diff --git a/src/lerobot/policies/dsrl/modeling_dsrl.py b/src/lerobot/policies/dsrl/modeling_dsrl.py
index 0faf3ee73..c1c431947 100644
--- a/src/lerobot/policies/dsrl/modeling_dsrl.py
+++ b/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(),
diff --git a/src/lerobot/policies/factory.py b/src/lerobot/policies/factory.py
index ac76baf9f..76dd0c84b 100644
--- a/src/lerobot/policies/factory.py
+++ b/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.pi0.configuration_pi0 import PI0Config
 from lerobot.policies.pi0fast.configuration_pi0fast import PI0FASTConfig
 from lerobot.policies.pi05.configuration_pi05 import PI05Config
@@ -58,7 +59,7 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:
 
     Args:
         name: The name of the policy. Supported names are "tdmpc", "diffusion", "act",
-              "vqbet", "pi0", "pi0fast", "sac", "reward_classifier", "smolvla".
+              "vqbet", "pi0", "pi0fast", "sac", "reward_classifier", "smolvla", "dsrl".
 
     Returns:
         The policy class corresponding to the given name.
@@ -106,6 +107,10 @@ 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
     else:
         raise NotImplementedError(f"Policy with name {name} is not implemented.")
 
@@ -120,7 +125,7 @@ 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", "pi0fast", "sac", "smolvla",
-                     "reward_classifier".
+                     "reward_classifier", "dsrl".
         **kwargs: Keyword arguments to be passed to the configuration class constructor.
 
     Returns:
@@ -149,6 +154,8 @@ 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)
     else:
         raise ValueError(f"Policy type '{policy_type}' is not available.")
 
@@ -307,6 +314,13 @@ 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"),
+        )
 
     else:
         raise NotImplementedError(f"Processor for policy type '{policy_cfg.type}' is not implemented.")