update factory with dsrl

src/lerobot/policies/dsrl/modeling_dsrl.py

@@ -249,7 +249,7 @@ class DSRLPolicy(PreTrainedPolicy):
         raise ValueError(f"Unknown model type: {model}")
 
     def update_target_networks(self):
-        """Update target networks with exponential moving average"""
+        """Update target networks of the action critic with exponential moving average"""
         for target_param, param in zip(
             self.action_critic_target.parameters(),
             self.action_critic_ensemble.parameters(),

src/lerobot/policies/factory.py

@@ -30,6 +30,7 @@ from lerobot.envs.configs import EnvConfig
 from lerobot.envs.utils import env_to_policy_features
 from lerobot.policies.act.configuration_act import ACTConfig
 from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
+from lerobot.policies.dsrl.configuration_dsrl import DSRLConfig
 from lerobot.policies.groot.configuration_groot import GrootConfig
 from lerobot.policies.pi0.configuration_pi0 import PI0Config
 from lerobot.policies.pi05.configuration_pi05 import PI05Config
@@ -59,7 +60,7 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:
 
     Args:
         name: The name of the policy. Supported names are "tdmpc", "diffusion", "act",
-              "vqbet", "pi0", "pi05", "sac", "reward_classifier", "smolvla".
+              "vqbet", "pi0", "pi05", "sac", "reward_classifier", "smolvla", "dsrl".
 
     Returns:
         The policy class corresponding to the given name.
@@ -103,6 +104,10 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:
         from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
 
         return SmolVLAPolicy
+    elif name == "dsrl":
+        from lerobot.policies.dsrl.modeling_dsrl import DSRLPolicy
+
+        return DSRLPolicy
     elif name == "groot":
         from lerobot.policies.groot.modeling_groot import GrootPolicy
 
@@ -121,7 +126,7 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
     Args:
         policy_type: The type of the policy. Supported types include "tdmpc",
                      "diffusion", "act", "vqbet", "pi0", "pi05", "sac", "smolvla",
-                     "reward_classifier".
+                     "reward_classifier", "dsrl".
         **kwargs: Keyword arguments to be passed to the configuration class constructor.
 
     Returns:
@@ -148,6 +153,8 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
         return SmolVLAConfig(**kwargs)
     elif policy_type == "reward_classifier":
         return RewardClassifierConfig(**kwargs)
+    elif policy_type == "dsrl":
+        return DSRLConfig(**kwargs)
     elif policy_type == "groot":
         return GrootConfig(**kwargs)
     else:
@@ -321,6 +328,21 @@ def make_pre_post_processors(
             config=policy_cfg,
             dataset_stats=kwargs.get("dataset_stats"),
         )
+    elif isinstance(policy_cfg, DSRLConfig):
+        from lerobot.policies.dsrl.processor_dsrl import make_dsrl_pre_post_processors
+
+        processors = make_dsrl_pre_post_processors(
+            config=policy_cfg,
+            dataset_stats=kwargs.get("dataset_stats"),
+        )
+
+    elif isinstance(policy_cfg, GrootConfig):
+        from lerobot.policies.groot.processor_groot import make_groot_pre_post_processors
+
+        processors = make_groot_pre_post_processors(
+            config=policy_cfg,
+            dataset_stats=kwargs.get("dataset_stats"),
+        )
 
     elif isinstance(policy_cfg, GrootConfig):
         from lerobot.policies.groot.processor_groot import make_groot_pre_post_processors