disable processor for sac/hilserl

src/lerobot/rl/learner.py

@@ -64,7 +64,7 @@ from lerobot.configs import parser
 from lerobot.configs.train import TrainRLServerPipelineConfig
 from lerobot.datasets.factory import make_dataset
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.policies.factory import make_policy, make_pre_post_processors
+from lerobot.policies.factory import make_policy
 from lerobot.rl.algorithms import make_algorithm
 from lerobot.rl.buffer import ReplayBuffer
 from lerobot.rl.data_sources import OnlineOfflineMixer
@@ -321,35 +321,8 @@ def add_actor_information_and_train(
         algorithm_name=cfg.algorithm,
     )
 
-    # Build policy preprocessor for batch normalization during training
-    processor_kwargs = {}
-    postprocessor_kwargs = {}
-    if (cfg.policy.pretrained_path and not cfg.resume) or not cfg.policy.pretrained_path:
-        processor_kwargs["dataset_stats"] = cfg.policy.dataset_stats
-
-    if cfg.policy.pretrained_path is not None:
-        processor_kwargs["preprocessor_overrides"] = {
-            "device_processor": {"device": device.type},
-            "normalizer_processor": {
-                "stats": cfg.policy.dataset_stats,
-                "features": {**policy.config.input_features, **policy.config.output_features},
-                "norm_map": policy.config.normalization_mapping,
-            },
-        }
-        postprocessor_kwargs["postprocessor_overrides"] = {
-            "unnormalizer_processor": {
-                "stats": cfg.policy.dataset_stats,
-                "features": policy.config.output_features,
-                "norm_map": policy.config.normalization_mapping,
-            },
-        }
-
-    preprocessor, postprocessor = make_pre_post_processors(
-        policy_cfg=cfg.policy,
-        pretrained_path=cfg.policy.pretrained_path,
-        **processor_kwargs,
-        **postprocessor_kwargs,
-    )
+    # TODO: Re-enable processor pipeline once refactoring is validated against main
+    preprocessor, postprocessor = None, None
 
     # Push initial policy weights to actors (same path as periodic push)
     state_bytes = state_to_bytes(algorithm.get_weights())