """Soft Actor-Critic. Owns critics, targets, temperature, and loss computation."""
+
+    config_class = SACAlgorithmConfig
+    name = "sac"
+
+    def __init__(
+        self,
+        policy: GaussianActorPolicy,
+        config: SACAlgorithmConfig,
+    ):
+        self.config = config
+        self.policy_config = config.policy_config
+        self.policy = policy
+        self.optimizers: dict[str, Optimizer] = {}
+        self._optimization_step: int = 0
+
+        action_dim = self.policy.config.output_features[ACTION].shape[0]
+        self._init_critics(action_dim)
+        self._init_temperature(action_dim)
+
+        self._device = torch.device(self.policy.config.device)
+        self._move_to_device()
+
+    def _init_critics(self, action_dim) -> None:
+        """Build critic ensemble, targets."""
+        encoder = self.policy.encoder_critic
+
+        heads = [
+            CriticHead(
+                input_dim=encoder.output_dim + action_dim,
+                **asdict(self.config.critic_network_kwargs),
+            )
+            for _ in range(self.config.num_critics)
+        ]
+        self.critic_ensemble = CriticEnsemble(encoder=encoder, ensemble=heads)
+        target_heads = [
+            CriticHead(
+                input_dim=encoder.output_dim + action_dim,
+                **asdict(self.config.critic_network_kwargs),
+            )
+            for _ in range(self.config.num_critics)
+        ]
+        self.critic_target = CriticEnsemble(encoder=encoder, ensemble=target_heads)
+        self.critic_target.load_state_dict(self.critic_ensemble.state_dict())
+
+        # TODO(Khalil): Investigate and fix torch.compile
+        # NOTE: torch.compile is disabled, policy does not converge when enabled.
+        if self.config.use_torch_compile:
+            self.critic_ensemble = torch.compile(self.critic_ensemble)
+            self.critic_target = torch.compile(self.critic_target)
+
+        self.discrete_critic_target = None
+        if self.policy_config.num_discrete_actions is not None:
+            self.discrete_critic_target = self._init_discrete_critic_target(encoder)
+
+    def _init_discrete_critic_target(self, encoder: GaussianActorObservationEncoder) -> DiscreteCritic:
+        """Build target discrete critic (main network is owned by the policy)."""
+        discrete_critic_target = DiscreteCritic(
+            encoder=encoder,
+            input_dim=encoder.output_dim,
+            output_dim=self.policy_config.num_discrete_actions,
+            **asdict(self.config.discrete_critic_network_kwargs),
+        )
+        # TODO(Khalil): Compile the discrete critic
+        discrete_critic_target.load_state_dict(self.policy.discrete_critic.state_dict())
+        return discrete_critic_target
+
+    def _init_temperature(self, continuous_action_dim: int) -> None:
+        """Set up temperature parameter (log_alpha) and target entropy."""
+        temp_init = self.config.temperature_init
+        self.log_alpha = nn.Parameter(torch.tensor([math.log(temp_init)]))
+
+        self.target_entropy = self.config.target_entropy
+        if self.target_entropy is None:
+            total_action_dim = continuous_action_dim + (
+                1 if self.policy_config.num_discrete_actions is not None else 0
+            )
+            self.target_entropy = -total_action_dim / 2
+
+    def _move_to_device(self) -> None:
+        self.policy.to(self._device)
+        self.critic_ensemble.to(self._device)
+        self.critic_target.to(self._device)
+        self.log_alpha = nn.Parameter(self.log_alpha.data.to(self._device))
+        if self.discrete_critic_target is not None:
+            self.discrete_critic_target.to(self._device)
+
+    @property
+    def temperature(self) -> float:
+        """Return the current temperature value, always in sync with log_alpha."""
+        return self.log_alpha.exp().item()
+
+    def _critic_forward(
+        self,
+        observations: dict[str, Tensor],
+        actions: Tensor,
+        use_target: bool = False,
+        observation_features: Tensor | None = None,
+    ) -> Tensor:
+        """Forward pass through a critic network ensemble
+
+        Args:
+            observations: Dictionary of observations
+            actions: Action tensor
+            use_target: If True, use target critics, otherwise use ensemble critics
+
+        Returns:
+            Tensor of Q-values from all critics
+        """
+
+        critics = self.critic_target if use_target else self.critic_ensemble
+        q_values = critics(observations, actions, observation_features)
+        return q_values
+
+    def _discrete_critic_forward(
+        self, observations, use_target=False, observation_features=None
+    ) -> torch.Tensor:
+        """Forward pass through a discrete critic network
+
+        Args:
+            observations: Dictionary of observations
+            use_target: If True, use target critics, otherwise use ensemble critics
+            observation_features: Optional pre-computed observation features to avoid recomputing encoder output
+
+        Returns:
+            Tensor of Q-values from the discrete critic network
+        """
+        discrete_critic = self.discrete_critic_target if use_target else self.policy.discrete_critic
+        q_values = discrete_critic(observations, observation_features)
+        return q_values
+
+    def update(self, batch_iterator: Iterator[BatchType]) -> TrainingStats:
+        """Run one SAC training step (critic / discrete-critic / actor / temperature).
+
+        Pulls ``utd_ratio`` batches from ``batch_iterator``, computes the relevant
+        losses, backpropagates each, and updates target networks.
+
+        Args:
+            batch_iterator: yields batches each containing
+                - ``action``: Action tensor
+                - ``reward``: Reward tensor
+                - ``state``: Observations tensor dict
+                - ``next_state``: Next observations tensor dict
+                - ``done``: Done mask tensor
+                - ``observation_feature``: Optional pre-computed observation features
+                - ``next_observation_feature``: Optional pre-computed next observation features
+                - ``complementary_info`` (optional): per-step extras like discrete penalties
+
+        Returns:
+            TrainingStats with per-component losses and grad norms.
+        """
+        clip = self.config.grad_clip_norm
+
+        for _ in range(self.config.utd_ratio - 1):
+            batch = next(batch_iterator)
+            fb = self._prepare_forward_batch(batch, include_complementary_info=True)
+
+            loss_critic = self._compute_loss_critic(fb)
+            self.optimizers["critic"].zero_grad()
+            loss_critic.backward()
+            torch.nn.utils.clip_grad_norm_(self.critic_ensemble.parameters(), max_norm=clip)
+            self.optimizers["critic"].step()
+
+            if self.policy_config.num_discrete_actions is not None:
+                loss_dc = self._compute_loss_discrete_critic(fb)
+                self.optimizers["discrete_critic"].zero_grad()
+                loss_dc.backward()
+                torch.nn.utils.clip_grad_norm_(self.policy.discrete_critic.parameters(), max_norm=clip)
+                self.optimizers["discrete_critic"].step()
+
+            self._update_target_networks()
+
+        batch = next(batch_iterator)
+        fb = self._prepare_forward_batch(batch, include_complementary_info=False)
+
+        loss_critic = self._compute_loss_critic(fb)
+        self.optimizers["critic"].zero_grad()
+        loss_critic.backward()
+        critic_grad = torch.nn.utils.clip_grad_norm_(self.critic_ensemble.parameters(), max_norm=clip).item()
+        self.optimizers["critic"].step()
+
+        stats = TrainingStats(
+            losses={"loss_critic": loss_critic.item()},
+            grad_norms={"critic": critic_grad},
+        )
+
+        if self.policy_config.num_discrete_actions is not None:
+            loss_dc = self._compute_loss_discrete_critic(fb)
+            self.optimizers["discrete_critic"].zero_grad()
+            loss_dc.backward()
+            dc_grad = torch.nn.utils.clip_grad_norm_(
+                self.policy.discrete_critic.parameters(), max_norm=clip
+            ).item()
+            self.optimizers["discrete_critic"].step()
+            stats.losses["loss_discrete_critic"] = loss_dc.item()
+            stats.grad_norms["discrete_critic"] = dc_grad
+
+        if self._optimization_step % self.config.policy_update_freq == 0:
+            for _ in range(self.config.policy_update_freq):
+                loss_actor = self._compute_loss_actor(fb)
+                self.optimizers["actor"].zero_grad()
+                loss_actor.backward()
+                actor_grad = torch.nn.utils.clip_grad_norm_(
+                    self.policy.actor.parameters(), max_norm=clip
+                ).item()
+                self.optimizers["actor"].step()
+
+                loss_temp = self._compute_loss_temperature(fb)
+                self.optimizers["temperature"].zero_grad()
+                loss_temp.backward()
+                temp_grad = torch.nn.utils.clip_grad_norm_([self.log_alpha], max_norm=clip).item()
+                self.optimizers["temperature"].step()
+
+            stats.losses["loss_actor"] = loss_actor.item()
+            stats.losses["loss_temperature"] = loss_temp.item()
+            stats.grad_norms["actor"] = actor_grad
+            stats.grad_norms["temperature"] = temp_grad
+            stats.extra["temperature"] = self.temperature
+
+        self._update_target_networks()
+        self._optimization_step += 1
+        return stats
+
+    def _compute_loss_critic(self, batch: dict[str, Any]) -> Tensor:
+        # Extract common components from batch
+        observations = batch["state"]
+        actions = batch[ACTION]
+        observation_features = batch.get("observation_feature")
+        # Extract critic-specific components
+        rewards = batch["reward"]
+        next_observations = batch["next_state"]
+        done = batch["done"]
+        next_observation_features = batch.get("next_observation_feature")
+
+        with torch.no_grad():
+            next_action_preds, next_log_probs, _ = self.policy.actor(
+                next_observations, next_observation_features
+            )
+
+            # 2- compute q targets
+            q_targets = self._critic_forward(
+                observations=next_observations,
+                actions=next_action_preds,
+                use_target=True,
+                observation_features=next_observation_features,
+            )
+
+            # subsample critics to prevent overfitting if use high UTD (update to date)
+            # TODO: Get indices before forward pass to avoid unnecessary computation
+            if self.config.num_subsample_critics is not None:
+                indices = torch.randperm(self.config.num_critics)
+                indices = indices[: self.config.num_subsample_critics]
+                q_targets = q_targets[indices]
+
+            # critics subsample size
+            min_q, _ = q_targets.min(dim=0)  # Get values from min operation
+            if self.config.use_backup_entropy:
+                min_q = min_q - (self.temperature * next_log_probs)
+
+            td_target = rewards + (1 - done) * self.config.discount * min_q
+
+        # 3- compute predicted qs
+        if self.policy_config.num_discrete_actions is not None:
+            # NOTE: We only want to keep the continuous action part
+            # In the buffer we have the full action space (continuous + discrete)
+            # We need to split them before concatenating them in the critic forward
+            actions: Tensor = actions[:, :DISCRETE_DIMENSION_INDEX]
+        q_preds = self._critic_forward(
+            observations=observations,
+            actions=actions,
+            use_target=False,
+            observation_features=observation_features,
+        )
+
+        # 4- Calculate loss
+        # Compute state-action value loss (TD loss) for all of the Q functions in the ensemble.
+        td_target_duplicate = einops.repeat(td_target, "b -> e b", e=q_preds.shape[0])
+        # You compute the mean loss of the batch for each critic and then to compute the final loss you sum them up
+        critics_loss = (
+            F.mse_loss(
+                input=q_preds,
+                target=td_target_duplicate,
+                reduction="none",
+            ).mean(dim=1)
+        ).sum()
+        return critics_loss
+
+    def _compute_loss_discrete_critic(self, batch: dict[str, Any]) -> Tensor:
+        observations = batch["state"]
+        actions = batch[ACTION]
+        rewards = batch["reward"]
+        next_observations = batch["next_state"]
+        done = batch["done"]
+        observation_features = batch.get("observation_feature")
+        next_observation_features = batch.get("next_observation_feature")
+        complementary_info = batch.get("complementary_info")
+
+        # NOTE: We only want to keep the discrete action part
+        # In the buffer we have the full action space (continuous + discrete)
+        # We need to split them before concatenating them in the critic forward
+        actions_discrete: Tensor = actions[:, DISCRETE_DIMENSION_INDEX:].clone()
+        actions_discrete = torch.round(actions_discrete)
+        actions_discrete = actions_discrete.long()
+
+        discrete_penalties: Tensor | None = None
+        if complementary_info is not None:
+            discrete_penalties = complementary_info.get("discrete_penalty")
+
+        with torch.no_grad():
+            # For DQN, select actions using online network, evaluate with target network
+            next_discrete_qs = self._discrete_critic_forward(
+                next_observations, use_target=False, observation_features=next_observation_features
+            )
+            best_next_discrete_action = torch.argmax(next_discrete_qs, dim=-1, keepdim=True)
+
+            # Get target Q-values from target network
+            target_next_discrete_qs = self._discrete_critic_forward(
+                observations=next_observations,
+                use_target=True,
+                observation_features=next_observation_features,
+            )
+
+            # Use gather to select Q-values for best actions
+            target_next_discrete_q = torch.gather(
+                target_next_discrete_qs, dim=1, index=best_next_discrete_action
+            ).squeeze(-1)
+
+            # Compute target Q-value with Bellman equation
+            rewards_discrete = rewards
+            if discrete_penalties is not None:
+                rewards_discrete = rewards + discrete_penalties
+            target_discrete_q = rewards_discrete + (1 - done) * self.config.discount * target_next_discrete_q
+
+        # Get predicted Q-values for current observations
+        predicted_discrete_qs = self._discrete_critic_forward(
+            observations=observations, use_target=False, observation_features=observation_features
+        )
+
+        # Use gather to select Q-values for taken actions
+        predicted_discrete_q = torch.gather(predicted_discrete_qs, dim=1, index=actions_discrete).squeeze(-1)
+
+        # Compute MSE loss between predicted and target Q-values
+        discrete_critic_loss = F.mse_loss(input=predicted_discrete_q, target=target_discrete_q)
+        return discrete_critic_loss
+
+    def _compute_loss_actor(self, batch: dict[str, Any]) -> Tensor:
+        observations = batch["state"]
+        observation_features = batch.get("observation_feature")
+
+        actions_pi, log_probs, _ = self.policy.actor(observations, observation_features)
+
+        q_preds = self._critic_forward(
+            observations=observations,
+            actions=actions_pi,
+            use_target=False,
+            observation_features=observation_features,
+        )
+        min_q_preds = q_preds.min(dim=0)[0]
+
+        actor_loss = ((self.temperature * log_probs) - min_q_preds).mean()
+        return actor_loss
+
+    def _compute_loss_temperature(self, batch: dict[str, Any]) -> Tensor:
+        """Compute the temperature loss"""
+        observations = batch["state"]
+        observation_features = batch.get("observation_feature")
+
+        # calculate temperature loss
+        with torch.no_grad():
+            _, log_probs, _ = self.policy.actor(observations, observation_features)
+
+        temperature_loss = (-self.log_alpha.exp() * (log_probs + self.target_entropy)).mean()
+        return temperature_loss
+
+    def _update_target_networks(self) -> None:
+        """Update target networks with exponential moving average"""
+        for target_p, p in zip(
+            self.critic_target.parameters(), self.critic_ensemble.parameters(), strict=True
+        ):
+            target_p.data.copy_(
+                p.data * self.config.critic_target_update_weight
+                + target_p.data * (1.0 - self.config.critic_target_update_weight)
+            )
+        if self.policy_config.num_discrete_actions is not None:
+            for target_p, p in zip(
+                self.discrete_critic_target.parameters(),
+                self.policy.discrete_critic.parameters(),
+                strict=True,
+            ):
+                target_p.data.copy_(
+                    p.data * self.config.critic_target_update_weight
+                    + target_p.data * (1.0 - self.config.critic_target_update_weight)
+                )
+
+    def _prepare_forward_batch(
+        self, batch: BatchType, *, include_complementary_info: bool = True
+    ) -> dict[str, Any]:
+        observations = batch["state"]
+        next_observations = batch["next_state"]
+        observation_features, next_observation_features = self.get_observation_features(
+            observations, next_observations
+        )
+        forward_batch: dict[str, Any] = {
+            ACTION: batch[ACTION],
+            "reward": batch["reward"],
+            "state": observations,
+            "next_state": next_observations,
+            "done": batch["done"],
+            "observation_feature": observation_features,
+            "next_observation_feature": next_observation_features,
+        }
+        if include_complementary_info and "complementary_info" in batch:
+            forward_batch["complementary_info"] = batch["complementary_info"]
+        return forward_batch
+
+    def make_optimizers_and_scheduler(self) -> dict[str, Optimizer]:
+        """
+        Creates and returns optimizers for the actor, critic, and temperature components of a reinforcement learning policy.
+
+        This function sets up Adam optimizers for:
+        - The **actor network**, ensuring that only relevant parameters are optimized.
+        - The **critic ensemble**, which evaluates the value function.
+        - The **temperature parameter**, which controls the entropy in soft actor-critic (SAC)-like methods.
+
+        It also initializes a learning rate scheduler, though currently, it is set to `None`.
+
+        NOTE:
+        - If the encoder is shared, its parameters are excluded from the actor's optimization process.
+        - The policy's log temperature (`log_alpha`) is wrapped in a list to ensure proper optimization as a standalone tensor.
+
+        Args:
+            cfg: Configuration object containing hyperparameters.
+            policy (nn.Module): The policy model containing the actor, critic, and temperature components.
+
+        Returns:
+            A dictionary mapping component names ("actor", "critic", "temperature")
+            to their respective Adam optimizers.
+        """
+        actor_params = self.policy.get_optim_params()["actor"]
+        self.optimizers = {
+            "actor": torch.optim.Adam(actor_params, lr=self.config.actor_lr),
+            "critic": torch.optim.Adam(self.critic_ensemble.parameters(), lr=self.config.critic_lr),
+            "temperature": torch.optim.Adam([self.log_alpha], lr=self.config.temperature_lr),
+        }
+        if self.policy_config.num_discrete_actions is not None:
+            self.optimizers["discrete_critic"] = torch.optim.Adam(
+                self.policy.discrete_critic.parameters(), lr=self.config.critic_lr
+            )
+        return self.optimizers
+
+    def get_optimizers(self) -> dict[str, Optimizer]:
+        return self.optimizers
+
+    def get_weights(self) -> dict[str, Any]:
+        """Send actor + discrete-critic state dicts."""
+        state_dicts: dict[str, Any] = {
+            "policy": move_state_dict_to_device(self.policy.actor.state_dict(), device="cpu"),
+        }
+        if self.policy_config.num_discrete_actions is not None:
+            state_dicts["discrete_critic"] = move_state_dict_to_device(
+                self.policy.discrete_critic.state_dict(), device="cpu"
+            )
+        return state_dicts
+
+    def load_weights(self, weights: dict[str, Any], device: str | torch.device = "cpu") -> None:
+        """Load actor + discrete-critic weights into the policy."""
+        self.policy.load_actor_weights(weights, device=device)
+
+    def get_observation_features(
+        self, observations: Tensor, next_observations: Tensor
+    ) -> tuple[Tensor | None, Tensor | None]:
+        """
+        Get observation features from the policy encoder. It act as cache for the observation features.
+        when the encoder is frozen, the observation features are not updated.
+        We can save compute by caching the observation features.
+
+        Args:
+            policy: The policy model
+            observations: The current observations
+            next_observations: The next observations
+
+        Returns:
+            tuple: observation_features, next_observation_features
+        """
+
+        if self.policy.config.vision_encoder_name is None or not self.policy.config.freeze_vision_encoder:
+            return None, None
+
+        with torch.no_grad():
+            observation_features = self.policy.actor.encoder.get_cached_image_features(observations)
+            next_observation_features = self.policy.actor.encoder.get_cached_image_features(next_observations)
+
+        return observation_features, next_observation_features
+
+
+class CriticHead(nn.Module):
+    def __init__(
+        self,
+        input_dim: int,
+        hidden_dims: list[int],
+        activations: Callable[[torch.Tensor], torch.Tensor] | str = nn.SiLU(),
+        activate_final: bool = False,
+        dropout_rate: float | None = None,
+        init_final: float | None = None,
+        final_activation: Callable[[torch.Tensor], torch.Tensor] | str | None = None,
+    ):
+        super().__init__()
+        self.net = MLP(
+            input_dim=input_dim,
+            hidden_dims=hidden_dims,
+            activations=activations,
+            activate_final=activate_final,
+            dropout_rate=dropout_rate,
+            final_activation=final_activation,
+        )
+        self.output_layer = nn.Linear(in_features=hidden_dims[-1], out_features=1)
+        if init_final is not None:
+            nn.init.uniform_(self.output_layer.weight, -init_final, init_final)
+            nn.init.uniform_(self.output_layer.bias, -init_final, init_final)
+        else:
+            orthogonal_init()(self.output_layer.weight)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.output_layer(self.net(x))
+
+
+class CriticEnsemble(nn.Module):
+    """
+    CriticEnsemble wraps multiple CriticHead modules into an ensemble.
+
+    Args:
+        encoder (GaussianActorObservationEncoder): encoder for observations.
+        ensemble (List[CriticHead]): list of critic heads.
+        init_final (float | None): optional initializer scale for final layers.
+
+    Forward returns a tensor of shape (num_critics, batch_size) containing Q-values.
+    """
+
+    def __init__(
+        self,
+        encoder: GaussianActorObservationEncoder,
+        ensemble: list[CriticHead],
+        init_final: float | None = None,
+    ):
+        super().__init__()
+        self.encoder = encoder
+        self.init_final = init_final
+        self.critics = nn.ModuleList(ensemble)
+
+    def forward(
+        self,
+        observations: dict[str, torch.Tensor],
+        actions: torch.Tensor,
+        observation_features: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        device = get_device_from_parameters(self)
+        # Move each tensor in observations to device
+        observations = {k: v.to(device) for k, v in observations.items()}
+
+        obs_enc = self.encoder(observations, cache=observation_features)
+
+        inputs = torch.cat([obs_enc, actions], dim=-1)
+
+        # Loop through critics and collect outputs
+        q_values = []
+        for critic in self.critics:
+            q_values.append(critic(inputs))
+
+        # Stack outputs to match expected shape [num_critics, batch_size]
+        q_values = torch.stack([q.squeeze(-1) for q in q_values], dim=0)
+        return q_values

src/lerobot/rl/buffer.py

@@ -97,8 +97,8 @@ class ReplayBuffer:
         Args:
             capacity (int): Maximum number of transitions to store in the buffer.
             device (str): The device where the tensors will be moved when sampling ("cuda:0" or "cpu").
-            state_keys (List[str]): The list of keys that appear in `state` and `next_state`.
-            image_augmentation_function (Optional[Callable]): A function that takes a batch of images
+            state_keys (list[str]): The list of keys that appear in `state` and `next_state`.
+            image_augmentation_function (Callable | None): A function that takes a batch of images
                 and returns a batch of augmented images. If None, a default augmentation function is used.
             use_drq (bool): Whether to use the default DRQ image augmentation style, when sampling in the buffer.
             storage_device: The device (e.g. "cpu" or "cuda:0") where the data will be stored.
@@ -634,7 +634,7 @@ class ReplayBuffer:
                 If None, you must handle or define default keys.
 
         Returns:
-            transitions (List[Transition]):
+            transitions (list[Transition]):
                 A list of Transition dictionaries with the same length as `dataset`.
         """
         if state_keys is None:

src/lerobot/rl/crop_dataset_roi.py

@@ -176,11 +176,11 @@ def convert_lerobot_dataset_to_cropped_lerobot_dataset(
 
     Args:
         original_dataset (LeRobotDataset): The source dataset.
-        crop_params_dict (Dict[str, Tuple[int, int, int, int]]):
+        crop_params_dict (dict[str, Tuple[int, int, int, int]]):
             A dictionary mapping observation keys to crop parameters (top, left, height, width).
         new_repo_id (str): Repository id for the new dataset.
         new_dataset_root (str): The root directory where the new dataset will be written.
-        resize_size (Tuple[int, int], optional): The target size (height, width) after cropping.
+        resize_size (tuple[int, int], optional): The target size (height, width) after cropping.
             Defaults to (128, 128).
 
     Returns:

src/lerobot/rl/data_sources/__init__.py

@@ -0,0 +1,19 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from lerobot.types import BatchType
+
+from .data_mixer import DataMixer, OnlineOfflineMixer
+
+__all__ = ["BatchType", "DataMixer", "OnlineOfflineMixer"]

src/lerobot/rl/data_sources/data_mixer.py

@@ -0,0 +1,97 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import abc
+
+from lerobot.types import BatchType
+
+from ..buffer import ReplayBuffer, concatenate_batch_transitions
+
+
+class DataMixer(abc.ABC):
+    """Abstract interface for all data mixing strategies."""
+
+    @abc.abstractmethod
+    def sample(self, batch_size: int) -> BatchType:
+        """Draw one batch of ``batch_size`` transitions."""
+        raise NotImplementedError
+
+    def get_iterator(
+        self,
+        batch_size: int,
+        async_prefetch: bool = True,
+        queue_size: int = 2,
+    ):
+        """Infinite iterator that yields batches."""
+        while True:
+            yield self.sample(batch_size)
+
+
+class OnlineOfflineMixer(DataMixer):
+    """Mixes transitions from an online and an offline replay buffer."""
+
+    def __init__(
+        self,
+        online_buffer: ReplayBuffer,
+        offline_buffer: ReplayBuffer | None = None,
+        online_ratio: float = 1.0,
+    ):
+        if not 0.0 <= online_ratio <= 1.0:
+            raise ValueError(f"online_ratio must be in [0, 1], got {online_ratio}")
+        self.online_buffer = online_buffer
+        self.offline_buffer = offline_buffer
+        self.online_ratio = online_ratio
+
+    def sample(self, batch_size: int) -> BatchType:
+        if self.offline_buffer is None:
+            return self.online_buffer.sample(batch_size)
+
+        n_online = max(1, int(batch_size * self.online_ratio))
+        n_offline = batch_size - n_online
+
+        online_batch = self.online_buffer.sample(n_online)
+        offline_batch = self.offline_buffer.sample(n_offline)
+        return concatenate_batch_transitions(online_batch, offline_batch)
+
+    def get_iterator(
+        self,
+        batch_size: int,
+        async_prefetch: bool = True,
+        queue_size: int = 2,
+    ):
+        """Yield batches by composing buffer async iterators."""
+
+        n_online = max(1, int(batch_size * self.online_ratio))
+
+        online_iter = self.online_buffer.get_iterator(
+            batch_size=n_online,
+            async_prefetch=async_prefetch,
+            queue_size=queue_size,
+        )
+
+        if self.offline_buffer is None:
+            yield from online_iter
+            return
+
+        n_offline = batch_size - n_online
+        offline_iter = self.offline_buffer.get_iterator(
+            batch_size=n_offline,
+            async_prefetch=async_prefetch,
+            queue_size=queue_size,
+        )
+
+        while True:
+            yield concatenate_batch_transitions(next(online_iter), next(offline_iter))

src/lerobot/rl/eval_policy.py

@@ -17,7 +17,6 @@ import logging
 
 from lerobot.cameras import opencv  # noqa: F401
 from lerobot.configs import parser
-from lerobot.configs.train import TrainRLServerPipelineConfig
 from lerobot.datasets import LeRobotDataset
 from lerobot.policies import make_policy
 from lerobot.robots import (  # noqa: F401
@@ -31,6 +30,7 @@ from lerobot.teleoperators import (
 )
 
 from .gym_manipulator import make_robot_env
+from .train_rl import TrainRLServerPipelineConfig
 
 logging.basicConfig(level=logging.INFO)
 

src/lerobot/rl/gym_manipulator.py

@@ -74,6 +74,7 @@ from lerobot.teleoperators import (
 from lerobot.teleoperators.teleoperator import Teleoperator
 from lerobot.teleoperators.utils import TeleopEvents
 from lerobot.utils.constants import ACTION, DONE, OBS_IMAGES, OBS_STATE, REWARD
+from lerobot.utils.import_utils import require_package
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import log_say
 
@@ -312,6 +313,7 @@ def make_robot_env(cfg: HILSerlRobotEnvConfig) -> tuple[gym.Env, Any]:
     # Check if this is a GymHIL simulation environment
     if cfg.name == "gym_hil":
         assert cfg.robot is None and cfg.teleop is None, "GymHIL environment does not support robot or teleop"
+        require_package("gym-hil", extra="hilserl", import_name="gym_hil")
         import gym_hil  # noqa: F401
 
         # Extract gripper settings with defaults
@@ -383,10 +385,21 @@ def make_processors(
             GymHILAdapterProcessorStep(),
             Numpy2TorchActionProcessorStep(),
             VanillaObservationProcessorStep(),
-            AddBatchDimensionProcessorStep(),
-            DeviceProcessorStep(device=device),
         ]
 
+        # Add time limit processor if reset config exists
+        if cfg.processor.reset is not None:
+            env_pipeline_steps.append(
+                TimeLimitProcessorStep(max_episode_steps=int(cfg.processor.reset.control_time_s * cfg.fps))
+            )
+
+        env_pipeline_steps.extend(
+            [
+                AddBatchDimensionProcessorStep(),
+                DeviceProcessorStep(device=device),
+            ]
+        )
+
         return DataProcessorPipeline(
             steps=env_pipeline_steps, to_transition=identity_transition, to_output=identity_transition
         ), DataProcessorPipeline(
@@ -551,8 +564,19 @@ def step_env_and_process_transition(
     terminated = terminated or processed_action_transition[TransitionKey.DONE]
     truncated = truncated or processed_action_transition[TransitionKey.TRUNCATED]
     complementary_data = processed_action_transition[TransitionKey.COMPLEMENTARY_DATA].copy()
+
+    if hasattr(env, "get_raw_joint_positions"):
+        raw_joint_positions = env.get_raw_joint_positions()
+        if raw_joint_positions is not None:
+            complementary_data["raw_joint_positions"] = raw_joint_positions
+
+    # Merge env and action-processor info: env wins for str keys, action-processor
+    # wins for `TeleopEvents` enum keys
+    action_info = processed_action_transition[TransitionKey.INFO]
     new_info = info.copy()
-    new_info.update(processed_action_transition[TransitionKey.INFO])
+    for key, value in action_info.items():
+        if isinstance(key, TeleopEvents):
+            new_info[key] = value
 
     new_transition = create_transition(
         observation=obs,
@@ -568,6 +592,24 @@ def step_env_and_process_transition(
     return new_transition
 
 
+def reset_and_build_transition(
+    env: gym.Env,
+    env_processor: DataProcessorPipeline[EnvTransition, EnvTransition],
+    action_processor: DataProcessorPipeline[EnvTransition, EnvTransition],
+) -> EnvTransition:
+    """Reset env + processors and return the first env-processed transition."""
+    obs, info = env.reset()
+    env_processor.reset()
+    action_processor.reset()
+    complementary_data: dict[str, Any] = {}
+    if hasattr(env, "get_raw_joint_positions"):
+        raw_joint_positions = env.get_raw_joint_positions()
+        if raw_joint_positions is not None:
+            complementary_data["raw_joint_positions"] = raw_joint_positions
+    transition = create_transition(observation=obs, info=info, complementary_data=complementary_data)
+    return env_processor(data=transition)
+
+
 def control_loop(
     env: gym.Env,
     env_processor: DataProcessorPipeline[EnvTransition, EnvTransition],
@@ -593,17 +635,7 @@ def control_loop(
     print("- When not intervening, robot will stay still")
     print("- Press Ctrl+C to exit")
 
-    # Reset environment and processors
-    obs, info = env.reset()
-    complementary_data = (
-        {"raw_joint_positions": info.pop("raw_joint_positions")} if "raw_joint_positions" in info else {}
-    )
-    env_processor.reset()
-    action_processor.reset()
-
-    # Process initial observation
-    transition = create_transition(observation=obs, info=info, complementary_data=complementary_data)
-    transition = env_processor(data=transition)
+    transition = reset_and_build_transition(env, env_processor, action_processor)
 
     # Determine if gripper is used
     use_gripper = cfg.env.processor.gripper.use_gripper if cfg.env.processor.gripper is not None else True
@@ -659,79 +691,82 @@ def control_loop(
     episode_step = 0
     episode_start_time = time.perf_counter()
 
-    while episode_idx < cfg.dataset.num_episodes_to_record:
-        step_start_time = time.perf_counter()
+    try:
+        while episode_idx < cfg.dataset.num_episodes_to_record:
+            step_start_time = time.perf_counter()
 
-        # Create a neutral action (no movement)
-        neutral_action = torch.tensor([0.0, 0.0, 0.0], dtype=torch.float32)
-        if use_gripper:
-            neutral_action = torch.cat([neutral_action, torch.tensor([0.0])])  # Gripper stay
+            # Create a neutral action (no movement)
+            neutral_action = torch.tensor([0.0, 0.0, 0.0], dtype=torch.float32)
+            if use_gripper:
+                neutral_action = torch.cat([neutral_action, torch.tensor([1.0])])  # Gripper stay
 
-        # Use the new step function
-        transition = step_env_and_process_transition(
-            env=env,
-            transition=transition,
-            action=neutral_action,
-            env_processor=env_processor,
-            action_processor=action_processor,
-        )
-        terminated = transition.get(TransitionKey.DONE, False)
-        truncated = transition.get(TransitionKey.TRUNCATED, False)
-
-        if cfg.mode == "record":
-            observations = {
+            observation = {
                 k: v.squeeze(0).cpu()
                 for k, v in transition[TransitionKey.OBSERVATION].items()
                 if isinstance(v, torch.Tensor)
             }
-            # Use teleop_action if available, otherwise use the action from the transition
-            action_to_record = transition[TransitionKey.COMPLEMENTARY_DATA].get(
-                "teleop_action", transition[TransitionKey.ACTION]
+
+            transition = step_env_and_process_transition(
+                env=env,
+                transition=transition,
+                action=neutral_action,
+                env_processor=env_processor,
+                action_processor=action_processor,
             )
-            frame = {
-                **observations,
-                ACTION: action_to_record.cpu(),
-                REWARD: np.array([transition[TransitionKey.REWARD]], dtype=np.float32),
-                DONE: np.array([terminated or truncated], dtype=bool),
-            }
-            if use_gripper:
-                discrete_penalty = transition[TransitionKey.COMPLEMENTARY_DATA].get("discrete_penalty", 0.0)
-                frame["complementary_info.discrete_penalty"] = np.array([discrete_penalty], dtype=np.float32)
+            terminated = transition.get(TransitionKey.DONE, False)
+            truncated = transition.get(TransitionKey.TRUNCATED, False)
 
-            if dataset is not None:
-                frame["task"] = cfg.dataset.task
-                dataset.add_frame(frame)
+            if cfg.mode == "record":
+                action_to_record = transition[TransitionKey.COMPLEMENTARY_DATA].get(
+                    "teleop_action", transition[TransitionKey.ACTION]
+                )
+                frame = {
+                    **observation,
+                    ACTION: action_to_record.cpu(),
+                    REWARD: np.array([transition[TransitionKey.REWARD]], dtype=np.float32),
+                    DONE: np.array([terminated or truncated], dtype=bool),
+                }
+                if use_gripper:
+                    discrete_penalty = transition[TransitionKey.COMPLEMENTARY_DATA].get(
+                        "discrete_penalty", 0.0
+                    )
+                    frame["complementary_info.discrete_penalty"] = np.array(
+                        [discrete_penalty], dtype=np.float32
+                    )
 
-        episode_step += 1
+                if dataset is not None:
+                    frame["task"] = cfg.dataset.task
+                    dataset.add_frame(frame)
 
-        # Handle episode termination
-        if terminated or truncated:
-            episode_time = time.perf_counter() - episode_start_time
-            logging.info(
-                f"Episode ended after {episode_step} steps in {episode_time:.1f}s with reward {transition[TransitionKey.REWARD]}"
-            )
-            episode_step = 0
-            episode_idx += 1
+            episode_step += 1
 
-            if dataset is not None:
-                if transition[TransitionKey.INFO].get(TeleopEvents.RERECORD_EPISODE, False):
-                    logging.info(f"Re-recording episode {episode_idx}")
-                    dataset.clear_episode_buffer()
-                    episode_idx -= 1
-                else:
-                    logging.info(f"Saving episode {episode_idx}")
-                    dataset.save_episode()
+            # Handle episode termination
+            if terminated or truncated:
+                episode_time = time.perf_counter() - episode_start_time
+                logging.info(
+                    f"Episode ended after {episode_step} steps in {episode_time:.1f}s with reward {transition[TransitionKey.REWARD]}"
+                )
+                episode_step = 0
+                episode_idx += 1
 
-            # Reset for new episode
-            obs, info = env.reset()
-            env_processor.reset()
-            action_processor.reset()
+                if dataset is not None:
+                    if transition[TransitionKey.INFO].get(TeleopEvents.RERECORD_EPISODE, False):
+                        logging.info(f"Re-recording episode {episode_idx}")
+                        dataset.clear_episode_buffer()
+                        episode_idx -= 1
+                    else:
+                        logging.info(f"Saving episode {episode_idx}")
+                        dataset.save_episode()
 
-            transition = create_transition(observation=obs, info=info)
-            transition = env_processor(transition)
+                # Reset for new episode
+                transition = reset_and_build_transition(env, env_processor, action_processor)
 
-        # Maintain fps timing
-        precise_sleep(max(dt - (time.perf_counter() - step_start_time), 0.0))
+            # Maintain fps timing
+            precise_sleep(max(dt - (time.perf_counter() - step_start_time), 0.0))
+    finally:
+        if dataset is not None and dataset.writer is not None and dataset.writer.image_writer is not None:
+            logging.info("Waiting for image writer to finish...")
+            dataset.writer.image_writer.stop()
 
     if dataset is not None and cfg.dataset.push_to_hub:
         logging.info("Finalizing dataset before pushing to hub")

src/lerobot/rl/learner.py

@@ -44,6 +44,8 @@ For more details on the complete HILSerl training workflow, see:
 https://github.com/michel-aractingi/lerobot-hilserl-guide
 """
 
+from __future__ import annotations
+
 import logging
 import os
 import shutil
@@ -51,8 +53,8 @@ import time
 from concurrent.futures import ThreadPoolExecutor
 from pathlib import Path
 from pprint import pformat
+from typing import TYPE_CHECKING, Any
 
-import grpc
 import torch
 from termcolor import colored
 from torch import nn
@@ -68,20 +70,11 @@ from lerobot.common.train_utils import (
 )
 from lerobot.common.wandb_utils import WandBLogger
 from lerobot.configs import parser
-from lerobot.configs.train import TrainRLServerPipelineConfig
 from lerobot.datasets import LeRobotDataset, make_dataset
-from lerobot.policies import make_policy
-from lerobot.policies.sac.modeling_sac import SACPolicy
+from lerobot.policies import make_policy, make_pre_post_processors
 from lerobot.robots import so_follower  # noqa: F401
 from lerobot.teleoperators import gamepad, so_leader  # noqa: F401
 from lerobot.teleoperators.utils import TeleopEvents
-from lerobot.transport import services_pb2_grpc
-from lerobot.transport.utils import (
-    MAX_MESSAGE_SIZE,
-    bytes_to_python_object,
-    bytes_to_transitions,
-    state_to_bytes,
-)
 from lerobot.utils.constants import (
     ACTION,
     CHECKPOINTS_DIR,
@@ -90,20 +83,44 @@ from lerobot.utils.constants import (
     TRAINING_STATE_DIR,
 )
 from lerobot.utils.device_utils import get_safe_torch_device
+from lerobot.utils.import_utils import _grpc_available, require_package
 from lerobot.utils.process import ProcessSignalHandler
 from lerobot.utils.random_utils import set_seed
-from lerobot.utils.transition import move_state_dict_to_device, move_transition_to_device
 from lerobot.utils.utils import (
     format_big_number,
     init_logging,
 )
 
-from .buffer import ReplayBuffer, concatenate_batch_transitions
+if TYPE_CHECKING or _grpc_available:
+    import grpc
+
+    from lerobot.transport import services_pb2_grpc
+    from lerobot.transport.utils import (
+        MAX_MESSAGE_SIZE,
+        bytes_to_python_object,
+        bytes_to_transitions,
+        state_to_bytes,
+    )
+else:
+    grpc = None
+    services_pb2_grpc = None
+    MAX_MESSAGE_SIZE = None
+    bytes_to_python_object = None
+    bytes_to_transitions = None
+    state_to_bytes = None
+
+from .algorithms.base import RLAlgorithm
+from .algorithms.factory import make_algorithm
+from .buffer import ReplayBuffer
+from .data_sources import OnlineOfflineMixer
 from .learner_service import MAX_WORKERS, SHUTDOWN_TIMEOUT, LearnerService
+from .train_rl import TrainRLServerPipelineConfig
+from .trainer import RLTrainer
 
 
 @parser.wrap()
 def train_cli(cfg: TrainRLServerPipelineConfig):
+    require_package("grpcio", extra="hilserl", import_name="grpc")
     if not use_threads(cfg):
         import torch.multiprocessing as mp
 
@@ -179,7 +196,7 @@ def train(cfg: TrainRLServerPipelineConfig, job_name: str | None = None):
 def start_learner_threads(
     cfg: TrainRLServerPipelineConfig,
     wandb_logger: WandBLogger | None,
-    shutdown_event: any,  # Event,
+    shutdown_event: Any,  # Event
 ) -> None:
     """
     Start the learner threads for training.
@@ -253,7 +270,7 @@ def start_learner_threads(
 def add_actor_information_and_train(
     cfg: TrainRLServerPipelineConfig,
     wandb_logger: WandBLogger | None,
-    shutdown_event: any,  # Event,
+    shutdown_event: Any,  # Event
     transition_queue: Queue,
     interaction_message_queue: Queue,
     parameters_queue: Queue,
@@ -266,8 +283,8 @@ def add_actor_information_and_train(
     - Transfers transitions from the actor to the replay buffer.
     - Logs received interaction messages.
     - Ensures training begins only when the replay buffer has a sufficient number of transitions.
-    - Samples batches from the replay buffer and performs multiple critic updates.
-    - Periodically updates the actor, critic, and temperature optimizers.
+    - Delegates training updates to an ``RLAlgorithm``.
+    - Periodically pushes updated weights to actors.
     - Logs training statistics, including loss values and optimization frequency.
 
     NOTE: This function doesn't have a single responsibility, it should be split into multiple functions
@@ -286,17 +303,13 @@ def add_actor_information_and_train(
     # of 7%
     device = get_safe_torch_device(try_device=cfg.policy.device, log=True)
     storage_device = get_safe_torch_device(try_device=cfg.policy.storage_device)
-    clip_grad_norm_value = cfg.policy.grad_clip_norm
     online_step_before_learning = cfg.policy.online_step_before_learning
-    utd_ratio = cfg.policy.utd_ratio
     fps = cfg.env.fps
     log_freq = cfg.log_freq
     save_freq = cfg.save_freq
-    policy_update_freq = cfg.policy.policy_update_freq
     policy_parameters_push_frequency = cfg.policy.actor_learner_config.policy_parameters_push_frequency
     saving_checkpoint = cfg.save_checkpoint
     online_steps = cfg.policy.online_steps
-    async_prefetch = cfg.policy.async_prefetch
 
     # Initialize logging for multiprocessing
     if not use_threads(cfg):
@@ -308,7 +321,7 @@ def add_actor_information_and_train(
 
     logging.info("Initializing policy")
 
-    policy: SACPolicy = make_policy(
+    policy = make_policy(
         cfg=cfg.policy,
         env_cfg=cfg.env,
     )
@@ -317,15 +330,17 @@ def add_actor_information_and_train(
 
     policy.train()
 
-    push_actor_policy_to_queue(parameters_queue=parameters_queue, policy=policy)
+    algorithm = make_algorithm(cfg=cfg.algorithm, policy=policy)
 
+    preprocessor, postprocessor = make_pre_post_processors(
+        policy_cfg=cfg.policy,
+        dataset_stats=cfg.policy.dataset_stats,
+    )
+
+    # Push initial policy weights to actors
+    push_actor_policy_to_queue(parameters_queue=parameters_queue, algorithm=algorithm)
     last_time_policy_pushed = time.time()
 
-    optimizers, lr_scheduler = make_optimizers_and_scheduler(cfg=cfg, policy=policy)
-
-    # If we are resuming, we need to load the training state
-    resume_optimization_step, resume_interaction_step = load_training_state(cfg=cfg, optimizers=optimizers)
-
     log_training_info(cfg=cfg, policy=policy)
 
     replay_buffer = initialize_replay_buffer(cfg, device, storage_device)
@@ -338,21 +353,35 @@ def add_actor_information_and_train(
             device=device,
             storage_device=storage_device,
         )
-        batch_size: int = batch_size // 2  # We will sample from both replay buffer
+
+    # DataMixer: online-only or online/offline 50-50 mix
+    data_mixer = OnlineOfflineMixer(
+        online_buffer=replay_buffer,
+        offline_buffer=offline_replay_buffer,
+        online_ratio=cfg.online_ratio,
+    )
+    # RLTrainer owns the iterator, preprocessor, and creates optimizers.
+    trainer = RLTrainer(
+        algorithm=algorithm,
+        data_mixer=data_mixer,
+        batch_size=batch_size,
+        preprocessor=preprocessor,
+    )
+
+    # If we are resuming, we need to load the training state
+    optimizers = algorithm.get_optimizers()
+    resume_optimization_step, resume_interaction_step = load_training_state(cfg=cfg, optimizers=optimizers)
 
     logging.info("Starting learner thread")
     interaction_message = None
     optimization_step = resume_optimization_step if resume_optimization_step is not None else 0
+    algorithm.optimization_step = optimization_step
     interaction_step_shift = resume_interaction_step if resume_interaction_step is not None else 0
 
     dataset_repo_id = None
     if cfg.dataset is not None:
         dataset_repo_id = cfg.dataset.repo_id
 
-    # Initialize iterators
-    online_iterator = None
-    offline_iterator = None
-
     # NOTE: THIS IS THE MAIN LOOP OF THE LEARNER
     while True:
         # Exit the training loop if shutdown is requested
@@ -365,7 +394,6 @@ def add_actor_information_and_train(
             transition_queue=transition_queue,
             replay_buffer=replay_buffer,
             offline_replay_buffer=offline_replay_buffer,
-            device=device,
             dataset_repo_id=dataset_repo_id,
             shutdown_event=shutdown_event,
         )
@@ -382,180 +410,20 @@ def add_actor_information_and_train(
         if len(replay_buffer) < online_step_before_learning:
             continue
 
-        if online_iterator is None:
-            online_iterator = replay_buffer.get_iterator(
-                batch_size=batch_size, async_prefetch=async_prefetch, queue_size=2
-            )
-
-        if offline_replay_buffer is not None and offline_iterator is None:
-            offline_iterator = offline_replay_buffer.get_iterator(
-                batch_size=batch_size, async_prefetch=async_prefetch, queue_size=2
-            )
-
         time_for_one_optimization_step = time.time()
-        for _ in range(utd_ratio - 1):
-            # Sample from the iterators
-            batch = next(online_iterator)
 
-            if dataset_repo_id is not None:
-                batch_offline = next(offline_iterator)
-                batch = concatenate_batch_transitions(
-                    left_batch_transitions=batch, right_batch_transition=batch_offline
-                )
-
-            actions = batch[ACTION]
-            rewards = batch["reward"]
-            observations = batch["state"]
-            next_observations = batch["next_state"]
-            done = batch["done"]
-            check_nan_in_transition(observations=observations, actions=actions, next_state=next_observations)
-
-            observation_features, next_observation_features = get_observation_features(
-                policy=policy, observations=observations, next_observations=next_observations
-            )
-
-            # Create a batch dictionary with all required elements for the forward method
-            forward_batch = {
-                ACTION: actions,
-                "reward": rewards,
-                "state": observations,
-                "next_state": next_observations,
-                "done": done,
-                "observation_feature": observation_features,
-                "next_observation_feature": next_observation_features,
-                "complementary_info": batch["complementary_info"],
-            }
-
-            # Use the forward method for critic loss
-            critic_output = policy.forward(forward_batch, model="critic")
-
-            # Main critic optimization
-            loss_critic = critic_output["loss_critic"]
-            optimizers["critic"].zero_grad()
-            loss_critic.backward()
-            critic_grad_norm = torch.nn.utils.clip_grad_norm_(
-                parameters=policy.critic_ensemble.parameters(), max_norm=clip_grad_norm_value
-            )
-            optimizers["critic"].step()
-
-            # Discrete critic optimization (if available)
-            if policy.config.num_discrete_actions is not None:
-                discrete_critic_output = policy.forward(forward_batch, model="discrete_critic")
-                loss_discrete_critic = discrete_critic_output["loss_discrete_critic"]
-                optimizers["discrete_critic"].zero_grad()
-                loss_discrete_critic.backward()
-                discrete_critic_grad_norm = torch.nn.utils.clip_grad_norm_(
-                    parameters=policy.discrete_critic.parameters(), max_norm=clip_grad_norm_value
-                )
-                optimizers["discrete_critic"].step()
-
-            # Update target networks (main and discrete)
-            policy.update_target_networks()
-
-        # Sample for the last update in the UTD ratio
-        batch = next(online_iterator)
-
-        if dataset_repo_id is not None:
-            batch_offline = next(offline_iterator)
-            batch = concatenate_batch_transitions(
-                left_batch_transitions=batch, right_batch_transition=batch_offline
-            )
-
-        actions = batch[ACTION]
-        rewards = batch["reward"]
-        observations = batch["state"]
-        next_observations = batch["next_state"]
-        done = batch["done"]
-
-        check_nan_in_transition(observations=observations, actions=actions, next_state=next_observations)
-
-        observation_features, next_observation_features = get_observation_features(
-            policy=policy, observations=observations, next_observations=next_observations
-        )
-
-        # Create a batch dictionary with all required elements for the forward method
-        forward_batch = {
-            ACTION: actions,
-            "reward": rewards,
-            "state": observations,
-            "next_state": next_observations,
-            "done": done,
-            "observation_feature": observation_features,
-            "next_observation_feature": next_observation_features,
-        }
-
-        critic_output = policy.forward(forward_batch, model="critic")
-
-        loss_critic = critic_output["loss_critic"]
-        optimizers["critic"].zero_grad()
-        loss_critic.backward()
-        critic_grad_norm = torch.nn.utils.clip_grad_norm_(
-            parameters=policy.critic_ensemble.parameters(), max_norm=clip_grad_norm_value
-        ).item()
-        optimizers["critic"].step()
-
-        # Initialize training info dictionary
-        training_infos = {
-            "loss_critic": loss_critic.item(),
-            "critic_grad_norm": critic_grad_norm,
-        }
-
-        # Discrete critic optimization (if available)
-        if policy.config.num_discrete_actions is not None:
-            discrete_critic_output = policy.forward(forward_batch, model="discrete_critic")
-            loss_discrete_critic = discrete_critic_output["loss_discrete_critic"]
-            optimizers["discrete_critic"].zero_grad()
-            loss_discrete_critic.backward()
-            discrete_critic_grad_norm = torch.nn.utils.clip_grad_norm_(
-                parameters=policy.discrete_critic.parameters(), max_norm=clip_grad_norm_value
-            ).item()
-            optimizers["discrete_critic"].step()
-
-            # Add discrete critic info to training info
-            training_infos["loss_discrete_critic"] = loss_discrete_critic.item()
-            training_infos["discrete_critic_grad_norm"] = discrete_critic_grad_norm
-
-        # Actor and temperature optimization (at specified frequency)
-        if optimization_step % policy_update_freq == 0:
-            for _ in range(policy_update_freq):
-                # Actor optimization
-                actor_output = policy.forward(forward_batch, model="actor")
-                loss_actor = actor_output["loss_actor"]
-                optimizers["actor"].zero_grad()
-                loss_actor.backward()
-                actor_grad_norm = torch.nn.utils.clip_grad_norm_(
-                    parameters=policy.actor.parameters(), max_norm=clip_grad_norm_value
-                ).item()
-                optimizers["actor"].step()
-
-                # Add actor info to training info
-                training_infos["loss_actor"] = loss_actor.item()
-                training_infos["actor_grad_norm"] = actor_grad_norm
-
-                # Temperature optimization
-                temperature_output = policy.forward(forward_batch, model="temperature")
-                loss_temperature = temperature_output["loss_temperature"]
-                optimizers["temperature"].zero_grad()
-                loss_temperature.backward()
-                temp_grad_norm = torch.nn.utils.clip_grad_norm_(
-                    parameters=[policy.log_alpha], max_norm=clip_grad_norm_value
-                ).item()
-                optimizers["temperature"].step()
-
-                # Add temperature info to training info
-                training_infos["loss_temperature"] = loss_temperature.item()
-                training_infos["temperature_grad_norm"] = temp_grad_norm
-                training_infos["temperature"] = policy.temperature
+        # One training step (trainer owns data_mixer iterator; algorithm owns UTD loop)
+        stats = trainer.training_step()
 
         # Push policy to actors if needed
         if time.time() - last_time_policy_pushed > policy_parameters_push_frequency:
-            push_actor_policy_to_queue(parameters_queue=parameters_queue, policy=policy)
+            push_actor_policy_to_queue(parameters_queue=parameters_queue, algorithm=algorithm)
             last_time_policy_pushed = time.time()
 
-        # Update target networks (main and discrete)
-        policy.update_target_networks()
+        training_infos = stats.to_log_dict()
 
         # Log training metrics at specified intervals
+        optimization_step = algorithm.optimization_step
         if optimization_step % log_freq == 0:
             training_infos["replay_buffer_size"] = len(replay_buffer)
             if offline_replay_buffer is not None:
@@ -583,7 +451,6 @@ def add_actor_information_and_train(
                 custom_step_key="Optimization step",
             )
 
-        optimization_step += 1
         if optimization_step % log_freq == 0:
             logging.info(f"[LEARNER] Number of optimization step: {optimization_step}")
 
@@ -600,6 +467,8 @@ def add_actor_information_and_train(
                 offline_replay_buffer=offline_replay_buffer,
                 dataset_repo_id=dataset_repo_id,
                 fps=fps,
+                preprocessor=preprocessor,
+                postprocessor=postprocessor,
             )
 
 
@@ -607,7 +476,7 @@ def start_learner(
     parameters_queue: Queue,
     transition_queue: Queue,
     interaction_message_queue: Queue,
-    shutdown_event: any,  # Event,
+    shutdown_event: Any,  # Event
     cfg: TrainRLServerPipelineConfig,
 ):
     """
@@ -684,6 +553,8 @@ def save_training_checkpoint(
     offline_replay_buffer: ReplayBuffer | None = None,
     dataset_repo_id: str | None = None,
     fps: int = 30,
+    preprocessor=None,
+    postprocessor=None,
 ) -> None:
     """
     Save training checkpoint and associated data.
@@ -707,6 +578,8 @@ def save_training_checkpoint(
         offline_replay_buffer: Optional offline replay buffer to save
         dataset_repo_id: Repository ID for dataset
         fps: Frames per second for dataset
+        preprocessor: Optional preprocessor pipeline to save
+        postprocessor: Optional postprocessor pipeline to save
     """
     logging.info(f"Checkpoint policy after step {optimization_step}")
     _num_digits = max(6, len(str(online_steps)))
@@ -723,6 +596,8 @@ def save_training_checkpoint(
         policy=policy,
         optimizer=optimizers,
         scheduler=None,
+        preprocessor=preprocessor,
+        postprocessor=postprocessor,
     )
 
     # Save interaction step manually
@@ -760,58 +635,6 @@ def save_training_checkpoint(
     logging.info("Resume training")
 
 
-def make_optimizers_and_scheduler(cfg: TrainRLServerPipelineConfig, policy: nn.Module):
-    """
-    Creates and returns optimizers for the actor, critic, and temperature components of a reinforcement learning policy.
-
-    This function sets up Adam optimizers for:
-    - The **actor network**, ensuring that only relevant parameters are optimized.
-    - The **critic ensemble**, which evaluates the value function.
-    - The **temperature parameter**, which controls the entropy in soft actor-critic (SAC)-like methods.
-
-    It also initializes a learning rate scheduler, though currently, it is set to `None`.
-
-    NOTE:
-    - If the encoder is shared, its parameters are excluded from the actor's optimization process.
-    - The policy's log temperature (`log_alpha`) is wrapped in a list to ensure proper optimization as a standalone tensor.
-
-    Args:
-        cfg: Configuration object containing hyperparameters.
-        policy (nn.Module): The policy model containing the actor, critic, and temperature components.
-
-    Returns:
-        Tuple[Dict[str, torch.optim.Optimizer], Optional[torch.optim.lr_scheduler._LRScheduler]]:
-        A tuple containing:
-        - `optimizers`: A dictionary mapping component names ("actor", "critic", "temperature") to their respective Adam optimizers.
-        - `lr_scheduler`: Currently set to `None` but can be extended to support learning rate scheduling.
-
-    """
-    optimizer_actor = torch.optim.Adam(
-        params=[
-            p
-            for n, p in policy.actor.named_parameters()
-            if not policy.config.shared_encoder or not n.startswith("encoder")
-        ],
-        lr=cfg.policy.actor_lr,
-    )
-    optimizer_critic = torch.optim.Adam(params=policy.critic_ensemble.parameters(), lr=cfg.policy.critic_lr)
-
-    if cfg.policy.num_discrete_actions is not None:
-        optimizer_discrete_critic = torch.optim.Adam(
-            params=policy.discrete_critic.parameters(), lr=cfg.policy.critic_lr
-        )
-    optimizer_temperature = torch.optim.Adam(params=[policy.log_alpha], lr=cfg.policy.critic_lr)
-    lr_scheduler = None
-    optimizers = {
-        "actor": optimizer_actor,
-        "critic": optimizer_critic,
-        "temperature": optimizer_temperature,
-    }
-    if cfg.policy.num_discrete_actions is not None:
-        optimizers["discrete_critic"] = optimizer_discrete_critic
-    return optimizers, lr_scheduler
-
-
 # Training setup functions
 
 
@@ -1016,33 +839,6 @@ def initialize_offline_replay_buffer(
 # Utilities/Helpers functions
 
 
-def get_observation_features(
-    policy: SACPolicy, observations: torch.Tensor, next_observations: torch.Tensor
-) -> tuple[torch.Tensor | None, torch.Tensor | None]:
-    """
-    Get observation features from the policy encoder. It act as cache for the observation features.
-    when the encoder is frozen, the observation features are not updated.
-    We can save compute by caching the observation features.
-
-    Args:
-        policy: The policy model
-        observations: The current observations
-        next_observations: The next observations
-
-    Returns:
-        tuple: observation_features, next_observation_features
-    """
-
-    if policy.config.vision_encoder_name is None or not policy.config.freeze_vision_encoder:
-        return None, None
-
-    with torch.no_grad():
-        observation_features = policy.actor.encoder.get_cached_image_features(observations)
-        next_observation_features = policy.actor.encoder.get_cached_image_features(next_observations)
-
-    return observation_features, next_observation_features
-
-
 def use_threads(cfg: TrainRLServerPipelineConfig) -> bool:
     return cfg.policy.concurrency.learner == "threads"
 
@@ -1093,19 +889,11 @@ def check_nan_in_transition(
     return nan_detected
 
 
-def push_actor_policy_to_queue(parameters_queue: Queue, policy: nn.Module):
+def push_actor_policy_to_queue(parameters_queue: Queue, algorithm: RLAlgorithm) -> None:
     logging.debug("[LEARNER] Pushing actor policy to the queue")
 
     # Create a dictionary to hold all the state dicts
-    state_dicts = {"policy": move_state_dict_to_device(policy.actor.state_dict(), device="cpu")}
-
-    # Add discrete critic if it exists
-    if hasattr(policy, "discrete_critic") and policy.discrete_critic is not None:
-        state_dicts["discrete_critic"] = move_state_dict_to_device(
-            policy.discrete_critic.state_dict(), device="cpu"
-        )
-        logging.debug("[LEARNER] Including discrete critic in state dict push")
-
+    state_dicts = algorithm.get_weights()
     state_bytes = state_to_bytes(state_dicts)
     parameters_queue.put(state_bytes)
 
@@ -1129,9 +917,8 @@ def process_transitions(
     transition_queue: Queue,
     replay_buffer: ReplayBuffer,
     offline_replay_buffer: ReplayBuffer,
-    device: str,
     dataset_repo_id: str | None,
-    shutdown_event: any,
+    shutdown_event: Any,  # Event
 ):
     """Process all available transitions from the queue.
 
@@ -1139,7 +926,6 @@ def process_transitions(
         transition_queue: Queue for receiving transitions from the actor
         replay_buffer: Replay buffer to add transitions to
         offline_replay_buffer: Offline replay buffer to add transitions to
-        device: Device to move transitions to
         dataset_repo_id: Repository ID for dataset
         shutdown_event: Event to signal shutdown
     """
@@ -1148,8 +934,6 @@ def process_transitions(
         transition_list = bytes_to_transitions(buffer=transition_list)
 
         for transition in transition_list:
-            transition = move_transition_to_device(transition=transition, device=device)
-
             # Skip transitions with NaN values
             if check_nan_in_transition(
                 observations=transition["state"],
@@ -1163,7 +947,7 @@ def process_transitions(
 
             # Add to offline buffer if it's an intervention
             if dataset_repo_id is not None and transition.get("complementary_info", {}).get(
-                TeleopEvents.IS_INTERVENTION
+                TeleopEvents.IS_INTERVENTION.value
             ):
                 offline_replay_buffer.add(**transition)
 
@@ -1172,7 +956,7 @@ def process_interaction_messages(
     interaction_message_queue: Queue,
     interaction_step_shift: int,
     wandb_logger: WandBLogger | None,
-    shutdown_event: any,
+    shutdown_event: Any,  # Event
 ) -> dict | None:
     """Process all available interaction messages from the queue.
 

src/lerobot/rl/learner_service.py

@@ -18,9 +18,21 @@
 import logging
 import time
 from multiprocessing import Event, Queue
+from typing import TYPE_CHECKING
 
-from lerobot.transport import services_pb2, services_pb2_grpc
-from lerobot.transport.utils import receive_bytes_in_chunks, send_bytes_in_chunks
+from lerobot.utils.import_utils import _grpc_available, require_package
+
+if TYPE_CHECKING or _grpc_available:
+    from lerobot.transport import services_pb2, services_pb2_grpc
+    from lerobot.transport.utils import receive_bytes_in_chunks, send_bytes_in_chunks
+
+    _ServicerBase = services_pb2_grpc.LearnerServiceServicer
+else:
+    services_pb2 = None
+    services_pb2_grpc = None
+    receive_bytes_in_chunks = None
+    send_bytes_in_chunks = None
+    _ServicerBase = object
 
 from .queue import get_last_item_from_queue
 
@@ -28,7 +40,7 @@ MAX_WORKERS = 3  # Stream parameters, send transitions and interactions
 SHUTDOWN_TIMEOUT = 10
 
 
-class LearnerService(services_pb2_grpc.LearnerServiceServicer):
+class LearnerService(_ServicerBase):
     """
     Implementation of the LearnerService gRPC service
     This service is used to send parameters to the Actor and receive transitions and interactions from the Actor
@@ -44,6 +56,7 @@ class LearnerService(services_pb2_grpc.LearnerServiceServicer):
         interaction_message_queue: Queue,
         queue_get_timeout: float = 0.001,
     ):
+        require_package("grpcio", extra="hilserl", import_name="grpc")
         self.shutdown_event = shutdown_event
         self.parameters_queue = parameters_queue
         self.seconds_between_pushes = seconds_between_pushes

src/lerobot/rl/train_rl.py

@@ -0,0 +1,50 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Top-level pipeline config for distributed RL training (actor / learner)."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from lerobot.configs.default import DatasetConfig
+from lerobot.configs.train import TrainPipelineConfig
+
+from .algorithms.configs import RLAlgorithmConfig
+from .algorithms.factory import make_algorithm_config
+from .algorithms.sac import SACAlgorithmConfig  # noqa: F401
+
+
+@dataclass(kw_only=True)
+class TrainRLServerPipelineConfig(TrainPipelineConfig):
+    # NOTE: In RL, we don't need an offline dataset
+    # TODO: Make `TrainPipelineConfig.dataset` optional
+    dataset: DatasetConfig | None = None  # type: ignore[assignment] # because the parent class has made it's type non-optional
+
+    # Algorithm config.
+    algorithm: RLAlgorithmConfig | None = None
+
+    # Data mixer strategy name. Currently supports "online_offline".
+    mixer: str = "online_offline"
+    # Fraction sampled from online replay when using OnlineOfflineMixer.
+    online_ratio: float = 0.5
+
+    def validate(self) -> None:
+        super().validate()
+
+        if self.algorithm is None:
+            self.algorithm = make_algorithm_config("sac")
+
+        if getattr(self.algorithm, "policy_config", None) is None:
+            self.algorithm.policy_config = self.policy

src/lerobot/rl/trainer.py

@@ -0,0 +1,101 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from collections.abc import Iterator
+from typing import Any
+
+from lerobot.types import BatchType
+
+from .algorithms.base import RLAlgorithm
+from .algorithms.configs import TrainingStats
+from .data_sources.data_mixer import DataMixer
+
+
+class RLTrainer:
+    """Unified training step orchestrator.
+
+    Holds the algorithm, a DataMixer, and an optional preprocessor.
+    """
+
+    def __init__(
+        self,
+        algorithm: RLAlgorithm,
+        data_mixer: DataMixer,
+        batch_size: int,
+        *,
+        preprocessor: Any | None = None,
+    ):
+        self.algorithm = algorithm
+        self.data_mixer = data_mixer
+        self.batch_size = batch_size
+        self._preprocessor = preprocessor
+
+        self._iterator: Iterator[BatchType] | None = None
+
+        self.algorithm.make_optimizers_and_scheduler()
+
+    def _build_data_iterator(self) -> Iterator[BatchType]:
+        """Create a fresh algorithm-configured iterator (optionally preprocessed)."""
+        raw = self.algorithm.configure_data_iterator(
+            data_mixer=self.data_mixer,
+            batch_size=self.batch_size,
+        )
+        if self._preprocessor is not None:
+            return _PreprocessedIterator(raw, self._preprocessor)
+        return raw
+
+    def reset_data_iterator(self) -> None:
+        """Discard the current iterator so it will be rebuilt lazily next step."""
+        self._iterator = None
+
+    def set_data_mixer(self, data_mixer: DataMixer, *, reset: bool = True) -> None:
+        """Swap the active data mixer, optionally resetting the iterator."""
+        self.data_mixer = data_mixer
+        if reset:
+            self.reset_data_iterator()
+
+    def training_step(self) -> TrainingStats:
+        """Run one training step (algorithm-agnostic)."""
+        if self._iterator is None:
+            self._iterator = self._build_data_iterator()
+        return self.algorithm.update(self._iterator)
+
+
+def preprocess_rl_batch(preprocessor: Any, batch: BatchType) -> BatchType:
+    """Apply policy preprocessing to RL observations only."""
+    observations = batch["state"]
+    next_observations = batch["next_state"]
+    batch["state"] = preprocessor.process_observation(observations)
+    batch["next_state"] = preprocessor.process_observation(next_observations)
+
+    return batch
+
+
+class _PreprocessedIterator:
+    """Iterator wrapper that preprocesses each sampled RL batch."""
+
+    __slots__ = ("_raw", "_preprocessor")
+
+    def __init__(self, raw_iterator: Iterator[BatchType], preprocessor: Any) -> None:
+        self._raw = raw_iterator
+        self._preprocessor = preprocessor
+
+    def __iter__(self) -> _PreprocessedIterator:
+        return self
+
+    def __next__(self) -> BatchType:
+        batch = next(self._raw)
+        return preprocess_rl_batch(self._preprocessor, batch)

src/lerobot/robots/so_follower/robot_kinematic_processor.py

@@ -353,7 +353,8 @@ class GripperVelocityToJoint(RobotActionProcessorStep):
         speed_factor: A scaling factor to convert the normalized velocity command to a position change.
         clip_min: The minimum allowed gripper joint position.
         clip_max: The maximum allowed gripper joint position.
-        discrete_gripper: If True, treat the input action as discrete (0: open, 1: close, 2: stay).
+        discrete_gripper: If True, interpret the input as a discrete class index
+            {0 = close, 1 = stay, 2 = open}, matching `GamepadTeleop.GripperAction`.
     """
 
     speed_factor: float = 20.0
@@ -377,10 +378,10 @@ class GripperVelocityToJoint(RobotActionProcessorStep):
             raise ValueError("Joints observation is require for computing robot kinematics")
 
         if self.discrete_gripper:
-            # Discrete gripper actions are in [0, 1, 2]
-            # 0: open, 1: close, 2: stay
-            # We need to shift them to [-1, 0, 1] and then scale them to clip_max
-            gripper_vel = (gripper_vel - 1) * self.clip_max
+            # Map discrete command {0=close, 1=stay, 2=open} -> signed velocity.
+            # Negation accounts for SO100 sign (joint position increases on close).
+            #   0 -> +clip_max (close), 1 -> 0 (stay), 2 -> -clip_max (open)
+            gripper_vel = -(gripper_vel - 1) * self.clip_max
 
         # Compute desired gripper position
         delta = gripper_vel * float(self.speed_factor)

src/lerobot/teleoperators/keyboard/teleop_keyboard.py

@@ -104,11 +104,14 @@ class KeyboardTeleop(Teleoperator):
 
     def _on_press(self, key):
         if hasattr(key, "char"):
-            self.event_queue.put((key.char, True))
+            key = key.char
+        self.event_queue.put((key, True))
 
     def _on_release(self, key):
         if hasattr(key, "char"):
-            self.event_queue.put((key.char, False))
+            key = key.char
+        self.event_queue.put((key, False))
+
         if key == keyboard.Key.esc:
             logging.info("ESC pressed, disconnecting.")
             self.disconnect()
@@ -204,8 +207,6 @@ class KeyboardEndEffectorTeleop(KeyboardTeleop):
                 # this is useful for retrieving other events like interventions for RL, episode success, etc.
                 self.misc_keys_queue.put(key)
 
-        self.current_pressed.clear()
-
         action_dict = {
             "delta_x": delta_x,
             "delta_y": delta_y,
@@ -256,6 +257,8 @@ class KeyboardEndEffectorTeleop(KeyboardTeleop):
         ]
         is_intervention = any(self.current_pressed.get(key, False) for key in movement_keys)
 
+        self.current_pressed.clear()
+
         # Check for episode control commands from misc_keys_queue
         terminate_episode = False
         success = False

src/lerobot/templates/lerobot_modelcard_template.md

@@ -39,8 +39,8 @@ For more details, see the [Physical Intelligence π₀ blog post](https://www.ph
 π₀.₅ represents a significant evolution from π₀, developed by Physical Intelligence to address a big challenge in robotics: open-world generalization. While robots can perform impressive tasks in controlled environments, π₀.₅ is designed to generalize to entirely new environments and situations that were never seen during training.
 
 For more details, see the [Physical Intelligence π₀.₅ blog post](https://www.physicalintelligence.company/blog/pi05).
-{% elif model_name == "sac" %}
-[Soft Actor-Critic (SAC)](https://huggingface.co/papers/1801.01290) is an entropy-regularised actor-critic algorithm offering stable, sample-efficient learning in continuous-control environments.
+{% elif model_name == "gaussian_actor" %}
+This is a Gaussian Actor policy (Gaussian policy with a tanh squash) — the policy-side component used by [Soft Actor-Critic (SAC)](https://huggingface.co/papers/1801.01290) and related maximum-entropy continuous-control algorithms.
 {% elif model_name == "reward_classifier" %}
 A reward classifier is a lightweight neural network that scores observations or trajectories for task success, providing a learned reward signal or offline evaluation when explicit rewards are unavailable.
 {% else %}

src/lerobot/types.py

@@ -40,6 +40,7 @@ PolicyAction = torch.Tensor
 RobotAction = dict[str, Any]
 EnvAction = np.ndarray
 RobotObservation = dict[str, Any]
+BatchType = dict[str, Any]
 
 
 EnvTransition = TypedDict(

src/lerobot/utils/import_utils.py

@@ -132,6 +132,7 @@ _faker_available = is_package_available("faker")
 _pynput_available = is_package_available("pynput")
 _pygame_available = is_package_available("pygame")
 _qwen_vl_utils_available = is_package_available("qwen-vl-utils", import_name="qwen_vl_utils")
+_grpc_available = is_package_available("grpcio", import_name="grpc")
 _wallx_deps_available = (
     _transformers_available and _peft_available and _torchdiffeq_available and _qwen_vl_utils_available
 )

tests/policies/test_gaussian_actor_config.py

@@ -17,19 +17,19 @@
 import pytest
 
 from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
-from lerobot.policies.sac.configuration_sac import (
+from lerobot.policies.gaussian_actor.configuration_gaussian_actor import (
     ActorLearnerConfig,
     ActorNetworkConfig,
     ConcurrencyConfig,
     CriticNetworkConfig,
+    GaussianActorConfig,
     PolicyConfig,
-    SACConfig,
 )
 from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_STATE
 
 
-def test_sac_config_default_initialization():
-    config = SACConfig()
+def test_gaussian_actor_config_default_initialization():
+    config = GaussianActorConfig()
 
     assert config.normalization_mapping == {
         "VISUAL": NormalizationMode.MEAN_STD,
@@ -55,9 +55,6 @@ def test_sac_config_default_initialization():
     # Basic parameters
     assert config.device == "cpu"
     assert config.storage_device == "cpu"
-    assert config.discount == 0.99
-    assert config.temperature_init == 1.0
-    assert config.num_critics == 2
 
     # Architecture specifics
     assert config.vision_encoder_name is None
@@ -66,6 +63,8 @@ def test_sac_config_default_initialization():
     assert config.shared_encoder is True
     assert config.num_discrete_actions is None
     assert config.image_embedding_pooling_dim == 8
+    assert config.state_encoder_hidden_dim == 256
+    assert config.latent_dim == 256
 
     # Training parameters
     assert config.online_steps == 1000000
@@ -73,20 +72,6 @@ def test_sac_config_default_initialization():
     assert config.offline_buffer_capacity == 100000
     assert config.async_prefetch is False
     assert config.online_step_before_learning == 100
-    assert config.policy_update_freq == 1
-
-    # SAC algorithm parameters
-    assert config.num_subsample_critics is None
-    assert config.critic_lr == 3e-4
-    assert config.actor_lr == 3e-4
-    assert config.temperature_lr == 3e-4
-    assert config.critic_target_update_weight == 0.005
-    assert config.utd_ratio == 1
-    assert config.state_encoder_hidden_dim == 256
-    assert config.latent_dim == 256
-    assert config.target_entropy is None
-    assert config.use_backup_entropy is True
-    assert config.grad_clip_norm == 40.0
 
     # Dataset stats defaults
     expected_dataset_stats = {
@@ -105,11 +90,6 @@ def test_sac_config_default_initialization():
     }
     assert config.dataset_stats == expected_dataset_stats
 
-    # Critic network configuration
-    assert config.critic_network_kwargs.hidden_dims == [256, 256]
-    assert config.critic_network_kwargs.activate_final is True
-    assert config.critic_network_kwargs.final_activation is None
-
     # Actor network configuration
     assert config.actor_network_kwargs.hidden_dims == [256, 256]
     assert config.actor_network_kwargs.activate_final is True
@@ -135,7 +115,6 @@ def test_sac_config_default_initialization():
     assert config.concurrency.learner == "threads"
 
     assert isinstance(config.actor_network_kwargs, ActorNetworkConfig)
-    assert isinstance(config.critic_network_kwargs, CriticNetworkConfig)
     assert isinstance(config.policy_kwargs, PolicyConfig)
     assert isinstance(config.actor_learner_config, ActorLearnerConfig)
     assert isinstance(config.concurrency, ConcurrencyConfig)
@@ -175,22 +154,22 @@ def test_concurrency_config():
     assert config.learner == "threads"
 
 
-def test_sac_config_custom_initialization():
-    config = SACConfig(
+def test_gaussian_actor_config_custom_initialization():
+    config = GaussianActorConfig(
         device="cpu",
-        discount=0.95,
-        temperature_init=0.5,
-        num_critics=3,
+        latent_dim=128,
+        state_encoder_hidden_dim=128,
+        num_discrete_actions=3,
     )
 
     assert config.device == "cpu"
-    assert config.discount == 0.95
-    assert config.temperature_init == 0.5
-    assert config.num_critics == 3
+    assert config.latent_dim == 128
+    assert config.state_encoder_hidden_dim == 128
+    assert config.num_discrete_actions == 3
 
 
 def test_validate_features():
-    config = SACConfig(
+    config = GaussianActorConfig(
         input_features={OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(10,))},
         output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(3,))},
     )
@@ -198,7 +177,7 @@ def test_validate_features():
 
 
 def test_validate_features_missing_observation():
-    config = SACConfig(
+    config = GaussianActorConfig(
         input_features={"wrong_key": PolicyFeature(type=FeatureType.STATE, shape=(10,))},
         output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(3,))},
     )
@@ -209,7 +188,7 @@ def test_validate_features_missing_observation():
 
 
 def test_validate_features_missing_action():
-    config = SACConfig(
+    config = GaussianActorConfig(
         input_features={OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(10,))},
         output_features={"wrong_key": PolicyFeature(type=FeatureType.ACTION, shape=(3,))},
     )

tests/policies/test_gaussian_actor_policy.py

@@ -0,0 +1,528 @@
+# !/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pytest
+
+pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
+
+import torch  # noqa: E402
+from torch import Tensor, nn  # noqa: E402
+
+from lerobot.configs.types import FeatureType, PolicyFeature  # noqa: E402
+from lerobot.policies.gaussian_actor.configuration_gaussian_actor import GaussianActorConfig  # noqa: E402
+from lerobot.policies.gaussian_actor.modeling_gaussian_actor import MLP, GaussianActorPolicy  # noqa: E402
+from lerobot.rl.algorithms.sac import SACAlgorithm, SACAlgorithmConfig  # noqa: E402
+from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_STATE  # noqa: E402
+from lerobot.utils.random_utils import seeded_context, set_seed  # noqa: E402
+
+try:
+    import transformers  # noqa: F401
+
+    TRANSFORMERS_AVAILABLE = True
+except ImportError:
+    TRANSFORMERS_AVAILABLE = False
+
+
+@pytest.fixture(autouse=True)
+def set_random_seed():
+    seed = 42
+    set_seed(seed)
+
+
+def test_mlp_with_default_args():
+    mlp = MLP(input_dim=10, hidden_dims=[256, 256])
+
+    x = torch.randn(10)
+    y = mlp(x)
+    assert y.shape == (256,)
+
+
+def test_mlp_with_batch_dim():
+    mlp = MLP(input_dim=10, hidden_dims=[256, 256])
+    x = torch.randn(2, 10)
+    y = mlp(x)
+    assert y.shape == (2, 256)
+
+
+def test_forward_with_empty_hidden_dims():
+    mlp = MLP(input_dim=10, hidden_dims=[])
+    x = torch.randn(1, 10)
+    assert mlp(x).shape == (1, 10)
+
+
+def test_mlp_with_dropout():
+    mlp = MLP(input_dim=10, hidden_dims=[256, 256, 11], dropout_rate=0.1)
+    x = torch.randn(1, 10)
+    y = mlp(x)
+    assert y.shape == (1, 11)
+
+    drop_out_layers_count = sum(isinstance(layer, nn.Dropout) for layer in mlp.net)
+    assert drop_out_layers_count == 2
+
+
+def test_mlp_with_custom_final_activation():
+    mlp = MLP(input_dim=10, hidden_dims=[256, 256], final_activation=torch.nn.Tanh())
+    x = torch.randn(1, 10)
+    y = mlp(x)
+    assert y.shape == (1, 256)
+    assert (y >= -1).all() and (y <= 1).all()
+
+
+def test_gaussian_actor_policy_with_default_args():
+    with pytest.raises(ValueError, match="should be an instance of class `PreTrainedConfig`"):
+        GaussianActorPolicy()
+
+
+def create_dummy_state(batch_size: int, state_dim: int = 10) -> Tensor:
+    return {
+        OBS_STATE: torch.randn(batch_size, state_dim),
+    }
+
+
+def create_dummy_with_visual_input(batch_size: int, state_dim: int = 10) -> Tensor:
+    return {
+        OBS_IMAGE: torch.randn(batch_size, 3, 84, 84),
+        OBS_STATE: torch.randn(batch_size, state_dim),
+    }
+
+
+def create_dummy_action(batch_size: int, action_dim: int = 10) -> Tensor:
+    return torch.randn(batch_size, action_dim)
+
+
+def create_default_train_batch(
+    batch_size: int = 8, state_dim: int = 10, action_dim: int = 10
+) -> dict[str, Tensor]:
+    return {
+        ACTION: create_dummy_action(batch_size, action_dim),
+        "reward": torch.randn(batch_size),
+        "state": create_dummy_state(batch_size, state_dim),
+        "next_state": create_dummy_state(batch_size, state_dim),
+        "done": torch.randn(batch_size),
+    }
+
+
+def create_train_batch_with_visual_input(
+    batch_size: int = 8, state_dim: int = 10, action_dim: int = 10
+) -> dict[str, Tensor]:
+    return {
+        ACTION: create_dummy_action(batch_size, action_dim),
+        "reward": torch.randn(batch_size),
+        "state": create_dummy_with_visual_input(batch_size, state_dim),
+        "next_state": create_dummy_with_visual_input(batch_size, state_dim),
+        "done": torch.randn(batch_size),
+    }
+
+
+def create_observation_batch(batch_size: int = 8, state_dim: int = 10) -> dict[str, Tensor]:
+    return {
+        OBS_STATE: torch.randn(batch_size, state_dim),
+    }
+
+
+def create_observation_batch_with_visual_input(batch_size: int = 8, state_dim: int = 10) -> dict[str, Tensor]:
+    return {
+        OBS_STATE: torch.randn(batch_size, state_dim),
+        OBS_IMAGE: torch.randn(batch_size, 3, 84, 84),
+    }
+
+
+def create_default_config(
+    state_dim: int, continuous_action_dim: int, has_discrete_action: bool = False
+) -> GaussianActorConfig:
+    action_dim = continuous_action_dim
+    if has_discrete_action:
+        action_dim += 1
+
+    config = GaussianActorConfig(
+        input_features={OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(state_dim,))},
+        output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(continuous_action_dim,))},
+        dataset_stats={
+            OBS_STATE: {
+                "min": [0.0] * state_dim,
+                "max": [1.0] * state_dim,
+            },
+            ACTION: {
+                "min": [0.0] * continuous_action_dim,
+                "max": [1.0] * continuous_action_dim,
+            },
+        },
+    )
+    config.validate_features()
+    return config
+
+
+def create_config_with_visual_input(
+    state_dim: int, continuous_action_dim: int, has_discrete_action: bool = False
+) -> GaussianActorConfig:
+    config = create_default_config(
+        state_dim=state_dim,
+        continuous_action_dim=continuous_action_dim,
+        has_discrete_action=has_discrete_action,
+    )
+    config.input_features[OBS_IMAGE] = PolicyFeature(type=FeatureType.VISUAL, shape=(3, 84, 84))
+    config.dataset_stats[OBS_IMAGE] = {
+        "mean": torch.randn(3, 1, 1),
+        "std": torch.randn(3, 1, 1),
+    }
+
+    config.state_encoder_hidden_dim = 32
+    config.latent_dim = 32
+
+    config.validate_features()
+    return config
+
+
+def _make_algorithm(config: GaussianActorConfig) -> tuple[SACAlgorithm, GaussianActorPolicy]:
+    """Helper to create policy + algorithm pair for tests that need critics."""
+    policy = GaussianActorPolicy(config=config)
+    policy.train()
+    algo_config = SACAlgorithmConfig.from_policy_config(config)
+    algorithm = SACAlgorithm(policy=policy, config=algo_config)
+    algorithm.make_optimizers_and_scheduler()
+    return algorithm, policy
+
+
+@pytest.mark.parametrize("batch_size,state_dim,action_dim", [(2, 6, 6), (1, 10, 10)])
+def test_gaussian_actor_policy_select_action(batch_size: int, state_dim: int, action_dim: int):
+    config = create_default_config(state_dim=state_dim, continuous_action_dim=action_dim)
+    policy = GaussianActorPolicy(config=config)
+    policy.eval()
+
+    with torch.no_grad():
+        observation_batch = create_observation_batch(batch_size=batch_size, state_dim=state_dim)
+        selected_action = policy.select_action(observation_batch)
+        # squeeze(0) removes batch dim when batch_size==1
+        assert selected_action.shape[-1] == action_dim
+
+
+def test_gaussian_actor_policy_select_action_with_discrete():
+    """select_action should return continuous + discrete actions."""
+    config = create_default_config(state_dim=10, continuous_action_dim=6)
+    config.num_discrete_actions = 3
+    policy = GaussianActorPolicy(config=config)
+    policy.eval()
+
+    with torch.no_grad():
+        observation_batch = create_observation_batch(batch_size=1, state_dim=10)
+        # Squeeze to unbatched (single observation)
+        observation_batch = {k: v.squeeze(0) for k, v in observation_batch.items()}
+        selected_action = policy.select_action(observation_batch)
+        assert selected_action.shape[-1] == 7  # 6 continuous + 1 discrete
+
+
+@pytest.mark.parametrize("batch_size,state_dim,action_dim", [(2, 6, 6), (1, 10, 10)])
+def test_gaussian_actor_policy_forward(batch_size: int, state_dim: int, action_dim: int):
+    config = create_default_config(state_dim=state_dim, continuous_action_dim=action_dim)
+    policy = GaussianActorPolicy(config=config)
+    policy.eval()
+
+    batch = create_default_train_batch(batch_size=batch_size, action_dim=action_dim, state_dim=state_dim)
+    with torch.no_grad():
+        output = policy.forward(batch)
+        assert "action" in output
+        assert "log_prob" in output
+        assert "action_mean" in output
+        assert output["action"].shape == (batch_size, action_dim)
+
+
+@pytest.mark.parametrize("batch_size,state_dim,action_dim", [(2, 6, 6), (1, 10, 10)])
+def test_gaussian_actor_training_through_sac(batch_size: int, state_dim: int, action_dim: int):
+    config = create_default_config(state_dim=state_dim, continuous_action_dim=action_dim)
+    algorithm, policy = _make_algorithm(config)
+
+    batch = create_default_train_batch(batch_size=batch_size, action_dim=action_dim, state_dim=state_dim)
+    forward_batch = algorithm._prepare_forward_batch(batch)
+
+    critic_loss = algorithm._compute_loss_critic(forward_batch)
+    assert critic_loss.item() is not None
+    assert critic_loss.shape == ()
+    algorithm.optimizers["critic"].zero_grad()
+    critic_loss.backward()
+    algorithm.optimizers["critic"].step()
+
+    actor_loss = algorithm._compute_loss_actor(forward_batch)
+    assert actor_loss.item() is not None
+    assert actor_loss.shape == ()
+    algorithm.optimizers["actor"].zero_grad()
+    actor_loss.backward()
+    algorithm.optimizers["actor"].step()
+
+    temp_loss = algorithm._compute_loss_temperature(forward_batch)
+    assert temp_loss.item() is not None
+    assert temp_loss.shape == ()
+    algorithm.optimizers["temperature"].zero_grad()
+    temp_loss.backward()
+    algorithm.optimizers["temperature"].step()
+
+
+@pytest.mark.parametrize("batch_size,state_dim,action_dim", [(2, 6, 6), (1, 10, 10)])
+def test_gaussian_actor_training_with_visual_input(batch_size: int, state_dim: int, action_dim: int):
+    config = create_config_with_visual_input(state_dim=state_dim, continuous_action_dim=action_dim)
+    algorithm, policy = _make_algorithm(config)
+
+    batch = create_train_batch_with_visual_input(
+        batch_size=batch_size, state_dim=state_dim, action_dim=action_dim
+    )
+    forward_batch = algorithm._prepare_forward_batch(batch)
+
+    critic_loss = algorithm._compute_loss_critic(forward_batch)
+    assert critic_loss.item() is not None
+    assert critic_loss.shape == ()
+    algorithm.optimizers["critic"].zero_grad()
+    critic_loss.backward()
+    algorithm.optimizers["critic"].step()
+
+    actor_loss = algorithm._compute_loss_actor(forward_batch)
+    assert actor_loss.item() is not None
+    assert actor_loss.shape == ()
+    algorithm.optimizers["actor"].zero_grad()
+    actor_loss.backward()
+    algorithm.optimizers["actor"].step()
+
+    policy.eval()
+    with torch.no_grad():
+        observation_batch = create_observation_batch_with_visual_input(
+            batch_size=batch_size, state_dim=state_dim
+        )
+        selected_action = policy.select_action(observation_batch)
+        assert selected_action.shape[-1] == action_dim
+
+
+@pytest.mark.parametrize(
+    "batch_size,state_dim,action_dim,vision_encoder_name",
+    [(1, 6, 6, "lerobot/resnet10"), (1, 6, 6, "facebook/convnext-base-224")],
+)
+@pytest.mark.skipif(not TRANSFORMERS_AVAILABLE, reason="Transformers are not installed")
+def test_gaussian_actor_policy_with_pretrained_encoder(
+    batch_size: int, state_dim: int, action_dim: int, vision_encoder_name: str
+):
+    config = create_config_with_visual_input(state_dim=state_dim, continuous_action_dim=action_dim)
+    config.vision_encoder_name = vision_encoder_name
+    algorithm, policy = _make_algorithm(config)
+
+    batch = create_train_batch_with_visual_input(
+        batch_size=batch_size, state_dim=state_dim, action_dim=action_dim
+    )
+    forward_batch = algorithm._prepare_forward_batch(batch)
+
+    critic_loss = algorithm._compute_loss_critic(forward_batch)
+    assert critic_loss.item() is not None
+    assert critic_loss.shape == ()
+    algorithm.optimizers["critic"].zero_grad()
+    critic_loss.backward()
+    algorithm.optimizers["critic"].step()
+
+    actor_loss = algorithm._compute_loss_actor(forward_batch)
+    assert actor_loss.item() is not None
+    assert actor_loss.shape == ()
+
+
+def test_gaussian_actor_training_with_shared_encoder():
+    batch_size = 2
+    action_dim = 10
+    state_dim = 10
+    config = create_config_with_visual_input(state_dim=state_dim, continuous_action_dim=action_dim)
+    config.shared_encoder = True
+
+    algorithm, policy = _make_algorithm(config)
+
+    batch = create_train_batch_with_visual_input(
+        batch_size=batch_size, state_dim=state_dim, action_dim=action_dim
+    )
+    forward_batch = algorithm._prepare_forward_batch(batch)
+
+    critic_loss = algorithm._compute_loss_critic(forward_batch)
+    assert critic_loss.shape == ()
+    algorithm.optimizers["critic"].zero_grad()
+    critic_loss.backward()
+    algorithm.optimizers["critic"].step()
+
+    actor_loss = algorithm._compute_loss_actor(forward_batch)
+    assert actor_loss.shape == ()
+    algorithm.optimizers["actor"].zero_grad()
+    actor_loss.backward()
+    algorithm.optimizers["actor"].step()
+
+
+def test_gaussian_actor_training_with_discrete_critic():
+    batch_size = 2
+    continuous_action_dim = 9
+    full_action_dim = continuous_action_dim + 1
+    state_dim = 10
+    config = create_config_with_visual_input(
+        state_dim=state_dim, continuous_action_dim=continuous_action_dim, has_discrete_action=True
+    )
+    config.num_discrete_actions = 5
+
+    algorithm, policy = _make_algorithm(config)
+
+    batch = create_train_batch_with_visual_input(
+        batch_size=batch_size, state_dim=state_dim, action_dim=full_action_dim
+    )
+    forward_batch = algorithm._prepare_forward_batch(batch)
+
+    critic_loss = algorithm._compute_loss_critic(forward_batch)
+    assert critic_loss.shape == ()
+    algorithm.optimizers["critic"].zero_grad()
+    critic_loss.backward()
+    algorithm.optimizers["critic"].step()
+
+    discrete_critic_loss = algorithm._compute_loss_discrete_critic(forward_batch)
+    assert discrete_critic_loss.shape == ()
+    algorithm.optimizers["discrete_critic"].zero_grad()
+    discrete_critic_loss.backward()
+    algorithm.optimizers["discrete_critic"].step()
+
+    actor_loss = algorithm._compute_loss_actor(forward_batch)
+    assert actor_loss.shape == ()
+    algorithm.optimizers["actor"].zero_grad()
+    actor_loss.backward()
+    algorithm.optimizers["actor"].step()
+
+    policy.eval()
+    with torch.no_grad():
+        observation_batch = create_observation_batch_with_visual_input(
+            batch_size=batch_size, state_dim=state_dim
+        )
+        # Policy.select_action now handles both continuous + discrete
+        selected_action = policy.select_action({k: v.squeeze(0) for k, v in observation_batch.items()})
+        assert selected_action.shape[-1] == continuous_action_dim + 1
+
+
+def test_sac_algorithm_target_entropy():
+    """Target entropy is an SAC hyperparameter and lives on the algorithm."""
+    config = create_default_config(continuous_action_dim=10, state_dim=10)
+    algorithm, _ = _make_algorithm(config)
+    assert algorithm.target_entropy == -5.0
+
+
+def test_sac_algorithm_target_entropy_with_discrete_action():
+    config = create_config_with_visual_input(state_dim=10, continuous_action_dim=6, has_discrete_action=True)
+    config.num_discrete_actions = 5
+    algorithm, _ = _make_algorithm(config)
+    assert algorithm.target_entropy == -3.5
+
+
+def test_sac_algorithm_temperature():
+    import math
+
+    config = create_default_config(continuous_action_dim=10, state_dim=10)
+    algo_config = SACAlgorithmConfig.from_policy_config(config)
+    policy = GaussianActorPolicy(config=config)
+    algorithm = SACAlgorithm(policy=policy, config=algo_config)
+
+    assert algorithm.temperature == pytest.approx(1.0)
+    algorithm.log_alpha.data = torch.tensor([math.log(0.1)])
+    assert algorithm.temperature == pytest.approx(0.1)
+
+
+def test_sac_algorithm_update_target_network():
+    config = create_default_config(state_dim=10, continuous_action_dim=6)
+    algo_config = SACAlgorithmConfig.from_policy_config(config)
+    algo_config.critic_target_update_weight = 1.0
+    policy = GaussianActorPolicy(config=config)
+    algorithm = SACAlgorithm(policy=policy, config=algo_config)
+
+    for p in algorithm.critic_ensemble.parameters():
+        p.data = torch.ones_like(p.data)
+
+    algorithm._update_target_networks()
+    for p in algorithm.critic_target.parameters():
+        assert torch.allclose(p.data, torch.ones_like(p.data))
+
+
+@pytest.mark.parametrize("num_critics", [1, 3])
+def test_sac_algorithm_with_critics_number_of_heads(num_critics: int):
+    batch_size = 2
+    action_dim = 10
+    state_dim = 10
+    config = create_config_with_visual_input(state_dim=state_dim, continuous_action_dim=action_dim)
+
+    policy = GaussianActorPolicy(config=config)
+    policy.train()
+    algo_config = SACAlgorithmConfig.from_policy_config(config)
+    algo_config.num_critics = num_critics
+    algorithm = SACAlgorithm(policy=policy, config=algo_config)
+    algorithm.make_optimizers_and_scheduler()
+
+    assert len(algorithm.critic_ensemble.critics) == num_critics
+
+    batch = create_train_batch_with_visual_input(
+        batch_size=batch_size, state_dim=state_dim, action_dim=action_dim
+    )
+    forward_batch = algorithm._prepare_forward_batch(batch)
+
+    critic_loss = algorithm._compute_loss_critic(forward_batch)
+    assert critic_loss.shape == ()
+    algorithm.optimizers["critic"].zero_grad()
+    critic_loss.backward()
+    algorithm.optimizers["critic"].step()
+
+
+def test_gaussian_actor_policy_save_and_load(tmp_path):
+    """Test that the policy can be saved and loaded from pretrained."""
+    root = tmp_path / "test_gaussian_actor_save_and_load"
+
+    state_dim = 10
+    action_dim = 10
+    batch_size = 2
+
+    config = create_default_config(state_dim=state_dim, continuous_action_dim=action_dim)
+    policy = GaussianActorPolicy(config=config)
+    policy.eval()
+    policy.save_pretrained(root)
+    loaded_policy = GaussianActorPolicy.from_pretrained(root, config=config)
+    loaded_policy.eval()
+
+    assert policy.state_dict().keys() == loaded_policy.state_dict().keys()
+    for k in policy.state_dict():
+        assert torch.allclose(policy.state_dict()[k], loaded_policy.state_dict()[k], atol=1e-6)
+
+    with torch.no_grad():
+        with seeded_context(12):
+            observation_batch = create_observation_batch(batch_size=batch_size, state_dim=state_dim)
+            actions = policy.select_action(observation_batch)
+
+        with seeded_context(12):
+            loaded_observation_batch = create_observation_batch(batch_size=batch_size, state_dim=state_dim)
+            loaded_actions = loaded_policy.select_action(loaded_observation_batch)
+
+        assert torch.allclose(actions, loaded_actions)
+
+
+def test_gaussian_actor_policy_save_and_load_with_discrete_critic(tmp_path):
+    """Discrete critic should be saved/loaded as part of the policy."""
+    root = tmp_path / "test_gaussian_actor_save_and_load_discrete"
+
+    state_dim = 10
+    action_dim = 6
+
+    config = create_default_config(state_dim=state_dim, continuous_action_dim=action_dim)
+    config.num_discrete_actions = 3
+    policy = GaussianActorPolicy(config=config)
+    policy.eval()
+    policy.save_pretrained(root)
+
+    loaded_policy = GaussianActorPolicy.from_pretrained(root, config=config)
+    loaded_policy.eval()
+
+    assert loaded_policy.discrete_critic is not None
+    dc_keys = [k for k in loaded_policy.state_dict() if k.startswith("discrete_critic.")]
+    assert len(dc_keys) > 0
+
+    for k in policy.state_dict():
+        assert torch.allclose(policy.state_dict()[k], loaded_policy.state_dict()[k], atol=1e-6)

tests/policies/test_sac_policy.py

@@ -1,546 +0,0 @@
-# !/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import math
-
-import pytest
-import torch
-from torch import Tensor, nn
-
-from lerobot.configs.types import FeatureType, PolicyFeature
-from lerobot.policies.sac.configuration_sac import SACConfig
-from lerobot.policies.sac.modeling_sac import MLP, SACPolicy
-from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_STATE
-from lerobot.utils.random_utils import seeded_context, set_seed
-
-try:
-    import transformers  # noqa: F401
-
-    TRANSFORMERS_AVAILABLE = True
-except ImportError:
-    TRANSFORMERS_AVAILABLE = False
-
-
-@pytest.fixture(autouse=True)
-def set_random_seed():
-    seed = 42
-    set_seed(seed)
-
-
-def test_mlp_with_default_args():
-    mlp = MLP(input_dim=10, hidden_dims=[256, 256])
-
-    x = torch.randn(10)
-    y = mlp(x)
-    assert y.shape == (256,)
-
-
-def test_mlp_with_batch_dim():
-    mlp = MLP(input_dim=10, hidden_dims=[256, 256])
-    x = torch.randn(2, 10)
-    y = mlp(x)
-    assert y.shape == (2, 256)
-
-
-def test_forward_with_empty_hidden_dims():
-    mlp = MLP(input_dim=10, hidden_dims=[])
-    x = torch.randn(1, 10)
-    assert mlp(x).shape == (1, 10)
-
-
-def test_mlp_with_dropout():
-    mlp = MLP(input_dim=10, hidden_dims=[256, 256, 11], dropout_rate=0.1)
-    x = torch.randn(1, 10)
-    y = mlp(x)
-    assert y.shape == (1, 11)
-
-    drop_out_layers_count = sum(isinstance(layer, nn.Dropout) for layer in mlp.net)
-    assert drop_out_layers_count == 2
-
-
-def test_mlp_with_custom_final_activation():
-    mlp = MLP(input_dim=10, hidden_dims=[256, 256], final_activation=torch.nn.Tanh())
-    x = torch.randn(1, 10)
-    y = mlp(x)
-    assert y.shape == (1, 256)
-    assert (y >= -1).all() and (y <= 1).all()
-
-
-def test_sac_policy_with_default_args():
-    with pytest.raises(ValueError, match="should be an instance of class `PreTrainedConfig`"):
-        SACPolicy()
-
-
-def create_dummy_state(batch_size: int, state_dim: int = 10) -> Tensor:
-    return {
-        OBS_STATE: torch.randn(batch_size, state_dim),
-    }
-
-
-def create_dummy_with_visual_input(batch_size: int, state_dim: int = 10) -> Tensor:
-    return {
-        OBS_IMAGE: torch.randn(batch_size, 3, 84, 84),
-        OBS_STATE: torch.randn(batch_size, state_dim),
-    }
-
-
-def create_dummy_action(batch_size: int, action_dim: int = 10) -> Tensor:
-    return torch.randn(batch_size, action_dim)
-
-
-def create_default_train_batch(
-    batch_size: int = 8, state_dim: int = 10, action_dim: int = 10
-) -> dict[str, Tensor]:
-    return {
-        ACTION: create_dummy_action(batch_size, action_dim),
-        "reward": torch.randn(batch_size),
-        "state": create_dummy_state(batch_size, state_dim),
-        "next_state": create_dummy_state(batch_size, state_dim),
-        "done": torch.randn(batch_size),
-    }
-
-
-def create_train_batch_with_visual_input(
-    batch_size: int = 8, state_dim: int = 10, action_dim: int = 10
-) -> dict[str, Tensor]:
-    return {
-        ACTION: create_dummy_action(batch_size, action_dim),
-        "reward": torch.randn(batch_size),
-        "state": create_dummy_with_visual_input(batch_size, state_dim),
-        "next_state": create_dummy_with_visual_input(batch_size, state_dim),
-        "done": torch.randn(batch_size),
-    }
-
-
-def create_observation_batch(batch_size: int = 8, state_dim: int = 10) -> dict[str, Tensor]:
-    return {
-        OBS_STATE: torch.randn(batch_size, state_dim),
-    }
-
-
-def create_observation_batch_with_visual_input(batch_size: int = 8, state_dim: int = 10) -> dict[str, Tensor]:
-    return {
-        OBS_STATE: torch.randn(batch_size, state_dim),
-        OBS_IMAGE: torch.randn(batch_size, 3, 84, 84),
-    }
-
-
-def make_optimizers(policy: SACPolicy, has_discrete_action: bool = False) -> dict[str, torch.optim.Optimizer]:
-    """Create optimizers for the SAC policy."""
-    optimizer_actor = torch.optim.Adam(
-        # Handle the case of shared encoder where the encoder weights are not optimized with the actor gradient
-        params=[
-            p
-            for n, p in policy.actor.named_parameters()
-            if not policy.config.shared_encoder or not n.startswith("encoder")
-        ],
-        lr=policy.config.actor_lr,
-    )
-    optimizer_critic = torch.optim.Adam(
-        params=policy.critic_ensemble.parameters(),
-        lr=policy.config.critic_lr,
-    )
-    optimizer_temperature = torch.optim.Adam(
-        params=[policy.log_alpha],
-        lr=policy.config.critic_lr,
-    )
-
-    optimizers = {
-        "actor": optimizer_actor,
-        "critic": optimizer_critic,
-        "temperature": optimizer_temperature,
-    }
-
-    if has_discrete_action:
-        optimizers["discrete_critic"] = torch.optim.Adam(
-            params=policy.discrete_critic.parameters(),
-            lr=policy.config.critic_lr,
-        )
-
-    return optimizers
-
-
-def create_default_config(
-    state_dim: int, continuous_action_dim: int, has_discrete_action: bool = False
-) -> SACConfig:
-    action_dim = continuous_action_dim
-    if has_discrete_action:
-        action_dim += 1
-
-    config = SACConfig(
-        input_features={OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(state_dim,))},
-        output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(continuous_action_dim,))},
-        dataset_stats={
-            OBS_STATE: {
-                "min": [0.0] * state_dim,
-                "max": [1.0] * state_dim,
-            },
-            ACTION: {
-                "min": [0.0] * continuous_action_dim,
-                "max": [1.0] * continuous_action_dim,
-            },
-        },
-    )
-    config.validate_features()
-    return config
-
-
-def create_config_with_visual_input(
-    state_dim: int, continuous_action_dim: int, has_discrete_action: bool = False
-) -> SACConfig:
-    config = create_default_config(
-        state_dim=state_dim,
-        continuous_action_dim=continuous_action_dim,
-        has_discrete_action=has_discrete_action,
-    )
-    config.input_features[OBS_IMAGE] = PolicyFeature(type=FeatureType.VISUAL, shape=(3, 84, 84))
-    config.dataset_stats[OBS_IMAGE] = {
-        "mean": torch.randn(3, 1, 1),
-        "std": torch.randn(3, 1, 1),
-    }
-
-    # Let make tests a little bit faster
-    config.state_encoder_hidden_dim = 32
-    config.latent_dim = 32
-
-    config.validate_features()
-    return config
-
-
-@pytest.mark.parametrize("batch_size,state_dim,action_dim", [(2, 6, 6), (1, 10, 10)])
-def test_sac_policy_with_default_config(batch_size: int, state_dim: int, action_dim: int):
-    batch = create_default_train_batch(batch_size=batch_size, action_dim=action_dim, state_dim=state_dim)
-    config = create_default_config(state_dim=state_dim, continuous_action_dim=action_dim)
-
-    policy = SACPolicy(config=config)
-    policy.train()
-
-    optimizers = make_optimizers(policy)
-
-    cirtic_loss = policy.forward(batch, model="critic")["loss_critic"]
-    assert cirtic_loss.item() is not None
-    assert cirtic_loss.shape == ()
-    cirtic_loss.backward()
-    optimizers["critic"].step()
-
-    actor_loss = policy.forward(batch, model="actor")["loss_actor"]
-    assert actor_loss.item() is not None
-    assert actor_loss.shape == ()
-
-    actor_loss.backward()
-    optimizers["actor"].step()
-
-    temperature_loss = policy.forward(batch, model="temperature")["loss_temperature"]
-    assert temperature_loss.item() is not None
-    assert temperature_loss.shape == ()
-
-    temperature_loss.backward()
-    optimizers["temperature"].step()
-
-    policy.eval()
-    with torch.no_grad():
-        observation_batch = create_observation_batch(batch_size=batch_size, state_dim=state_dim)
-        selected_action = policy.select_action(observation_batch)
-        assert selected_action.shape == (batch_size, action_dim)
-
-
-@pytest.mark.parametrize("batch_size,state_dim,action_dim", [(2, 6, 6), (1, 10, 10)])
-def test_sac_policy_with_visual_input(batch_size: int, state_dim: int, action_dim: int):
-    config = create_config_with_visual_input(state_dim=state_dim, continuous_action_dim=action_dim)
-    policy = SACPolicy(config=config)
-
-    batch = create_train_batch_with_visual_input(
-        batch_size=batch_size, state_dim=state_dim, action_dim=action_dim
-    )
-
-    policy.train()
-
-    optimizers = make_optimizers(policy)
-
-    cirtic_loss = policy.forward(batch, model="critic")["loss_critic"]
-    assert cirtic_loss.item() is not None
-    assert cirtic_loss.shape == ()
-    cirtic_loss.backward()
-    optimizers["critic"].step()
-
-    actor_loss = policy.forward(batch, model="actor")["loss_actor"]
-    assert actor_loss.item() is not None
-    assert actor_loss.shape == ()
-
-    actor_loss.backward()
-    optimizers["actor"].step()
-
-    temperature_loss = policy.forward(batch, model="temperature")["loss_temperature"]
-    assert temperature_loss.item() is not None
-    assert temperature_loss.shape == ()
-
-    temperature_loss.backward()
-    optimizers["temperature"].step()
-
-    policy.eval()
-    with torch.no_grad():
-        observation_batch = create_observation_batch_with_visual_input(
-            batch_size=batch_size, state_dim=state_dim
-        )
-        selected_action = policy.select_action(observation_batch)
-        assert selected_action.shape == (batch_size, action_dim)
-
-
-# Let's check best candidates for pretrained encoders
-@pytest.mark.parametrize(
-    "batch_size,state_dim,action_dim,vision_encoder_name",
-    [(1, 6, 6, "helper2424/resnet10"), (1, 6, 6, "facebook/convnext-base-224")],
-)
-@pytest.mark.skipif(not TRANSFORMERS_AVAILABLE, reason="Transformers are not installed")
-@pytest.mark.skip(
-    reason="helper2424/resnet10 needs to be updated to work with the latest version of transformers"
-)
-def test_sac_policy_with_pretrained_encoder(
-    batch_size: int, state_dim: int, action_dim: int, vision_encoder_name: str
-):
-    config = create_config_with_visual_input(state_dim=state_dim, continuous_action_dim=action_dim)
-    config.vision_encoder_name = vision_encoder_name
-    policy = SACPolicy(config=config)
-    policy.train()
-
-    batch = create_train_batch_with_visual_input(
-        batch_size=batch_size, state_dim=state_dim, action_dim=action_dim
-    )
-
-    optimizers = make_optimizers(policy)
-
-    cirtic_loss = policy.forward(batch, model="critic")["loss_critic"]
-    assert cirtic_loss.item() is not None
-    assert cirtic_loss.shape == ()
-    cirtic_loss.backward()
-    optimizers["critic"].step()
-
-    actor_loss = policy.forward(batch, model="actor")["loss_actor"]
-    assert actor_loss.item() is not None
-    assert actor_loss.shape == ()
-
-
-def test_sac_policy_with_shared_encoder():
-    batch_size = 2
-    action_dim = 10
-    state_dim = 10
-    config = create_config_with_visual_input(state_dim=state_dim, continuous_action_dim=action_dim)
-    config.shared_encoder = True
-
-    policy = SACPolicy(config=config)
-    policy.train()
-
-    batch = create_train_batch_with_visual_input(
-        batch_size=batch_size, state_dim=state_dim, action_dim=action_dim
-    )
-
-    policy.train()
-
-    optimizers = make_optimizers(policy)
-
-    cirtic_loss = policy.forward(batch, model="critic")["loss_critic"]
-    assert cirtic_loss.item() is not None
-    assert cirtic_loss.shape == ()
-    cirtic_loss.backward()
-    optimizers["critic"].step()
-
-    actor_loss = policy.forward(batch, model="actor")["loss_actor"]
-    assert actor_loss.item() is not None
-    assert actor_loss.shape == ()
-
-    actor_loss.backward()
-    optimizers["actor"].step()
-
-
-def test_sac_policy_with_discrete_critic():
-    batch_size = 2
-    continuous_action_dim = 9
-    full_action_dim = continuous_action_dim + 1  # the last action is discrete
-    state_dim = 10
-    config = create_config_with_visual_input(
-        state_dim=state_dim, continuous_action_dim=continuous_action_dim, has_discrete_action=True
-    )
-
-    num_discrete_actions = 5
-    config.num_discrete_actions = num_discrete_actions
-
-    policy = SACPolicy(config=config)
-    policy.train()
-
-    batch = create_train_batch_with_visual_input(
-        batch_size=batch_size, state_dim=state_dim, action_dim=full_action_dim
-    )
-
-    policy.train()
-
-    optimizers = make_optimizers(policy, has_discrete_action=True)
-
-    cirtic_loss = policy.forward(batch, model="critic")["loss_critic"]
-    assert cirtic_loss.item() is not None
-    assert cirtic_loss.shape == ()
-    cirtic_loss.backward()
-    optimizers["critic"].step()
-
-    discrete_critic_loss = policy.forward(batch, model="discrete_critic")["loss_discrete_critic"]
-    assert discrete_critic_loss.item() is not None
-    assert discrete_critic_loss.shape == ()
-    discrete_critic_loss.backward()
-    optimizers["discrete_critic"].step()
-
-    actor_loss = policy.forward(batch, model="actor")["loss_actor"]
-    assert actor_loss.item() is not None
-    assert actor_loss.shape == ()
-
-    actor_loss.backward()
-    optimizers["actor"].step()
-
-    policy.eval()
-    with torch.no_grad():
-        observation_batch = create_observation_batch_with_visual_input(
-            batch_size=batch_size, state_dim=state_dim
-        )
-        selected_action = policy.select_action(observation_batch)
-        assert selected_action.shape == (batch_size, full_action_dim)
-
-        discrete_actions = selected_action[:, -1].long()
-        discrete_action_values = set(discrete_actions.tolist())
-
-        assert all(action in range(num_discrete_actions) for action in discrete_action_values), (
-            f"Discrete action {discrete_action_values} is not in range({num_discrete_actions})"
-        )
-
-
-def test_sac_policy_with_default_entropy():
-    config = create_default_config(continuous_action_dim=10, state_dim=10)
-    policy = SACPolicy(config=config)
-    assert policy.target_entropy == -5.0
-
-
-def test_sac_policy_default_target_entropy_with_discrete_action():
-    config = create_config_with_visual_input(state_dim=10, continuous_action_dim=6, has_discrete_action=True)
-    policy = SACPolicy(config=config)
-    assert policy.target_entropy == -3.0
-
-
-def test_sac_policy_with_predefined_entropy():
-    config = create_default_config(state_dim=10, continuous_action_dim=6)
-    config.target_entropy = -3.5
-
-    policy = SACPolicy(config=config)
-    assert policy.target_entropy == pytest.approx(-3.5)
-
-
-def test_sac_policy_update_temperature():
-    """Test that temperature property is always in sync with log_alpha."""
-    config = create_default_config(continuous_action_dim=10, state_dim=10)
-    policy = SACPolicy(config=config)
-
-    assert policy.temperature == pytest.approx(1.0)
-    policy.log_alpha.data = torch.tensor([math.log(0.1)])
-    # Temperature property automatically reflects log_alpha changes
-    assert policy.temperature == pytest.approx(0.1)
-
-
-def test_sac_policy_update_target_network():
-    config = create_default_config(state_dim=10, continuous_action_dim=6)
-    config.critic_target_update_weight = 1.0
-
-    policy = SACPolicy(config=config)
-    policy.train()
-
-    for p in policy.critic_ensemble.parameters():
-        p.data = torch.ones_like(p.data)
-
-    policy.update_target_networks()
-    for p in policy.critic_target.parameters():
-        assert torch.allclose(p.data, torch.ones_like(p.data)), (
-            f"Target network {p.data} is not equal to {torch.ones_like(p.data)}"
-        )
-
-
-@pytest.mark.parametrize("num_critics", [1, 3])
-def test_sac_policy_with_critics_number_of_heads(num_critics: int):
-    batch_size = 2
-    action_dim = 10
-    state_dim = 10
-    config = create_config_with_visual_input(state_dim=state_dim, continuous_action_dim=action_dim)
-    config.num_critics = num_critics
-
-    policy = SACPolicy(config=config)
-    policy.train()
-
-    assert len(policy.critic_ensemble.critics) == num_critics
-
-    batch = create_train_batch_with_visual_input(
-        batch_size=batch_size, state_dim=state_dim, action_dim=action_dim
-    )
-
-    policy.train()
-
-    optimizers = make_optimizers(policy)
-
-    cirtic_loss = policy.forward(batch, model="critic")["loss_critic"]
-    assert cirtic_loss.item() is not None
-    assert cirtic_loss.shape == ()
-    cirtic_loss.backward()
-    optimizers["critic"].step()
-
-
-def test_sac_policy_save_and_load(tmp_path):
-    root = tmp_path / "test_sac_save_and_load"
-
-    state_dim = 10
-    action_dim = 10
-    batch_size = 2
-
-    config = create_default_config(state_dim=state_dim, continuous_action_dim=action_dim)
-    policy = SACPolicy(config=config)
-    policy.eval()
-    policy.save_pretrained(root)
-    loaded_policy = SACPolicy.from_pretrained(root, config=config)
-    loaded_policy.eval()
-
-    batch = create_default_train_batch(batch_size=1, state_dim=10, action_dim=10)
-
-    with torch.no_grad():
-        with seeded_context(12):
-            # Collect policy values before saving
-            cirtic_loss = policy.forward(batch, model="critic")["loss_critic"]
-            actor_loss = policy.forward(batch, model="actor")["loss_actor"]
-            temperature_loss = policy.forward(batch, model="temperature")["loss_temperature"]
-
-            observation_batch = create_observation_batch(batch_size=batch_size, state_dim=state_dim)
-            actions = policy.select_action(observation_batch)
-
-        with seeded_context(12):
-            # Collect policy values after loading
-            loaded_cirtic_loss = loaded_policy.forward(batch, model="critic")["loss_critic"]
-            loaded_actor_loss = loaded_policy.forward(batch, model="actor")["loss_actor"]
-            loaded_temperature_loss = loaded_policy.forward(batch, model="temperature")["loss_temperature"]
-
-            loaded_observation_batch = create_observation_batch(batch_size=batch_size, state_dim=state_dim)
-            loaded_actions = loaded_policy.select_action(loaded_observation_batch)
-
-        assert policy.state_dict().keys() == loaded_policy.state_dict().keys()
-        for k in policy.state_dict():
-            assert torch.allclose(policy.state_dict()[k], loaded_policy.state_dict()[k], atol=1e-6)
-
-        # Compare values before and after saving and loading
-        # They should be the same
-        assert torch.allclose(cirtic_loss, loaded_cirtic_loss)
-        assert torch.allclose(actor_loss, loaded_actor_loss)
-        assert torch.allclose(temperature_loss, loaded_temperature_loss)
-        assert torch.allclose(actions, loaded_actions)

tests/processor/test_gaussian_actor_processor.py

@@ -21,8 +21,8 @@ import pytest
 import torch
 
 from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
-from lerobot.policies.sac.configuration_sac import SACConfig
-from lerobot.policies.sac.processor_sac import make_sac_pre_post_processors
+from lerobot.policies.gaussian_actor.configuration_gaussian_actor import GaussianActorConfig
+from lerobot.policies.gaussian_actor.processor_gaussian_actor import make_gaussian_actor_pre_post_processors
 from lerobot.processor import (
     AddBatchDimensionProcessorStep,
     DataProcessorPipeline,
@@ -38,7 +38,7 @@ from lerobot.utils.constants import ACTION, OBS_STATE
 
 def create_default_config():
     """Create a default SAC configuration for testing."""
-    config = SACConfig()
+    config = GaussianActorConfig()
     config.input_features = {
         OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(10,)),
     }
@@ -66,7 +66,7 @@ def test_make_sac_processor_basic():
     config = create_default_config()
     stats = create_default_stats()
 
-    preprocessor, postprocessor = make_sac_pre_post_processors(
+    preprocessor, postprocessor = make_gaussian_actor_pre_post_processors(
         config,
         stats,
     )
@@ -88,12 +88,12 @@ def test_make_sac_processor_basic():
     assert isinstance(postprocessor.steps[1], DeviceProcessorStep)
 
 
-def test_sac_processor_normalization_modes():
+def test_gaussian_actor_processor_normalization_modes():
     """Test that SAC processor correctly handles different normalization modes."""
     config = create_default_config()
     stats = create_default_stats()
 
-    preprocessor, postprocessor = make_sac_pre_post_processors(
+    preprocessor, postprocessor = make_gaussian_actor_pre_post_processors(
         config,
         stats,
     )
@@ -121,13 +121,13 @@ def test_sac_processor_normalization_modes():
 
 
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
-def test_sac_processor_cuda():
+def test_gaussian_actor_processor_cuda():
     """Test SAC processor with CUDA device."""
     config = create_default_config()
     config.device = "cuda"
     stats = create_default_stats()
 
-    preprocessor, postprocessor = make_sac_pre_post_processors(
+    preprocessor, postprocessor = make_gaussian_actor_pre_post_processors(
         config,
         stats,
     )
@@ -153,13 +153,13 @@ def test_sac_processor_cuda():
 
 
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
-def test_sac_processor_accelerate_scenario():
+def test_gaussian_actor_processor_accelerate_scenario():
     """Test SAC processor in simulated Accelerate scenario."""
     config = create_default_config()
     config.device = "cuda:0"
     stats = create_default_stats()
 
-    preprocessor, postprocessor = make_sac_pre_post_processors(
+    preprocessor, postprocessor = make_gaussian_actor_pre_post_processors(
         config,
         stats,
     )
@@ -180,13 +180,13 @@ def test_sac_processor_accelerate_scenario():
 
 
 @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires at least 2 GPUs")
-def test_sac_processor_multi_gpu():
+def test_gaussian_actor_processor_multi_gpu():
     """Test SAC processor with multi-GPU setup."""
     config = create_default_config()
     config.device = "cuda:0"
     stats = create_default_stats()
 
-    preprocessor, postprocessor = make_sac_pre_post_processors(
+    preprocessor, postprocessor = make_gaussian_actor_pre_post_processors(
         config,
         stats,
     )
@@ -206,11 +206,11 @@ def test_sac_processor_multi_gpu():
     assert processed[TransitionKey.ACTION.value].device == device
 
 
-def test_sac_processor_without_stats():
+def test_gaussian_actor_processor_without_stats():
     """Test SAC processor creation without dataset statistics."""
     config = create_default_config()
 
-    preprocessor, postprocessor = make_sac_pre_post_processors(config, dataset_stats=None)
+    preprocessor, postprocessor = make_gaussian_actor_pre_post_processors(config, dataset_stats=None)
 
     # Should still create processors
     assert preprocessor is not None
@@ -226,12 +226,12 @@ def test_sac_processor_without_stats():
     assert processed is not None
 
 
-def test_sac_processor_save_and_load():
+def test_gaussian_actor_processor_save_and_load():
     """Test saving and loading SAC processor."""
     config = create_default_config()
     stats = create_default_stats()
 
-    preprocessor, postprocessor = make_sac_pre_post_processors(
+    preprocessor, postprocessor = make_gaussian_actor_pre_post_processors(
         config,
         stats,
     )
@@ -257,14 +257,14 @@ def test_sac_processor_save_and_load():
 
 
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
-def test_sac_processor_mixed_precision():
+def test_gaussian_actor_processor_mixed_precision():
     """Test SAC processor with mixed precision."""
     config = create_default_config()
     config.device = "cuda"
     stats = create_default_stats()
 
     # Create processor
-    preprocessor, postprocessor = make_sac_pre_post_processors(
+    preprocessor, postprocessor = make_gaussian_actor_pre_post_processors(
         config,
         stats,
     )
@@ -304,12 +304,12 @@ def test_sac_processor_mixed_precision():
     assert processed[TransitionKey.ACTION.value].dtype == torch.float16
 
 
-def test_sac_processor_batch_data():
+def test_gaussian_actor_processor_batch_data():
     """Test SAC processor with batched data."""
     config = create_default_config()
     stats = create_default_stats()
 
-    preprocessor, postprocessor = make_sac_pre_post_processors(
+    preprocessor, postprocessor = make_gaussian_actor_pre_post_processors(
         config,
         stats,
     )
@@ -329,12 +329,12 @@ def test_sac_processor_batch_data():
     assert processed[TransitionKey.ACTION.value].shape == (batch_size, 5)
 
 
-def test_sac_processor_edge_cases():
+def test_gaussian_actor_processor_edge_cases():
     """Test SAC processor with edge cases."""
     config = create_default_config()
     stats = create_default_stats()
 
-    preprocessor, postprocessor = make_sac_pre_post_processors(
+    preprocessor, postprocessor = make_gaussian_actor_pre_post_processors(
         config,
         stats,
     )
@@ -358,13 +358,13 @@ def test_sac_processor_edge_cases():
 
 
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
-def test_sac_processor_bfloat16_device_float32_normalizer():
+def test_gaussian_actor_processor_bfloat16_device_float32_normalizer():
     """Test: DeviceProcessor(bfloat16) + NormalizerProcessor(float32) → output bfloat16 via automatic adaptation"""
     config = create_default_config()
     config.device = "cuda"
     stats = create_default_stats()
 
-    preprocessor, _ = make_sac_pre_post_processors(
+    preprocessor, _ = make_gaussian_actor_pre_post_processors(
         config,
         stats,
     )

tests/processor/test_normalize_processor.py

@@ -1804,13 +1804,15 @@ def test_stats_override_preservation_in_load_state_dict():
                 override_normalizer.stats[key][stat_name], original_stats[key][stat_name]
             ), f"Stats for {key}.{stat_name} should not match original stats"
 
-    # Verify that _tensor_stats are also correctly set to match the override stats
+    # Verify that _tensor_stats values match the override stats
+    # Note: visual stats are reshaped from (C,) to (C,1,1) by _reshape_visual_stats
     expected_tensor_stats = to_tensor(override_stats)
     for key in expected_tensor_stats:
         for stat_name in expected_tensor_stats[key]:
             if isinstance(expected_tensor_stats[key][stat_name], torch.Tensor):
                 torch.testing.assert_close(
-                    override_normalizer._tensor_stats[key][stat_name], expected_tensor_stats[key][stat_name]
+                    override_normalizer._tensor_stats[key][stat_name].squeeze(),
+                    expected_tensor_stats[key][stat_name].squeeze(),
                 )
 
 
@@ -1849,12 +1851,16 @@ def test_stats_without_override_loads_normally():
     # Stats should now match the original stats (normal behavior)
     # Check that all keys and values match
     assert set(new_normalizer.stats.keys()) == set(original_stats.keys())
+    # Note: visual stats are reshaped from (C,) to (C,1,1) by _reshape_visual_stats,
+    # so we squeeze before comparing values.
     for key in original_stats:
         assert set(new_normalizer.stats[key].keys()) == set(original_stats[key].keys())
         for stat_name in original_stats[key]:
-            np.testing.assert_allclose(
-                new_normalizer.stats[key][stat_name], original_stats[key][stat_name], rtol=1e-6, atol=1e-6
-            )
+            actual = new_normalizer.stats[key][stat_name]
+            expected = original_stats[key][stat_name]
+            if hasattr(actual, "squeeze"):
+                actual = actual.squeeze()
+            np.testing.assert_allclose(actual, expected, rtol=1e-6, atol=1e-6)
 
 
 def test_stats_explicit_provided_flag_detection():
@@ -2075,8 +2081,9 @@ def test_stats_reconstruction_after_load_state_dict():
     assert ACTION in new_normalizer.stats
 
     # Check that values are correct (converted back from tensors)
-    np.testing.assert_allclose(new_normalizer.stats[OBS_IMAGE]["mean"], [0.5, 0.5, 0.5])
-    np.testing.assert_allclose(new_normalizer.stats[OBS_IMAGE]["std"], [0.2, 0.2, 0.2])
+    # Note: visual stats are reshaped to (C,1,1), so we squeeze before comparing
+    np.testing.assert_allclose(new_normalizer.stats[OBS_IMAGE]["mean"].squeeze(), [0.5, 0.5, 0.5])
+    np.testing.assert_allclose(new_normalizer.stats[OBS_IMAGE]["std"].squeeze(), [0.2, 0.2, 0.2])
     np.testing.assert_allclose(new_normalizer.stats[OBS_STATE]["min"], [0.0, -1.0])
     np.testing.assert_allclose(new_normalizer.stats[OBS_STATE]["max"], [1.0, 1.0])
     np.testing.assert_allclose(new_normalizer.stats[ACTION]["mean"], [0.0, 0.0])

tests/rewards/test_modeling_classifier.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import pytest
 import torch
 
 from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
@@ -36,9 +35,6 @@ def test_classifier_output():
 
 
 @skip_if_package_missing("transformers")
-@pytest.mark.skip(
-    reason="helper2424/resnet10 needs to be updated to work with the latest version of transformers"
-)
 def test_binary_classifier_with_default_params():
     from lerobot.rewards.classifier.modeling_classifier import Classifier
 
@@ -80,9 +76,6 @@ def test_binary_classifier_with_default_params():
 
 
 @skip_if_package_missing("transformers")
-@pytest.mark.skip(
-    reason="helper2424/resnet10 needs to be updated to work with the latest version of transformers"
-)
 def test_multiclass_classifier():
     from lerobot.rewards.classifier.modeling_classifier import Classifier
 
@@ -122,9 +115,6 @@ def test_multiclass_classifier():
 
 
 @skip_if_package_missing("transformers")
-@pytest.mark.skip(
-    reason="helper2424/resnet10 needs to be updated to work with the latest version of transformers"
-)
 def test_default_device():
     from lerobot.rewards.classifier.modeling_classifier import Classifier
 
@@ -141,9 +131,6 @@ def test_default_device():
 
 
 @skip_if_package_missing("transformers")
-@pytest.mark.skip(
-    reason="helper2424/resnet10 needs to be updated to work with the latest version of transformers"
-)
 def test_explicit_device_setup():
     from lerobot.rewards.classifier.modeling_classifier import Classifier
 

tests/rl/test_actor_learner.py

@@ -22,12 +22,14 @@ import pytest
 import torch
 
 pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
+pytest.importorskip("grpc")
 
 from torch.multiprocessing import Event, Queue
 
-from lerobot.configs.train import TrainRLServerPipelineConfig
-from lerobot.policies.sac.configuration_sac import SACConfig
-from lerobot.utils.constants import OBS_STR
+from lerobot.configs.types import FeatureType, PolicyFeature
+from lerobot.policies.gaussian_actor.configuration_gaussian_actor import GaussianActorConfig
+from lerobot.rl.train_rl import TrainRLServerPipelineConfig
+from lerobot.utils.constants import ACTION, OBS_STATE, OBS_STR
 from lerobot.utils.transition import Transition
 from tests.utils import skip_if_package_missing
 
@@ -79,7 +81,7 @@ def cfg():
 
     port = find_free_port()
 
-    policy_cfg = SACConfig()
+    policy_cfg = GaussianActorConfig()
     policy_cfg.actor_learner_config.learner_host = "127.0.0.1"
     policy_cfg.actor_learner_config.learner_port = port
     policy_cfg.concurrency.actor = "threads"
@@ -299,3 +301,164 @@ def test_end_to_end_parameters_flow(cfg, data_size):
     assert received_params.keys() == input_params.keys()
     for key in input_params:
         assert torch.allclose(received_params[key], input_params[key])
+
+
+def test_learner_algorithm_wiring():
+    """Verify that make_algorithm constructs an SACAlgorithm from config,
+    make_optimizers_and_scheduler() creates the right optimizers, update() works, and
+    get_weights() output is serializable."""
+    from lerobot.policies.gaussian_actor.modeling_gaussian_actor import GaussianActorPolicy
+    from lerobot.rl.algorithms.factory import make_algorithm
+    from lerobot.rl.algorithms.sac import SACAlgorithm, SACAlgorithmConfig
+    from lerobot.transport.utils import state_to_bytes
+
+    state_dim = 10
+    action_dim = 6
+
+    sac_cfg = GaussianActorConfig(
+        input_features={OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(state_dim,))},
+        output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(action_dim,))},
+        dataset_stats={
+            OBS_STATE: {"min": [0.0] * state_dim, "max": [1.0] * state_dim},
+            ACTION: {"min": [0.0] * action_dim, "max": [1.0] * action_dim},
+        },
+    )
+    sac_cfg.validate_features()
+
+    policy = GaussianActorPolicy(config=sac_cfg)
+    policy.train()
+
+    algorithm = make_algorithm(cfg=SACAlgorithmConfig.from_policy_config(sac_cfg), policy=policy)
+    assert isinstance(algorithm, SACAlgorithm)
+
+    optimizers = algorithm.make_optimizers_and_scheduler()
+    assert "actor" in optimizers
+    assert "critic" in optimizers
+    assert "temperature" in optimizers
+
+    batch_size = 4
+
+    def batch_iterator():
+        while True:
+            yield {
+                ACTION: torch.randn(batch_size, action_dim),
+                "reward": torch.randn(batch_size),
+                "state": {OBS_STATE: torch.randn(batch_size, state_dim)},
+                "next_state": {OBS_STATE: torch.randn(batch_size, state_dim)},
+                "done": torch.zeros(batch_size),
+                "complementary_info": {},
+            }
+
+    stats = algorithm.update(batch_iterator())
+    assert "loss_critic" in stats.losses
+
+    # get_weights -> state_to_bytes round-trip
+    weights = algorithm.get_weights()
+    assert len(weights) > 0
+    serialized = state_to_bytes(weights)
+    assert isinstance(serialized, bytes)
+    assert len(serialized) > 0
+
+    # RLTrainer with DataMixer
+    from lerobot.rl.buffer import ReplayBuffer
+    from lerobot.rl.data_sources import OnlineOfflineMixer
+    from lerobot.rl.trainer import RLTrainer
+
+    replay_buffer = ReplayBuffer(
+        capacity=50,
+        device="cpu",
+        state_keys=[OBS_STATE],
+        storage_device="cpu",
+        use_drq=False,
+    )
+    for _ in range(50):
+        replay_buffer.add(
+            state={OBS_STATE: torch.randn(state_dim)},
+            action=torch.randn(action_dim),
+            reward=1.0,
+            next_state={OBS_STATE: torch.randn(state_dim)},
+            done=False,
+            truncated=False,
+        )
+    data_mixer = OnlineOfflineMixer(online_buffer=replay_buffer, offline_buffer=None)
+    trainer = RLTrainer(
+        algorithm=algorithm,
+        data_mixer=data_mixer,
+        batch_size=batch_size,
+    )
+    trainer_stats = trainer.training_step()
+    assert "loss_critic" in trainer_stats.losses
+
+
+def test_initial_and_periodic_weight_push_consistency():
+    """Both initial and periodic weight pushes should use algorithm.get_weights()
+    and produce identical structures."""
+    from lerobot.policies.gaussian_actor.modeling_gaussian_actor import GaussianActorPolicy
+    from lerobot.rl.algorithms.factory import make_algorithm
+    from lerobot.rl.algorithms.sac import SACAlgorithmConfig
+    from lerobot.transport.utils import bytes_to_state_dict, state_to_bytes
+
+    state_dim = 10
+    action_dim = 6
+    sac_cfg = GaussianActorConfig(
+        input_features={OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(state_dim,))},
+        output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(action_dim,))},
+        dataset_stats={
+            OBS_STATE: {"min": [0.0] * state_dim, "max": [1.0] * state_dim},
+            ACTION: {"min": [0.0] * action_dim, "max": [1.0] * action_dim},
+        },
+    )
+    sac_cfg.validate_features()
+
+    policy = GaussianActorPolicy(config=sac_cfg)
+    policy.train()
+    algorithm = make_algorithm(cfg=SACAlgorithmConfig.from_policy_config(sac_cfg), policy=policy)
+    algorithm.make_optimizers_and_scheduler()
+
+    # Simulate initial push (same code path the learner now uses)
+    initial_weights = algorithm.get_weights()
+    initial_bytes = state_to_bytes(initial_weights)
+
+    # Simulate periodic push
+    periodic_weights = algorithm.get_weights()
+    periodic_bytes = state_to_bytes(periodic_weights)
+
+    initial_decoded = bytes_to_state_dict(initial_bytes)
+    periodic_decoded = bytes_to_state_dict(periodic_bytes)
+
+    assert initial_decoded.keys() == periodic_decoded.keys()
+
+
+def test_actor_side_algorithm_select_action_and_load_weights():
+    """Simulate actor: create algorithm without optimizers, select_action, load_weights."""
+    from lerobot.policies.gaussian_actor.modeling_gaussian_actor import GaussianActorPolicy
+    from lerobot.rl.algorithms.factory import make_algorithm
+    from lerobot.rl.algorithms.sac import SACAlgorithm, SACAlgorithmConfig
+
+    state_dim = 10
+    action_dim = 6
+    sac_cfg = GaussianActorConfig(
+        input_features={OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(state_dim,))},
+        output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(action_dim,))},
+        dataset_stats={
+            OBS_STATE: {"min": [0.0] * state_dim, "max": [1.0] * state_dim},
+            ACTION: {"min": [0.0] * action_dim, "max": [1.0] * action_dim},
+        },
+    )
+    sac_cfg.validate_features()
+
+    # Actor side: no optimizers
+    policy = GaussianActorPolicy(config=sac_cfg)
+    policy.eval()
+    algorithm = make_algorithm(cfg=SACAlgorithmConfig.from_policy_config(sac_cfg), policy=policy)
+    assert isinstance(algorithm, SACAlgorithm)
+    assert algorithm.optimizers == {}
+
+    # select_action should work
+    obs = {OBS_STATE: torch.randn(state_dim)}
+    action = policy.select_action(obs)
+    assert action.shape == (action_dim,)
+
+    # Simulate receiving weights from learner
+    fake_weights = algorithm.get_weights()
+    algorithm.load_weights(fake_weights, device="cpu")

tests/rl/test_data_mixer.py

@@ -0,0 +1,89 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tests for RL data mixing (DataMixer, OnlineOfflineMixer)."""
+
+import pytest
+
+pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
+
+import torch  # noqa: E402
+
+from lerobot.rl.buffer import ReplayBuffer  # noqa: E402
+from lerobot.rl.data_sources import OnlineOfflineMixer  # noqa: E402
+from lerobot.utils.constants import OBS_STATE  # noqa: E402
+
+
+def _make_buffer(capacity: int = 100, state_dim: int = 4) -> ReplayBuffer:
+    buf = ReplayBuffer(
+        capacity=capacity,
+        device="cpu",
+        state_keys=[OBS_STATE],
+        storage_device="cpu",
+        use_drq=False,
+    )
+    for i in range(capacity):
+        buf.add(
+            state={OBS_STATE: torch.randn(state_dim)},
+            action=torch.randn(2),
+            reward=1.0,
+            next_state={OBS_STATE: torch.randn(state_dim)},
+            done=bool(i % 10 == 9),
+            truncated=False,
+        )
+    return buf
+
+
+def test_online_only_mixer_sample():
+    """OnlineOfflineMixer with no offline buffer returns online-only batches."""
+    buf = _make_buffer(capacity=50)
+    mixer = OnlineOfflineMixer(online_buffer=buf, offline_buffer=None, online_ratio=0.5)
+    batch = mixer.sample(batch_size=8)
+    assert batch["state"][OBS_STATE].shape[0] == 8
+    assert batch["action"].shape[0] == 8
+    assert batch["reward"].shape[0] == 8
+
+
+def test_online_only_mixer_ratio_one():
+    """OnlineOfflineMixer with online_ratio=1.0 and no offline is equivalent to online-only."""
+    buf = _make_buffer(capacity=50)
+    mixer = OnlineOfflineMixer(online_buffer=buf, offline_buffer=None, online_ratio=1.0)
+    batch = mixer.sample(batch_size=10)
+    assert batch["state"][OBS_STATE].shape[0] == 10
+
+
+def test_online_offline_mixer_sample():
+    """OnlineOfflineMixer with two buffers returns concatenated batches."""
+    online = _make_buffer(capacity=50)
+    offline = _make_buffer(capacity=50)
+    mixer = OnlineOfflineMixer(
+        online_buffer=online,
+        offline_buffer=offline,
+        online_ratio=0.5,
+    )
+    batch = mixer.sample(batch_size=10)
+    assert batch["state"][OBS_STATE].shape[0] == 10
+    assert batch["action"].shape[0] == 10
+    # 5 from online, 5 from offline (approx)
+    assert batch["reward"].shape[0] == 10
+
+
+def test_online_offline_mixer_iterator():
+    """get_iterator yields batches of the requested size."""
+    buf = _make_buffer(capacity=50)
+    mixer = OnlineOfflineMixer(online_buffer=buf, offline_buffer=None)
+    it = mixer.get_iterator(batch_size=4, async_prefetch=False)
+    batch1 = next(it)
+    batch2 = next(it)
+    assert batch1["state"][OBS_STATE].shape[0] == 4
+    assert batch2["state"][OBS_STATE].shape[0] == 4

tests/rl/test_queue.py

@@ -20,7 +20,7 @@ from queue import Queue
 
 import pytest
 
-pytest.importorskip("grpc")
+pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
 
 from torch.multiprocessing import Queue as TorchMPQueue  # noqa: E402
 

tests/rl/test_sac_algorithm.py

@@ -0,0 +1,517 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tests for the RL algorithm abstraction and SACAlgorithm implementation."""
+
+import pytest
+
+pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
+
+import torch  # noqa: E402
+
+from lerobot.configs.types import FeatureType, PolicyFeature  # noqa: E402
+from lerobot.policies.gaussian_actor.configuration_gaussian_actor import GaussianActorConfig  # noqa: E402
+from lerobot.policies.gaussian_actor.modeling_gaussian_actor import GaussianActorPolicy  # noqa: E402
+from lerobot.rl.algorithms.configs import RLAlgorithmConfig, TrainingStats  # noqa: E402
+from lerobot.rl.algorithms.factory import make_algorithm  # noqa: E402
+from lerobot.rl.algorithms.sac import SACAlgorithm, SACAlgorithmConfig  # noqa: E402
+from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_STATE  # noqa: E402
+from lerobot.utils.random_utils import set_seed  # noqa: E402
+
+# ---------------------------------------------------------------------------
+# Helpers (reuse patterns from tests/policies/test_gaussian_actor_policy.py)
+# ---------------------------------------------------------------------------
+
+
+@pytest.fixture(autouse=True)
+def set_random_seed():
+    set_seed(42)
+
+
+def _make_sac_config(
+    state_dim: int = 10,
+    action_dim: int = 6,
+    num_discrete_actions: int | None = None,
+    with_images: bool = False,
+) -> GaussianActorConfig:
+    config = GaussianActorConfig(
+        input_features={OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(state_dim,))},
+        output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(action_dim,))},
+        dataset_stats={
+            OBS_STATE: {"min": [0.0] * state_dim, "max": [1.0] * state_dim},
+            ACTION: {"min": [0.0] * action_dim, "max": [1.0] * action_dim},
+        },
+        num_discrete_actions=num_discrete_actions,
+    )
+    if with_images:
+        config.input_features[OBS_IMAGE] = PolicyFeature(type=FeatureType.VISUAL, shape=(3, 84, 84))
+        config.dataset_stats[OBS_IMAGE] = {
+            "mean": torch.randn(3, 1, 1).tolist(),
+            "std": torch.randn(3, 1, 1).abs().tolist(),
+        }
+        config.latent_dim = 32
+        config.state_encoder_hidden_dim = 32
+    config.validate_features()
+    return config
+
+
+def _make_algorithm(
+    state_dim: int = 10,
+    action_dim: int = 6,
+    utd_ratio: int = 1,
+    policy_update_freq: int = 1,
+    num_discrete_actions: int | None = None,
+    with_images: bool = False,
+) -> tuple[SACAlgorithm, GaussianActorPolicy]:
+    sac_cfg = _make_sac_config(
+        state_dim=state_dim,
+        action_dim=action_dim,
+        num_discrete_actions=num_discrete_actions,
+        with_images=with_images,
+    )
+    policy = GaussianActorPolicy(config=sac_cfg)
+    policy.train()
+    algo_config = SACAlgorithmConfig.from_policy_config(sac_cfg)
+    algo_config.utd_ratio = utd_ratio
+    algo_config.policy_update_freq = policy_update_freq
+    algorithm = SACAlgorithm(policy=policy, config=algo_config)
+    algorithm.make_optimizers_and_scheduler()
+    return algorithm, policy
+
+
+def _make_batch(
+    batch_size: int = 4,
+    state_dim: int = 10,
+    action_dim: int = 6,
+    with_images: bool = False,
+) -> dict:
+    obs = {OBS_STATE: torch.randn(batch_size, state_dim)}
+    next_obs = {OBS_STATE: torch.randn(batch_size, state_dim)}
+    if with_images:
+        obs[OBS_IMAGE] = torch.randn(batch_size, 3, 84, 84)
+        next_obs[OBS_IMAGE] = torch.randn(batch_size, 3, 84, 84)
+    return {
+        ACTION: torch.randn(batch_size, action_dim),
+        "reward": torch.randn(batch_size),
+        "state": obs,
+        "next_state": next_obs,
+        "done": torch.zeros(batch_size),
+        "complementary_info": {},
+    }
+
+
+def _batch_iterator(**batch_kwargs):
+    """Infinite iterator that yields fresh batches (mirrors a real DataMixer iterator)."""
+    while True:
+        yield _make_batch(**batch_kwargs)
+
+
+# ===========================================================================
+# Registry / config tests
+# ===========================================================================
+
+
+def test_sac_algorithm_config_registered():
+    """SACAlgorithmConfig should be discoverable through the registry."""
+    assert "sac" in RLAlgorithmConfig.get_known_choices()
+    cls = RLAlgorithmConfig.get_choice_class("sac")
+    assert cls is SACAlgorithmConfig
+
+
+def test_sac_algorithm_config_from_policy_config():
+    """from_policy_config embeds the policy config and uses SAC defaults."""
+    sac_cfg = _make_sac_config()
+    algo_cfg = SACAlgorithmConfig.from_policy_config(sac_cfg)
+    assert algo_cfg.policy_config is sac_cfg
+    assert algo_cfg.discrete_critic_network_kwargs is sac_cfg.discrete_critic_network_kwargs
+    # Defaults come from SACAlgorithmConfig, not from the policy config.
+    assert algo_cfg.utd_ratio == 1
+    assert algo_cfg.policy_update_freq == 1
+    assert algo_cfg.grad_clip_norm == 40.0
+    assert algo_cfg.actor_lr == 3e-4
+
+
+# ===========================================================================
+# TrainingStats tests
+# ===========================================================================
+
+
+def test_training_stats_defaults():
+    stats = TrainingStats()
+    assert stats.losses == {}
+    assert stats.grad_norms == {}
+    assert stats.extra == {}
+
+
+# ===========================================================================
+# get_weights
+# ===========================================================================
+
+
+def test_get_weights_returns_policy_state_dict():
+    algorithm, policy = _make_algorithm()
+    weights = algorithm.get_weights()
+    assert "policy" in weights
+    actor_state_dict = policy.actor.state_dict()
+    for key in actor_state_dict:
+        assert key in weights["policy"]
+        assert torch.equal(weights["policy"][key].cpu(), actor_state_dict[key].cpu())
+
+
+def test_get_weights_includes_discrete_critic_when_present():
+    algorithm, _ = _make_algorithm(num_discrete_actions=3, action_dim=6)
+    weights = algorithm.get_weights()
+    assert "discrete_critic" in weights
+    assert len(weights["discrete_critic"]) > 0
+
+
+def test_get_weights_excludes_discrete_critic_when_absent():
+    algorithm, _ = _make_algorithm()
+    weights = algorithm.get_weights()
+    assert "discrete_critic" not in weights
+
+
+def test_get_weights_are_on_cpu():
+    algorithm, _ = _make_algorithm(num_discrete_actions=3, action_dim=6)
+    weights = algorithm.get_weights()
+    for group_name, state_dict in weights.items():
+        for key, tensor in state_dict.items():
+            assert tensor.device == torch.device("cpu"), f"{group_name}/{key} is not on CPU"
+
+
+# ===========================================================================
+# select_action (lives on the policy, not the algorithm)
+# ===========================================================================
+
+
+def test_select_action_returns_correct_shape():
+    action_dim = 6
+    _, policy = _make_algorithm(state_dim=10, action_dim=action_dim)
+    policy.eval()
+    obs = {OBS_STATE: torch.randn(10)}
+    action = policy.select_action(obs)
+    assert action.shape == (action_dim,)
+
+
+def test_select_action_with_discrete_critic():
+    continuous_dim = 5
+    _, policy = _make_algorithm(state_dim=10, action_dim=continuous_dim, num_discrete_actions=3)
+    policy.eval()
+    obs = {OBS_STATE: torch.randn(10)}
+    action = policy.select_action(obs)
+    assert action.shape == (continuous_dim + 1,)
+
+
+# ===========================================================================
+# update (single batch, utd_ratio=1)
+# ===========================================================================
+
+
+def test_update_returns_training_stats():
+    algorithm, _ = _make_algorithm()
+    stats = algorithm.update(_batch_iterator())
+    assert isinstance(stats, TrainingStats)
+    assert "loss_critic" in stats.losses
+    assert isinstance(stats.losses["loss_critic"], float)
+
+
+def test_update_populates_actor_and_temperature_losses():
+    """With policy_update_freq=1 and step 0, actor/temperature should be updated."""
+    algorithm, _ = _make_algorithm(policy_update_freq=1)
+    stats = algorithm.update(_batch_iterator())
+    assert "loss_actor" in stats.losses
+    assert "loss_temperature" in stats.losses
+    assert "temperature" in stats.extra
+
+
+@pytest.mark.parametrize("policy_update_freq", [2, 3])
+def test_update_skips_actor_at_non_update_steps(policy_update_freq):
+    """Actor/temperature should only update when optimization_step % freq == 0."""
+    algorithm, _ = _make_algorithm(policy_update_freq=policy_update_freq)
+    it = _batch_iterator()
+
+    # Step 0: should update actor
+    stats_0 = algorithm.update(it)
+    assert "loss_actor" in stats_0.losses
+
+    # Step 1: should NOT update actor
+    stats_1 = algorithm.update(it)
+    assert "loss_actor" not in stats_1.losses
+
+
+def test_update_increments_optimization_step():
+    algorithm, _ = _make_algorithm()
+    it = _batch_iterator()
+    assert algorithm.optimization_step == 0
+    algorithm.update(it)
+    assert algorithm.optimization_step == 1
+    algorithm.update(it)
+    assert algorithm.optimization_step == 2
+
+
+def test_update_with_discrete_critic():
+    algorithm, _ = _make_algorithm(num_discrete_actions=3, action_dim=6)
+    stats = algorithm.update(_batch_iterator(action_dim=7))  # continuous + 1 discrete
+    assert "loss_discrete_critic" in stats.losses
+    assert "discrete_critic" in stats.grad_norms
+
+
+# ===========================================================================
+# update with UTD ratio > 1
+# ===========================================================================
+
+
+@pytest.mark.parametrize("utd_ratio", [2, 4])
+def test_update_with_utd_ratio(utd_ratio):
+    algorithm, _ = _make_algorithm(utd_ratio=utd_ratio)
+    stats = algorithm.update(_batch_iterator())
+    assert isinstance(stats, TrainingStats)
+    assert "loss_critic" in stats.losses
+    assert algorithm.optimization_step == 1
+
+
+def test_update_utd_ratio_pulls_utd_batches():
+    """next(batch_iterator) should be called exactly utd_ratio times."""
+    utd_ratio = 3
+    algorithm, _ = _make_algorithm(utd_ratio=utd_ratio)
+
+    call_count = 0
+
+    def counting_iterator():
+        nonlocal call_count
+        while True:
+            call_count += 1
+            yield _make_batch()
+
+    algorithm.update(counting_iterator())
+    assert call_count == utd_ratio
+
+
+def test_update_utd_ratio_3_critic_warmup_changes_weights():
+    """With utd_ratio=3, critic weights should change after update (3 critic steps)."""
+    algorithm, policy = _make_algorithm(utd_ratio=3)
+
+    critic_params_before = {n: p.clone() for n, p in algorithm.critic_ensemble.named_parameters()}
+
+    algorithm.update(_batch_iterator())
+
+    changed = False
+    for n, p in algorithm.critic_ensemble.named_parameters():
+        if not torch.equal(p, critic_params_before[n]):
+            changed = True
+            break
+    assert changed, "Critic weights should have changed after UTD update"
+
+
+# ===========================================================================
+# get_observation_features
+# ===========================================================================
+
+
+def test_get_observation_features_returns_none_without_frozen_encoder():
+    algorithm, _ = _make_algorithm(with_images=False)
+    obs = {OBS_STATE: torch.randn(4, 10)}
+    next_obs = {OBS_STATE: torch.randn(4, 10)}
+    feat, next_feat = algorithm.get_observation_features(obs, next_obs)
+    assert feat is None
+    assert next_feat is None
+
+
+# ===========================================================================
+# optimization_step setter
+# ===========================================================================
+
+
+def test_optimization_step_can_be_set_for_resume():
+    algorithm, _ = _make_algorithm()
+    algorithm.optimization_step = 100
+    assert algorithm.optimization_step == 100
+
+
+# ===========================================================================
+# make_algorithm factory
+# ===========================================================================
+
+
+def test_make_algorithm_returns_sac_for_sac_policy():
+    sac_cfg = _make_sac_config()
+    policy = GaussianActorPolicy(config=sac_cfg)
+    algorithm = make_algorithm(cfg=SACAlgorithmConfig.from_policy_config(sac_cfg), policy=policy)
+    assert isinstance(algorithm, SACAlgorithm)
+    assert algorithm.optimizers == {}
+
+
+def test_make_optimizers_creates_expected_keys():
+    """make_optimizers_and_scheduler() should populate the algorithm with Adam optimizers."""
+    sac_cfg = _make_sac_config()
+    policy = GaussianActorPolicy(config=sac_cfg)
+    algorithm = make_algorithm(cfg=SACAlgorithmConfig.from_policy_config(sac_cfg), policy=policy)
+    optimizers = algorithm.make_optimizers_and_scheduler()
+    assert "actor" in optimizers
+    assert "critic" in optimizers
+    assert "temperature" in optimizers
+    assert all(isinstance(v, torch.optim.Adam) for v in optimizers.values())
+    assert algorithm.get_optimizers() is optimizers
+
+
+def test_actor_side_no_optimizers():
+    """Actor-side usage: no optimizers needed, make_optimizers_and_scheduler is not called."""
+    sac_cfg = _make_sac_config()
+    policy = GaussianActorPolicy(config=sac_cfg)
+    algorithm = make_algorithm(cfg=SACAlgorithmConfig.from_policy_config(sac_cfg), policy=policy)
+    assert isinstance(algorithm, SACAlgorithm)
+    assert algorithm.optimizers == {}
+
+
+def test_make_algorithm_uses_sac_algorithm_defaults():
+    """make_algorithm populates SACAlgorithmConfig with its own defaults."""
+    sac_cfg = _make_sac_config()
+    policy = GaussianActorPolicy(config=sac_cfg)
+    algorithm = make_algorithm(cfg=SACAlgorithmConfig.from_policy_config(sac_cfg), policy=policy)
+    assert algorithm.config.utd_ratio == 1
+    assert algorithm.config.policy_update_freq == 1
+    assert algorithm.config.grad_clip_norm == 40.0
+
+
+def test_unknown_algorithm_name_raises_in_registry():
+    """The ChoiceRegistry is the source of truth for unknown algorithm names."""
+    with pytest.raises(KeyError):
+        RLAlgorithmConfig.get_choice_class("unknown_algo")
+
+
+# ===========================================================================
+# load_weights (round-trip with get_weights)
+# ===========================================================================
+
+
+def test_load_weights_round_trip():
+    """get_weights -> load_weights should restore identical parameters on a fresh policy."""
+    algo_src, _ = _make_algorithm(state_dim=10, action_dim=6)
+    algo_src.update(_batch_iterator())
+
+    sac_cfg = _make_sac_config(state_dim=10, action_dim=6)
+    policy_dst = GaussianActorPolicy(config=sac_cfg)
+    algo_dst = SACAlgorithm(policy=policy_dst, config=algo_src.config)
+
+    weights = algo_src.get_weights()
+    algo_dst.load_weights(weights, device="cpu")
+
+    dst_actor_state_dict = algo_dst.policy.actor.state_dict()
+    for key, tensor in weights["policy"].items():
+        assert torch.equal(
+            dst_actor_state_dict[key].cpu(),
+            tensor.cpu(),
+        ), f"Policy param '{key}' mismatch after load_weights"
+
+
+def test_load_weights_round_trip_with_discrete_critic():
+    algo_src, _ = _make_algorithm(num_discrete_actions=3, action_dim=6)
+    algo_src.update(_batch_iterator(action_dim=7))
+
+    sac_cfg = _make_sac_config(num_discrete_actions=3, action_dim=6)
+    policy_dst = GaussianActorPolicy(config=sac_cfg)
+    algo_dst = SACAlgorithm(policy=policy_dst, config=algo_src.config)
+
+    weights = algo_src.get_weights()
+    algo_dst.load_weights(weights, device="cpu")
+
+    assert "discrete_critic" in weights
+    assert len(weights["discrete_critic"]) > 0
+    dst_discrete_critic_state_dict = algo_dst.policy.discrete_critic.state_dict()
+    for key, tensor in weights["discrete_critic"].items():
+        assert torch.equal(
+            dst_discrete_critic_state_dict[key].cpu(),
+            tensor.cpu(),
+        ), f"Discrete critic param '{key}' mismatch after load_weights"
+
+
+def test_load_weights_ignores_missing_discrete_critic():
+    """load_weights should not fail when weights lack discrete_critic on a non-discrete policy."""
+    algorithm, _ = _make_algorithm()
+    weights = algorithm.get_weights()
+    algorithm.load_weights(weights, device="cpu")
+
+
+def test_actor_side_weight_sync_with_discrete_critic():
+    """End-to-end: learner ``algorithm.get_weights()`` -> actor ``policy.load_actor_weights()``."""
+    # Learner side: train the algorithm so its weights diverge from init.
+    algo_src, _ = _make_algorithm(num_discrete_actions=3, action_dim=6)
+    algo_src.update(_batch_iterator(action_dim=7))
+    weights = algo_src.get_weights()
+
+    # Actor side: fresh policy, no algorithm/optimizer.
+    sac_cfg = _make_sac_config(num_discrete_actions=3, action_dim=6)
+    policy_actor = GaussianActorPolicy(config=sac_cfg)
+
+    # Snapshot initial actor state for the "did it change?" assertion below.
+    initial_discrete_critic_state_dict = {
+        k: v.clone() for k, v in policy_actor.discrete_critic.state_dict().items()
+    }
+
+    policy_actor.load_actor_weights(weights, device="cpu")
+
+    # Actor weights match the learner's exported actor state dict.
+    actor_state_dict = policy_actor.actor.state_dict()
+    for key, tensor in weights["policy"].items():
+        assert torch.equal(actor_state_dict[key].cpu(), tensor.cpu()), (
+            f"Actor param '{key}' not synced by load_actor_weights"
+        )
+
+    # Discrete critic weights match the learner's exported discrete critic.
+    discrete_critic_state_dict = policy_actor.discrete_critic.state_dict()
+    for key, tensor in weights["discrete_critic"].items():
+        assert torch.equal(discrete_critic_state_dict[key].cpu(), tensor.cpu()), (
+            f"Discrete critic param '{key}' not synced by load_actor_weights"
+        )
+
+    # Sanity: the discrete critic actually changed (otherwise the sync is trivial).
+    changed = any(
+        not torch.equal(initial_discrete_critic_state_dict[key], discrete_critic_state_dict[key])
+        for key in initial_discrete_critic_state_dict
+        if key in discrete_critic_state_dict
+    )
+    assert changed, "Discrete critic weights did not change between init and after sync"
+
+
+# ===========================================================================
+# TrainingStats generic losses dict
+# ===========================================================================
+
+
+def test_training_stats_generic_losses():
+    stats = TrainingStats(
+        losses={"loss_bc": 0.5, "loss_q": 1.2},
+        extra={"temperature": 0.1},
+    )
+    assert stats.losses["loss_bc"] == 0.5
+    assert stats.losses["loss_q"] == 1.2
+    assert stats.extra["temperature"] == 0.1
+
+
+# ===========================================================================
+# Registry-driven make_algorithm
+# ===========================================================================
+
+
+def test_make_algorithm_builds_sac():
+    """make_algorithm should look up the SAC class from the registry and instantiate it."""
+    sac_cfg = _make_sac_config()
+    algo_config = SACAlgorithmConfig.from_policy_config(sac_cfg)
+    algo_config.utd_ratio = 2
+    policy = GaussianActorPolicy(config=sac_cfg)
+
+    algorithm = make_algorithm(cfg=algo_config, policy=policy)
+    assert isinstance(algorithm, SACAlgorithm)
+    assert algorithm.config.utd_ratio == 2

tests/rl/test_trainer.py

@@ -0,0 +1,127 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pytest
+
+pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
+
+import torch  # noqa: E402
+from torch import Tensor  # noqa: E402
+
+from lerobot.rl.algorithms.base import RLAlgorithm  # noqa: E402
+from lerobot.rl.algorithms.configs import TrainingStats  # noqa: E402
+from lerobot.rl.trainer import RLTrainer  # noqa: E402
+from lerobot.utils.constants import ACTION, OBS_STATE  # noqa: E402
+
+
+class _DummyRLAlgorithmConfig:
+    """Dummy config for testing."""
+
+
+class _DummyRLAlgorithm(RLAlgorithm):
+    config_class = _DummyRLAlgorithmConfig
+    name = "dummy_rl_algorithm"
+
+    def __init__(self):
+        self.configure_calls = 0
+        self.update_calls = 0
+
+    def select_action(self, observation: dict[str, Tensor]) -> Tensor:
+        return torch.zeros(1)
+
+    def configure_data_iterator(
+        self,
+        data_mixer,
+        batch_size: int,
+        *,
+        async_prefetch: bool = True,
+        queue_size: int = 2,
+    ):
+        self.configure_calls += 1
+        return data_mixer.get_iterator(
+            batch_size=batch_size,
+            async_prefetch=async_prefetch,
+            queue_size=queue_size,
+        )
+
+    def make_optimizers_and_scheduler(self):
+        return {}
+
+    def update(self, batch_iterator):
+        self.update_calls += 1
+        _ = next(batch_iterator)
+        return TrainingStats(losses={"dummy": 1.0})
+
+    def load_weights(self, weights, device="cpu") -> None:
+        _ = (weights, device)
+
+
+class _SimpleMixer:
+    def get_iterator(self, batch_size: int, async_prefetch: bool = True, queue_size: int = 2):
+        _ = (async_prefetch, queue_size)
+        while True:
+            yield {
+                "state": {OBS_STATE: torch.randn(batch_size, 3)},
+                ACTION: torch.randn(batch_size, 2),
+                "reward": torch.randn(batch_size),
+                "next_state": {OBS_STATE: torch.randn(batch_size, 3)},
+                "done": torch.zeros(batch_size),
+                "truncated": torch.zeros(batch_size),
+                "complementary_info": None,
+            }
+
+
+def test_trainer_lazy_iterator_lifecycle_and_reset():
+    algo = _DummyRLAlgorithm()
+    mixer = _SimpleMixer()
+    trainer = RLTrainer(algorithm=algo, data_mixer=mixer, batch_size=4)
+
+    # First call builds iterator once.
+    trainer.training_step()
+    assert algo.configure_calls == 1
+    assert algo.update_calls == 1
+
+    # Second call reuses existing iterator.
+    trainer.training_step()
+    assert algo.configure_calls == 1
+    assert algo.update_calls == 2
+
+    # Explicit reset forces lazy rebuild on next step.
+    trainer.reset_data_iterator()
+    trainer.training_step()
+    assert algo.configure_calls == 2
+    assert algo.update_calls == 3
+
+
+def test_trainer_set_data_mixer_resets_by_default():
+    algo = _DummyRLAlgorithm()
+    mixer_a = _SimpleMixer()
+    mixer_b = _SimpleMixer()
+    trainer = RLTrainer(algorithm=algo, data_mixer=mixer_a, batch_size=2)
+
+    trainer.training_step()
+    assert algo.configure_calls == 1
+
+    trainer.set_data_mixer(mixer_b, reset=True)
+    trainer.training_step()
+    assert algo.configure_calls == 2
+
+
+def test_algorithm_optimization_step_contract_defaults():
+    algo = _DummyRLAlgorithm()
+    assert algo.optimization_step == 0
+    algo.optimization_step = 11
+    assert algo.optimization_step == 11

tests/utils/test_process.py

@@ -22,7 +22,7 @@ from unittest.mock import patch
 
 import pytest
 
-pytest.importorskip("grpc")
+pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
 
 from lerobot.utils.process import ProcessSignalHandler  # noqa: E402
 

tests/utils/test_replay_buffer.py

@@ -19,7 +19,6 @@ from collections.abc import Callable
 
 import pytest
 
-pytest.importorskip("grpc")
 pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
 
 import torch  # noqa: E402