chore(docs): update doctrines pipeline files (#1872)

* docs(processor): update docstrings batch_processor

* docs(processor): update docstrings device_processor

* docs(processor): update docstrings tokenizer_processor

* update docstrings processor_act

* update docstrings for pipeline_features

* update docstrings for utils

* update docstring for processor_diffusion

* update docstrings factory

* add docstrings to pi0 processor

* add docstring to pi0fast processor

* add docstring classifier processor

* add docstring to sac processor

* add docstring smolvla processor

* add docstring to tdmpc processor

* add docstring to vqbet processor

* add docstrings to converters

* add docstrings for delta_action_processor

* add docstring to gym action processor

* update hil processor

* add docstring to joint obs processor

* add docstring to migrate_normalize_processor

* update docstrings normalize processor

* update docstring normalize processor

* update docstrings observation processor

* update docstrings rename_processor

* add docstrings robot_kinematic_processor

* cleanup rl comments

* add docstring to train.py

* add docstring to teleoperate.py

* add docstrings to phone_processor.py

* add docstrings to teleop_phone.py

* add docstrings to control_utils.py

* add docstrings to visualization_utils.py

---------

Co-authored-by: Pepijn <pepijn@huggingface.co>

src/lerobot/scripts/rl/actor.py

@@ -98,9 +98,7 @@ from lerobot.utils.utils import (
 
 ACTOR_SHUTDOWN_TIMEOUT = 30
 
-#################################################
-# Main entry point #
-#################################################
+# Main entry point
 
 
 @parser.wrap()
@@ -207,9 +205,7 @@ def actor_cli(cfg: TrainRLServerPipelineConfig):
     logging.info("[ACTOR] queues closed")
 
 
-#################################################
-# Core algorithm functions #
-#################################################
+# Core algorithm functions
 
 
 def act_with_policy(
@@ -406,9 +402,7 @@ def act_with_policy(
             busy_wait(1 / cfg.env.fps - dt_time)
 
 
-#################################################
-#  Communication Functions - Group all gRPC/messaging functions  #
-#################################################
+#  Communication Functions - Group all gRPC/messaging functions
 
 
 def establish_learner_connection(
@@ -653,9 +647,7 @@ def interactions_stream(
     return services_pb2.Empty()
 
 
-#################################################
-#  Policy functions #
-#################################################
+#  Policy functions
 
 
 def update_policy_parameters(policy: SACPolicy, parameters_queue: Queue, device):
@@ -687,9 +679,7 @@ def update_policy_parameters(policy: SACPolicy, parameters_queue: Queue, device)
             logging.info("[ACTOR] Loaded discrete critic parameters from Learner.")
 
 
-#################################################
-#  Utilities functions #
-#################################################
+#  Utilities functions
 
 
 def push_transitions_to_transport_queue(transitions: list, transitions_queue):

src/lerobot/scripts/rl/learner.py

@@ -103,11 +103,6 @@ from lerobot.utils.wandb_utils import WandBLogger
 LOG_PREFIX = "[LEARNER]"
 
 
-#################################################
-# MAIN ENTRY POINTS AND CORE ALGORITHM FUNCTIONS #
-#################################################
-
-
 @parser.wrap()
 def train_cli(cfg: TrainRLServerPipelineConfig):
     if not use_threads(cfg):
@@ -250,9 +245,7 @@ def start_learner_threads(
     logging.info("[LEARNER] queues closed")
 
 
-#################################################
-# Core algorithm functions #
-#################################################
+# Core algorithm functions
 
 
 def add_actor_information_and_train(
@@ -820,9 +813,7 @@ def make_optimizers_and_scheduler(cfg: TrainRLServerPipelineConfig, policy: nn.M
     return optimizers, lr_scheduler
 
 
-#################################################
-# Training setup functions #
-#################################################
+# Training setup functions
 
 
 def handle_resume_logic(cfg: TrainRLServerPipelineConfig) -> TrainRLServerPipelineConfig:
@@ -1023,9 +1014,7 @@ def initialize_offline_replay_buffer(
     return offline_replay_buffer
 
 
-#################################################
-# Utilities/Helpers functions #
-#################################################
+# Utilities/Helpers functions
 
 
 def get_observation_features(

src/lerobot/scripts/train.py

@@ -65,6 +65,28 @@ def update_policy(
     use_amp: bool = False,
     lock=None,
 ) -> tuple[MetricsTracker, dict]:
+    """
+    Performs a single training step to update the policy's weights.
+
+    This function executes the forward and backward passes, clips gradients, and steps the optimizer and
+    learning rate scheduler. It also handles mixed-precision training via a GradScaler.
+
+    Args:
+        train_metrics: A MetricsTracker instance to record training statistics.
+        policy: The policy model to be trained.
+        batch: A batch of training data.
+        optimizer: The optimizer used to update the policy's parameters.
+        grad_clip_norm: The maximum norm for gradient clipping.
+        grad_scaler: The GradScaler for automatic mixed-precision training.
+        lr_scheduler: An optional learning rate scheduler.
+        use_amp: A boolean indicating whether to use automatic mixed precision.
+        lock: An optional lock for thread-safe optimizer updates.
+
+    Returns:
+        A tuple containing:
+        - The updated MetricsTracker with new statistics for this step.
+        - A dictionary of outputs from the policy's forward pass, for logging purposes.
+    """
     start_time = time.perf_counter()
     device = get_device_from_parameters(policy)
     policy.train()
@@ -108,6 +130,20 @@ def update_policy(
 
 @parser.wrap()
 def train(cfg: TrainPipelineConfig):
+    """
+    Main function to train a policy.
+
+    This function orchestrates the entire training pipeline, including:
+    - Setting up logging, seeding, and device configuration.
+    - Creating the dataset, evaluation environment (if applicable), policy, and optimizer.
+    - Handling resumption from a checkpoint.
+    - Running the main training loop, which involves fetching data batches and calling `update_policy`.
+    - Periodically logging metrics, saving model checkpoints, and evaluating the policy.
+    - Pushing the final trained model to the Hugging Face Hub if configured.
+
+    Args:
+        cfg: A `TrainPipelineConfig` object containing all training configurations.
+    """
     cfg.validate()
     logging.info(pformat(cfg.to_dict()))