refactor(rl): make algorithm a nested config so all SAC hyperparameters are JSON-addressable

src/lerobot/configs/train.py

@@ -207,18 +207,3 @@ class TrainPipelineConfig(HubMixin):
         cli_args = kwargs.pop("cli_args", [])
         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
-
-    # Algorithm name registered in RLAlgorithmConfig registry
-    algorithm: str = "sac"
-
-    # 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

src/lerobot/rl/actor.py

@@ -60,11 +60,11 @@ 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 PreTrainedPolicy, make_policy, make_pre_post_processors
 from lerobot.processor import TransitionKey
 from lerobot.rl.process import ProcessSignalHandler
 from lerobot.rl.queue import get_last_item_from_queue
+from lerobot.rl.train_rl import TrainRLServerPipelineConfig
 from lerobot.robots import so_follower  # noqa: F401
 from lerobot.teleoperators import gamepad, so_leader  # noqa: F401
 from lerobot.teleoperators.utils import TeleopEvents

src/lerobot/rl/algorithms/factory.py

@@ -20,16 +20,5 @@ from lerobot.rl.algorithms.base import RLAlgorithm
 from lerobot.rl.algorithms.configs import RLAlgorithmConfig
 
 
-def make_algorithm(
+def make_algorithm(cfg: RLAlgorithmConfig, policy: torch.nn.Module) -> RLAlgorithm:
-    policy: torch.nn.Module,
+    return cfg.build_algorithm(policy)
-    policy_cfg,
-    *,
-    algorithm_name: str,
-) -> RLAlgorithm:
-    known = RLAlgorithmConfig.get_known_choices()
-    if algorithm_name not in known:
-        raise ValueError(f"No RLAlgorithmConfig registered for '{algorithm_name}'. Known: {list(known)}")
-
-    config_cls = RLAlgorithmConfig.get_choice_class(algorithm_name)
-    algo_config = config_cls.from_policy_config(policy_cfg)
-    return algo_config.build_algorithm(policy)

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

@@ -34,9 +34,6 @@ if TYPE_CHECKING:
 class SACAlgorithmConfig(RLAlgorithmConfig):
     """SAC algorithm hyperparameters."""
 
-    # Policy config
-    policy_config: GaussianActorConfig
-
     # Optimizer learning rates
     actor_lr: float = 3e-4
     critic_lr: float = 3e-4
@@ -69,6 +66,9 @@ class SACAlgorithmConfig(RLAlgorithmConfig):
     # 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."""
@@ -78,6 +78,13 @@ class SACAlgorithmConfig(RLAlgorithmConfig):
         )
 
     def build_algorithm(self, policy: torch.nn.Module) -> SACAlgorithm:
+        if self.policy_config is None:
+            raise ValueError(
+                "SACAlgorithmConfig.policy_config is None. "
+                "It must be populated (typically by TrainRLServerPipelineConfig.validate) "
+                "before calling build_algorithm()."
+            )
+
         from lerobot.rl.algorithms.sac.sac_algorithm import SACAlgorithm
 
         return SACAlgorithm(policy=policy, config=self)

src/lerobot/rl/eval_policy.py

@@ -17,9 +17,9 @@ 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.rl.train_rl import TrainRLServerPipelineConfig
 from lerobot.robots import (  # noqa: F401
     RobotConfig,
     make_robot_from_config,

src/lerobot/rl/learner.py

@@ -69,7 +69,6 @@ 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, make_pre_post_processors
 from lerobot.rl.algorithms.base import RLAlgorithm
@@ -77,6 +76,7 @@ from lerobot.rl.algorithms.factory import make_algorithm
 from lerobot.rl.buffer import ReplayBuffer
 from lerobot.rl.data_sources import OnlineOfflineMixer
 from lerobot.rl.process import ProcessSignalHandler
+from lerobot.rl.train_rl import TrainRLServerPipelineConfig
 from lerobot.rl.trainer import RLTrainer
 from lerobot.robots import so_follower  # noqa: F401
 from lerobot.teleoperators import gamepad, so_leader  # noqa: F401
@@ -316,11 +316,7 @@ def add_actor_information_and_train(
 
     policy.train()
 
-    algorithm = make_algorithm(
+    algorithm = make_algorithm(cfg=cfg.algorithm, policy=policy)
-        policy=policy,
-        policy_cfg=cfg.policy,
-        algorithm_name=cfg.algorithm,
-    )
 
     preprocessor, postprocessor = make_pre_post_processors(
         policy_cfg=cfg.policy,

src/lerobot/rl/train_rl.py

@@ -0,0 +1,54 @@
+# 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 lerobot.rl.algorithms.configs import RLAlgorithmConfig
+from lerobot.rl.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 (a `draccus.ChoiceRegistry` subclass selected by `type`,
+    # e.g. ``"type": "sac"``). When omitted, defaults to a SAC config with
+    # default hyperparameters. The top-level `policy` is injected into
+    # ``algorithm.policy_config`` at validation time.
+    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:
+            sac_cls = RLAlgorithmConfig.get_choice_class("sac")
+            self.algorithm = sac_cls()
+
+        # The pipeline owns the policy config; inject it so the algorithm can
+        # introspect policy architecture (e.g. ``num_discrete_actions``).
+        if getattr(self.algorithm, "policy_config", None) is None:
+            self.algorithm.policy_config = self.policy

tests/rl/test_actor_learner.py

@@ -26,9 +26,9 @@ pytest.importorskip("grpc")
 
 from torch.multiprocessing import Event, Queue
 
-from lerobot.configs.train import TrainRLServerPipelineConfig
 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
@@ -314,7 +314,7 @@ def test_learner_algorithm_wiring():
     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
+    from lerobot.rl.algorithms.sac import SACAlgorithm, SACAlgorithmConfig
     from lerobot.transport.utils import state_to_bytes
 
     state_dim = 10
@@ -333,7 +333,7 @@ def test_learner_algorithm_wiring():
     policy = GaussianActorPolicy(config=sac_cfg)
     policy.train()
 
-    algorithm = make_algorithm(policy=policy, policy_cfg=sac_cfg, algorithm_name="sac")
+    algorithm = make_algorithm(cfg=SACAlgorithmConfig.from_policy_config(sac_cfg), policy=policy)
     assert isinstance(algorithm, SACAlgorithm)
 
     optimizers = algorithm.make_optimizers_and_scheduler()
@@ -400,6 +400,7 @@ def test_initial_and_periodic_weight_push_consistency():
     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
@@ -416,7 +417,7 @@ def test_initial_and_periodic_weight_push_consistency():
 
     policy = GaussianActorPolicy(config=sac_cfg)
     policy.train()
-    algorithm = make_algorithm(policy=policy, policy_cfg=sac_cfg, algorithm_name="sac")
+    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)
@@ -437,7 +438,7 @@ 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
+    from lerobot.rl.algorithms.sac import SACAlgorithm, SACAlgorithmConfig
 
     state_dim = 10
     action_dim = 6
@@ -454,7 +455,7 @@ def test_actor_side_algorithm_select_action_and_load_weights():
     # Actor side: no optimizers
     policy = GaussianActorPolicy(config=sac_cfg)
     policy.eval()
-    algorithm = make_algorithm(policy=policy, policy_cfg=sac_cfg, algorithm_name="sac")
+    algorithm = make_algorithm(cfg=SACAlgorithmConfig.from_policy_config(sac_cfg), policy=policy)
     assert isinstance(algorithm, SACAlgorithm)
     assert algorithm.optimizers == {}
 

tests/rl/test_sac_algorithm.py

@@ -348,7 +348,7 @@ def test_optimization_step_can_be_set_for_resume():
 def test_make_algorithm_returns_sac_for_sac_policy():
     sac_cfg = _make_sac_config()
     policy = GaussianActorPolicy(config=sac_cfg)
-    algorithm = make_algorithm(policy=policy, policy_cfg=sac_cfg, algorithm_name="sac")
+    algorithm = make_algorithm(cfg=SACAlgorithmConfig.from_policy_config(sac_cfg), policy=policy)
     assert isinstance(algorithm, SACAlgorithm)
     assert algorithm.optimizers == {}
 
@@ -357,7 +357,7 @@ 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(policy=policy, policy_cfg=sac_cfg, algorithm_name="sac")
+    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
@@ -370,7 +370,7 @@ 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(policy=policy, policy_cfg=sac_cfg, algorithm_name="sac")
+    algorithm = make_algorithm(cfg=SACAlgorithmConfig.from_policy_config(sac_cfg), policy=policy)
     assert isinstance(algorithm, SACAlgorithm)
     assert algorithm.optimizers == {}
 
@@ -379,18 +379,16 @@ 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(policy=policy, policy_cfg=sac_cfg, algorithm_name="sac")
+    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_make_algorithm_raises_for_unknown_type():
+def test_unknown_algorithm_name_raises_in_registry():
-    class FakeConfig:
+    """The ChoiceRegistry is the source of truth for unknown algorithm names."""
-        type = "unknown_algo"
+    with pytest.raises(KeyError):
-
+        RLAlgorithmConfig.get_choice_class("unknown_algo")
-    with pytest.raises(ValueError, match="No RLAlgorithmConfig"):
-        make_algorithm(policy=None, policy_cfg=FakeConfig(), algorithm_name="unknown_algo")
 
 
 # ===========================================================================
@@ -523,5 +521,5 @@ def test_make_algorithm_uses_build_algorithm():
     """make_algorithm should delegate to config.build_algorithm (no hardcoded if/else)."""
     sac_cfg = _make_sac_config()
     policy = GaussianActorPolicy(config=sac_cfg)
-    algorithm = make_algorithm(policy=policy, policy_cfg=sac_cfg, algorithm_name="sac")
+    algorithm = make_algorithm(cfg=SACAlgorithmConfig.from_policy_config(sac_cfg), policy=policy)
     assert isinstance(algorithm, SACAlgorithm)