Training2 / src /lerobot /rl /learner.py
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# !/usr/bin/env python

# Copyright 2025 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.
"""
Learner server runner for distributed HILSerl robot policy training.
This script implements the learner component of the distributed HILSerl architecture.
It initializes the policy network, maintains replay buffers, and updates
the policy based on transitions received from the actor server.
Examples of usage:
- Start a learner server for training:
```bash
python -m lerobot.rl.learner --config_path src/lerobot/configs/train_config_hilserl_so100.json
```
**NOTE**: Start the learner server before launching the actor server. The learner opens a gRPC server
to communicate with actors.
**NOTE**: Training progress can be monitored through Weights & Biases if wandb.enable is set to true
in your configuration.
**WORKFLOW**:
1. Create training configuration with proper policy, dataset, and environment settings
2. Start this learner server with the configuration
3. Start an actor server with the same configuration
4. Monitor training progress through wandb dashboard
For more details on the complete HILSerl training workflow, see:
https://github.com/michel-aractingi/lerobot-hilserl-guide
"""

import logging
import os
import shutil
import time
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from pprint import pformat
from typing import TYPE_CHECKING, Any

from lerobot.utils.import_utils import _grpc_available, require_package

if TYPE_CHECKING or _grpc_available:
    import grpc

    from lerobot.transport import services_pb2_grpc
else:
    grpc = None
    services_pb2_grpc = None

import torch
from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE
from safetensors.torch import load_file as load_safetensors
from termcolor import colored
from torch import nn
from torch.multiprocessing import Queue
from torch.optim.optimizer import Optimizer

from lerobot.cameras import opencv  # noqa: F401
from lerobot.common.train_utils import (
    get_step_checkpoint_dir,
    load_training_state as utils_load_training_state,
    save_checkpoint,
    update_last_checkpoint,
)
from lerobot.common.wandb_utils import WandBLogger
from lerobot.configs import parser
from lerobot.datasets import LeRobotDataset, make_dataset
from lerobot.policies import make_policy, make_pre_post_processors
from lerobot.robots import so_follower  # noqa: F401
from lerobot.teleoperators import gamepad, so_leader  # noqa: F401
from lerobot.teleoperators.utils import TeleopEvents
from lerobot.transport.utils import (
    MAX_MESSAGE_SIZE,
    bytes_to_python_object,
    bytes_to_transitions,
    state_to_bytes,
)
from lerobot.utils.constants import (
    ACTION,
    ALGORITHM_DIR,
    CHECKPOINTS_DIR,
    LAST_CHECKPOINT_LINK,
    PRETRAINED_MODEL_DIR,
    TRAINING_STATE_DIR,
    TRAINING_STEP,
)
from lerobot.utils.device_utils import get_safe_torch_device
from lerobot.utils.io_utils import load_json, write_json
from lerobot.utils.process import ProcessSignalHandler
from lerobot.utils.random_utils import set_seed
from lerobot.utils.utils import (
    format_big_number,
    init_logging,
)

from .algorithms.base import RLAlgorithm
from .algorithms.factory import make_algorithm
from .buffer import ReplayBuffer
from .data_sources import OnlineOfflineMixer
from .learner_service import MAX_WORKERS, SHUTDOWN_TIMEOUT, LearnerService
from .train_rl import TrainRLServerPipelineConfig
from .trainer import RLTrainer


@parser.wrap()
def train_cli(cfg: TrainRLServerPipelineConfig):
    # Fail fast with a friendly error if the optional ``hilserl`` extra is missing.
    require_package("grpcio", extra="hilserl", import_name="grpc")
    if not use_threads(cfg):
        import torch.multiprocessing as mp

        mp.set_start_method("spawn")

    # Use the job_name from the config
    train(
        cfg,
        job_name=cfg.job_name,
    )

    logging.info("[LEARNER] train_cli finished")


def train(cfg: TrainRLServerPipelineConfig, job_name: str | None = None):
    """
    Main training function that initializes and runs the training process.
    Args:
        cfg (TrainRLServerPipelineConfig): The training configuration
        job_name (str | None, optional): Job name for logging. Defaults to None.
    """

    cfg.validate()

    if job_name is None:
        job_name = cfg.job_name

    if job_name is None:
        raise ValueError("Job name must be specified either in config or as a parameter")

    display_pid = False
    if not use_threads(cfg):
        display_pid = True

    # Create logs directory to ensure it exists
    log_dir = os.path.join(cfg.output_dir, "logs")
    os.makedirs(log_dir, exist_ok=True)
    log_file = os.path.join(log_dir, f"learner_{job_name}.log")

    # Initialize logging with explicit log file
    init_logging(log_file=log_file, display_pid=display_pid)
    logging.info(f"Learner logging initialized, writing to {log_file}")
    logging.info(pformat(cfg.to_dict()))

    # Setup WandB logging if enabled
    if cfg.wandb.enable and cfg.wandb.project:
        from lerobot.common.wandb_utils import WandBLogger

        wandb_logger = WandBLogger(cfg)
    else:
        wandb_logger = None
        logging.info(colored("Logs will be saved locally.", "yellow", attrs=["bold"]))

    # Handle resume logic
    cfg = handle_resume_logic(cfg)

    set_seed(seed=cfg.seed)

    torch.backends.cudnn.benchmark = True
    torch.backends.cuda.matmul.allow_tf32 = True

    is_threaded = use_threads(cfg)
    shutdown_event = ProcessSignalHandler(is_threaded, display_pid=display_pid).shutdown_event

    start_learner_threads(
        cfg=cfg,
        wandb_logger=wandb_logger,
        shutdown_event=shutdown_event,
    )


def start_learner_threads(
    cfg: TrainRLServerPipelineConfig,
    wandb_logger: WandBLogger | None,
    shutdown_event: Any,  # Event
) -> None:
    """
    Start the learner threads for training.
    Args:
        cfg (TrainRLServerPipelineConfig): Training configuration
        wandb_logger (WandBLogger | None): Logger for metrics
        shutdown_event: Event to signal shutdown
    """
    # Create multiprocessing queues
    transition_queue = Queue()
    interaction_message_queue = Queue()
    parameters_queue = Queue()

    concurrency_entity = None

    if use_threads(cfg):
        from threading import Thread

        concurrency_entity = Thread
    else:
        from torch.multiprocessing import Process

        concurrency_entity = Process

    communication_process = concurrency_entity(
        target=start_learner,
        args=(
            parameters_queue,
            transition_queue,
            interaction_message_queue,
            shutdown_event,
            cfg,
        ),
        daemon=True,
    )
    communication_process.start()

    try:
        add_actor_information_and_train(
            cfg=cfg,
            wandb_logger=wandb_logger,
            shutdown_event=shutdown_event,
            transition_queue=transition_queue,
            interaction_message_queue=interaction_message_queue,
            parameters_queue=parameters_queue,
        )
        logging.info("[LEARNER] Training process stopped")
    except Exception:
        logging.exception("[LEARNER] Unhandled exception in training loop")
        shutdown_event.set()
    finally:
        logging.info("[LEARNER] Closing queues")
        transition_queue.close()
        interaction_message_queue.close()
        parameters_queue.close()

        communication_process.join()
        logging.info("[LEARNER] Communication process joined")

        transition_queue.cancel_join_thread()
        interaction_message_queue.cancel_join_thread()
        parameters_queue.cancel_join_thread()

        logging.info("[LEARNER] Cleanup complete")


# Core algorithm functions


def add_actor_information_and_train(
    cfg: TrainRLServerPipelineConfig,
    wandb_logger: WandBLogger | None,
    shutdown_event: Any,  # Event
    transition_queue: Queue,
    interaction_message_queue: Queue,
    parameters_queue: Queue,
):
    """
    Handles data transfer from the actor to the learner, manages training updates,
    and logs training progress in an online reinforcement learning setup.
    This function continuously:
    - Transfers transitions from the actor to the replay buffer.
    - Logs received interaction messages.
    - Ensures training begins only when the replay buffer has a sufficient number of transitions.
    - Delegates training updates to an ``RLAlgorithm``.
    - Periodically pushes updated weights to actors.
    - Logs training statistics, including loss values and optimization frequency.
    NOTE: This function doesn't have a single responsibility, it should be split into multiple functions
    in the future. The reason why we did that is the  GIL in Python. It's super slow the performance
    are divided by 200. So we need to have a single thread that does all the work.
    Args:
        cfg (TrainRLServerPipelineConfig): Configuration object containing hyperparameters.
        wandb_logger (WandBLogger | None): Logger for tracking training progress.
        shutdown_event (Event): Event to signal shutdown.
        transition_queue (Queue): Queue for receiving transitions from the actor.
        interaction_message_queue (Queue): Queue for receiving interaction messages from the actor.
        parameters_queue (Queue): Queue for sending policy parameters to the actor.
    """
    # Extract all configuration variables at the beginning, it improve the speed performance
    # of 7%
    device = get_safe_torch_device(try_device=cfg.policy.device, log=True)
    storage_device = get_safe_torch_device(try_device=cfg.policy.storage_device)
    online_step_before_learning = cfg.policy.online_step_before_learning
    fps = cfg.env.fps
    log_freq = cfg.log_freq
    save_freq = cfg.save_freq
    policy_parameters_push_frequency = cfg.policy.actor_learner_config.policy_parameters_push_frequency
    saving_checkpoint = cfg.save_checkpoint
    online_steps = cfg.policy.online_steps

    # Initialize logging for multiprocessing
    if not use_threads(cfg):
        log_dir = os.path.join(cfg.output_dir, "logs")
        os.makedirs(log_dir, exist_ok=True)
        log_file = os.path.join(log_dir, f"learner_train_process_{os.getpid()}.log")
        init_logging(log_file=log_file, display_pid=True)
        logging.info("Initialized logging for actor information and training process")

    logging.info("Initializing policy")

    policy = make_policy(
        cfg=cfg.policy,
        env_cfg=cfg.env,
    )

    assert isinstance(policy, nn.Module)

    policy.train()

    algorithm = make_algorithm(cfg=cfg.algorithm, policy=policy)

    preprocessor, postprocessor = make_pre_post_processors(
        policy_cfg=cfg.policy,
        dataset_stats=cfg.policy.dataset_stats,
    )

    # Push initial policy weights to actors
    push_actor_policy_to_queue(parameters_queue=parameters_queue, algorithm=algorithm)
    last_time_policy_pushed = time.time()

    log_training_info(cfg=cfg, policy=policy)

    replay_buffer = initialize_replay_buffer(cfg, device, storage_device)
    batch_size = cfg.batch_size
    offline_replay_buffer = None

    if cfg.dataset is not None:
        offline_replay_buffer = initialize_offline_replay_buffer(
            cfg=cfg,
            device=device,
            storage_device=storage_device,
        )

    # DataMixer: online-only or online/offline 50-50 mix
    data_mixer = OnlineOfflineMixer(
        online_buffer=replay_buffer,
        offline_buffer=offline_replay_buffer,
        online_ratio=cfg.online_ratio,
    )
    # RLTrainer owns the iterator, preprocessor, and creates optimizers.
    trainer = RLTrainer(
        algorithm=algorithm,
        data_mixer=data_mixer,
        batch_size=batch_size,
        preprocessor=preprocessor,
    )

    # If we are resuming, we need to load the training state
    optimizers = algorithm.get_optimizers()
    resume_optimization_step, resume_interaction_step = load_training_state(
        cfg=cfg, optimizers=optimizers, algorithm=algorithm, device=device
    )

    logging.info("Starting learner thread")
    interaction_message = None
    optimization_step = resume_optimization_step if resume_optimization_step is not None else 0
    algorithm.optimization_step = optimization_step
    interaction_step_shift = resume_interaction_step if resume_interaction_step is not None else 0

    dataset_repo_id = None
    if cfg.dataset is not None:
        dataset_repo_id = cfg.dataset.repo_id

    # NOTE: THIS IS THE MAIN LOOP OF THE LEARNER
    while True:
        # Exit the training loop if shutdown is requested
        if shutdown_event is not None and shutdown_event.is_set():
            logging.info("[LEARNER] Shutdown signal received. Exiting...")
            break

        # Process all available transitions to the replay buffer, send by the actor server
        process_transitions(
            transition_queue=transition_queue,
            replay_buffer=replay_buffer,
            offline_replay_buffer=offline_replay_buffer,
            dataset_repo_id=dataset_repo_id,
            shutdown_event=shutdown_event,
        )

        # Process all available interaction messages sent by the actor server
        interaction_message = process_interaction_messages(
            interaction_message_queue=interaction_message_queue,
            interaction_step_shift=interaction_step_shift,
            wandb_logger=wandb_logger,
            shutdown_event=shutdown_event,
        )

        # Wait until the replay buffer has enough samples to start training
        if len(replay_buffer) < online_step_before_learning:
            continue

        time_for_one_optimization_step = time.time()

        # One training step (trainer owns data_mixer iterator; algorithm owns UTD loop)
        stats = trainer.training_step()

        # Push policy to actors if needed
        if time.time() - last_time_policy_pushed > policy_parameters_push_frequency:
            push_actor_policy_to_queue(parameters_queue=parameters_queue, algorithm=algorithm)
            last_time_policy_pushed = time.time()

        training_infos = stats.to_log_dict()

        # Log training metrics at specified intervals
        optimization_step = algorithm.optimization_step
        if optimization_step % log_freq == 0:
            training_infos["replay_buffer_size"] = len(replay_buffer)
            if offline_replay_buffer is not None:
                training_infos["offline_replay_buffer_size"] = len(offline_replay_buffer)
            training_infos["Optimization step"] = optimization_step

            # Log training metrics
            if wandb_logger:
                wandb_logger.log_dict(d=training_infos, mode="train", custom_step_key="Optimization step")

        # Calculate and log optimization frequency
        time_for_one_optimization_step = time.time() - time_for_one_optimization_step
        frequency_for_one_optimization_step = 1 / (time_for_one_optimization_step + 1e-9)

        logging.info(f"[LEARNER] Optimization frequency loop [Hz]: {frequency_for_one_optimization_step}")

        # Log optimization frequency
        if wandb_logger:
            wandb_logger.log_dict(
                {
                    "Optimization frequency loop [Hz]": frequency_for_one_optimization_step,
                    "Optimization step": optimization_step,
                },
                mode="train",
                custom_step_key="Optimization step",
            )

        if optimization_step % log_freq == 0:
            logging.info(f"[LEARNER] Number of optimization step: {optimization_step}")

        # Save checkpoint at specified intervals
        if saving_checkpoint and (optimization_step % save_freq == 0 or optimization_step == online_steps):
            save_training_checkpoint(
                cfg=cfg,
                optimization_step=optimization_step,
                online_steps=online_steps,
                interaction_message=interaction_message,
                policy=policy,
                optimizers=optimizers,
                replay_buffer=replay_buffer,
                algorithm=algorithm,
                offline_replay_buffer=offline_replay_buffer,
                dataset_repo_id=dataset_repo_id,
                fps=fps,
                preprocessor=preprocessor,
                postprocessor=postprocessor,
            )


def start_learner(
    parameters_queue: Queue,
    transition_queue: Queue,
    interaction_message_queue: Queue,
    shutdown_event: Any,  # Event
    cfg: TrainRLServerPipelineConfig,
):
    """
    Start the learner server for training.
    It will receive transitions and interaction messages from the actor server,
    and send policy parameters to the actor server.
    Args:
        parameters_queue: Queue for sending policy parameters to the actor
        transition_queue: Queue for receiving transitions from the actor
        interaction_message_queue: Queue for receiving interaction messages from the actor
        shutdown_event: Event to signal shutdown
        cfg: Training configuration
    """
    if not use_threads(cfg):
        # Create a process-specific log file
        log_dir = os.path.join(cfg.output_dir, "logs")
        os.makedirs(log_dir, exist_ok=True)
        log_file = os.path.join(log_dir, f"learner_process_{os.getpid()}.log")

        # Initialize logging with explicit log file
        init_logging(log_file=log_file, display_pid=True)
        logging.info("Learner server process logging initialized")

        # Setup process handlers to handle shutdown signal
        # But use shutdown event from the main process
        # Return back for MP
        # TODO: Check if its useful
        _ = ProcessSignalHandler(False, display_pid=True)

    service = LearnerService(
        shutdown_event=shutdown_event,
        parameters_queue=parameters_queue,
        seconds_between_pushes=cfg.policy.actor_learner_config.policy_parameters_push_frequency,
        transition_queue=transition_queue,
        interaction_message_queue=interaction_message_queue,
        queue_get_timeout=cfg.policy.actor_learner_config.queue_get_timeout,
    )

    server = grpc.server(
        ThreadPoolExecutor(max_workers=MAX_WORKERS),
        options=[
            ("grpc.max_receive_message_length", MAX_MESSAGE_SIZE),
            ("grpc.max_send_message_length", MAX_MESSAGE_SIZE),
        ],
    )

    services_pb2_grpc.add_LearnerServiceServicer_to_server(
        service,
        server,
    )

    host = cfg.policy.actor_learner_config.learner_host
    port = cfg.policy.actor_learner_config.learner_port

    server.add_insecure_port(f"{host}:{port}")
    server.start()
    logging.info("[LEARNER] gRPC server started")

    shutdown_event.wait()
    logging.info("[LEARNER] Stopping gRPC server...")
    server.stop(SHUTDOWN_TIMEOUT)
    logging.info("[LEARNER] gRPC server stopped")


def save_training_checkpoint(
    cfg: TrainRLServerPipelineConfig,
    optimization_step: int,
    online_steps: int,
    interaction_message: dict | None,
    policy: nn.Module,
    optimizers: dict[str, Optimizer],
    replay_buffer: ReplayBuffer,
    algorithm: RLAlgorithm | None = None,
    offline_replay_buffer: ReplayBuffer | None = None,
    dataset_repo_id: str | None = None,
    fps: int = 30,
    preprocessor=None,
    postprocessor=None,
) -> None:
    """
    Save training checkpoint and associated data.
    This function performs the following steps:
    1. Creates a checkpoint directory with the current optimization step
    2. Saves the policy model, configuration, and optimizer states
    3. Saves the current interaction step for resuming training
    4. Updates the "last" checkpoint symlink to point to this checkpoint
    5. Saves the replay buffer as a dataset for later use
    6. If an offline replay buffer exists, saves it as a separate dataset
    Args:
        cfg: Training configuration
        optimization_step: Current optimization step
        online_steps: Total number of online steps
        interaction_message: Dictionary containing interaction information
        policy: Policy model to save
        optimizers: Dictionary of optimizers
        replay_buffer: Replay buffer to save as dataset
        offline_replay_buffer: Optional offline replay buffer to save
        dataset_repo_id: Repository ID for dataset
        fps: Frames per second for dataset
        preprocessor: Optional preprocessor pipeline to save
        postprocessor: Optional postprocessor pipeline to save
    """
    logging.info(f"Checkpoint policy after step {optimization_step}")
    _num_digits = max(6, len(str(online_steps)))
    interaction_step = interaction_message["Interaction step"] if interaction_message is not None else 0

    # Create checkpoint directory
    checkpoint_dir = get_step_checkpoint_dir(cfg.output_dir, online_steps, optimization_step)

    # Save policy artifacts (pretrained_model/) + Trainer scaffolding (training_state/).
    save_checkpoint(
        checkpoint_dir=checkpoint_dir,
        step=optimization_step,
        cfg=cfg,
        policy=policy,
        optimizer=optimizers,
        scheduler=None,
        preprocessor=preprocessor,
        postprocessor=postprocessor,
    )

    # Algorithm-owned tensors live in their own component subfolder
    # so they can be `push_to_hub`'d independently and don't bloat the inference artifact.
    if algorithm is not None:
        algorithm.save_pretrained(checkpoint_dir / ALGORITHM_DIR)

    # Enrich training_step.json with the RL-specific interaction_step counter so
    # both can be restored from a single file.
    training_state_dir = checkpoint_dir / TRAINING_STATE_DIR
    write_json(
        {"step": optimization_step, "interaction_step": interaction_step},
        training_state_dir / TRAINING_STEP,
    )

    # Update the "last" symlink
    update_last_checkpoint(checkpoint_dir)

    # TODO : temporary save replay buffer here, remove later when on the robot
    # We want to control this with the keyboard inputs
    dataset_dir = os.path.join(cfg.output_dir, "dataset")
    if os.path.exists(dataset_dir) and os.path.isdir(dataset_dir):
        shutil.rmtree(dataset_dir)

    # Save dataset
    # NOTE: Handle the case where the dataset repo id is not specified in the config
    # eg. RL training without demonstrations data
    repo_id_buffer_save = cfg.env.task if dataset_repo_id is None else dataset_repo_id
    replay_buffer.to_lerobot_dataset(repo_id=repo_id_buffer_save, fps=fps, root=dataset_dir)

    if offline_replay_buffer is not None:
        dataset_offline_dir = os.path.join(cfg.output_dir, "dataset_offline")
        if os.path.exists(dataset_offline_dir) and os.path.isdir(dataset_offline_dir):
            shutil.rmtree(dataset_offline_dir)

        offline_replay_buffer.to_lerobot_dataset(
            cfg.dataset.repo_id,
            fps=fps,
            root=dataset_offline_dir,
        )

    logging.info("Resume training")


# Training setup functions


def handle_resume_logic(cfg: TrainRLServerPipelineConfig) -> TrainRLServerPipelineConfig:
    """
    Handle the resume logic for training.
    If resume is True:
    - Verifies that a checkpoint exists
    - Loads the checkpoint configuration
    - Logs resumption details
    - Returns the checkpoint configuration
    If resume is False:
    - Checks if an output directory exists (to prevent accidental overwriting)
    - Returns the original configuration
    Args:
        cfg (TrainRLServerPipelineConfig): The training configuration
    Returns:
        TrainRLServerPipelineConfig: The updated configuration
    Raises:
        RuntimeError: If resume is True but no checkpoint found, or if resume is False but directory exists
    """
    out_dir = cfg.output_dir

    # Case 1: Not resuming, but need to check if directory exists to prevent overwrites
    if not cfg.resume:
        checkpoint_dir = os.path.join(out_dir, CHECKPOINTS_DIR, LAST_CHECKPOINT_LINK)
        if os.path.exists(checkpoint_dir):
            raise RuntimeError(
                f"Output directory {checkpoint_dir} already exists. Use `resume=true` to resume training."
            )
        return cfg

    # Case 2: Resuming training
    checkpoint_dir = os.path.join(out_dir, CHECKPOINTS_DIR, LAST_CHECKPOINT_LINK)
    if not os.path.exists(checkpoint_dir):
        raise RuntimeError(f"No model checkpoint found in {checkpoint_dir} for resume=True")

    # Log that we found a valid checkpoint and are resuming
    logging.info(
        colored(
            "Valid checkpoint found: resume=True detected, resuming previous run",
            color="yellow",
            attrs=["bold"],
        )
    )

    # Load config using Draccus
    checkpoint_cfg_path = os.path.join(checkpoint_dir, PRETRAINED_MODEL_DIR, "train_config.json")
    checkpoint_cfg = TrainRLServerPipelineConfig.from_pretrained(checkpoint_cfg_path)

    # Ensure resume flag is set in returned config
    checkpoint_cfg.resume = True
    return checkpoint_cfg


def load_training_state(
    cfg: TrainRLServerPipelineConfig,
    optimizers: Optimizer | dict[str, Optimizer],
    algorithm: RLAlgorithm | None = None,
    device: str | torch.device = "cpu",
):
    """
    Loads the training state (optimizers, RNG, step + interaction step, and
    algorithm-owned tensors) from the most recent checkpoint.
    Args:
        cfg: Training configuration.
        optimizers: Optimizers to load state into.
        algorithm: Algorithm whose state dict should be restored.
            Required for full main-equivalent resume;
            the policy itself is restored separately via ``make_policy``.
        device: Device on which to place loaded algorithm tensors.
    Returns:
        tuple: (optimization_step, interaction_step) or (None, None) if not resuming
    """
    if not cfg.resume:
        return None, None

    # Construct path to the last checkpoint directory
    checkpoint_dir = Path(cfg.output_dir) / CHECKPOINTS_DIR / LAST_CHECKPOINT_LINK

    logging.info(f"Loading training state from {checkpoint_dir}")

    try:
        # Restore optimizers + RNG + step from the standard `training_state/` folder
        step, optimizers, _ = utils_load_training_state(checkpoint_dir, optimizers, None)

        # Restore algorithm-owned tensors
        if algorithm is not None:
            algo_dir = checkpoint_dir / ALGORITHM_DIR
            if algo_dir.is_dir():
                tensors = load_safetensors(str(algo_dir / SAFETENSORS_SINGLE_FILE))
                algorithm.load_state_dict(tensors, device=device)
                logging.info(f"Loaded algorithm state from {algo_dir}")
            else:
                logging.warning(
                    f"No algorithm state found at {algo_dir}; "
                    "will keep their freshly-initialised values. Adam moments restored from the "
                    "old optimizer state may not match these reset parameters."
                )

        # Read interaction_step from the enriched training_step.json
        training_step_path = checkpoint_dir / TRAINING_STATE_DIR / TRAINING_STEP
        interaction_step = int(load_json(training_step_path).get("interaction_step", 0))

        logging.info(f"Resuming from step {step}, interaction step {interaction_step}")
        return step, interaction_step

    except Exception as e:
        logging.error(f"Failed to load training state: {e}")
        return None, None


def log_training_info(cfg: TrainRLServerPipelineConfig, policy: nn.Module) -> None:
    """
    Log information about the training process.
    Args:
        cfg (TrainRLServerPipelineConfig): Training configuration
        policy (nn.Module): Policy model
    """
    num_learnable_params = sum(p.numel() for p in policy.parameters() if p.requires_grad)
    num_total_params = sum(p.numel() for p in policy.parameters())

    logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {cfg.output_dir}")
    logging.info(f"{cfg.env.task=}")
    logging.info(f"{cfg.policy.online_steps=}")
    logging.info(f"{num_learnable_params=} ({format_big_number(num_learnable_params)})")
    logging.info(f"{num_total_params=} ({format_big_number(num_total_params)})")


def initialize_replay_buffer(
    cfg: TrainRLServerPipelineConfig, device: str, storage_device: str
) -> ReplayBuffer:
    """
    Initialize a replay buffer, either empty or from a dataset if resuming.
    Args:
        cfg (TrainRLServerPipelineConfig): Training configuration
        device (str): Device to store tensors on
        storage_device (str): Device for storage optimization
    Returns:
        ReplayBuffer: Initialized replay buffer
    """
    if not cfg.resume:
        return ReplayBuffer(
            capacity=cfg.policy.online_buffer_capacity,
            device=device,
            state_keys=cfg.policy.input_features.keys(),
            storage_device=storage_device,
            optimize_memory=True,
        )

    logging.info("Resume training load the online dataset")
    dataset_path = os.path.join(cfg.output_dir, "dataset")

    # NOTE: In RL is possible to not have a dataset.
    repo_id = None
    if cfg.dataset is not None:
        repo_id = cfg.dataset.repo_id
    dataset = LeRobotDataset(
        repo_id=repo_id,
        root=dataset_path,
    )
    return ReplayBuffer.from_lerobot_dataset(
        lerobot_dataset=dataset,
        capacity=cfg.policy.online_buffer_capacity,
        device=device,
        state_keys=cfg.policy.input_features.keys(),
        optimize_memory=True,
    )


def initialize_offline_replay_buffer(
    cfg: TrainRLServerPipelineConfig,
    device: str,
    storage_device: str,
) -> ReplayBuffer:
    """
    Initialize an offline replay buffer from a dataset.
    Args:
        cfg (TrainRLServerPipelineConfig): Training configuration
        device (str): Device to store tensors on
        storage_device (str): Device for storage optimization
    Returns:
        ReplayBuffer: Initialized offline replay buffer
    """
    if not cfg.resume:
        logging.info("make_dataset offline buffer")
        offline_dataset = make_dataset(cfg)
    else:
        logging.info("load offline dataset")
        dataset_offline_path = os.path.join(cfg.output_dir, "dataset_offline")
        offline_dataset = LeRobotDataset(
            repo_id=cfg.dataset.repo_id,
            root=dataset_offline_path,
        )

    logging.info("Convert to a offline replay buffer")
    offline_replay_buffer = ReplayBuffer.from_lerobot_dataset(
        offline_dataset,
        device=device,
        state_keys=cfg.policy.input_features.keys(),
        storage_device=storage_device,
        optimize_memory=True,
        capacity=cfg.policy.offline_buffer_capacity,
    )
    return offline_replay_buffer


# Utilities/Helpers functions


def use_threads(cfg: TrainRLServerPipelineConfig) -> bool:
    return cfg.policy.concurrency.learner == "threads"


def check_nan_in_transition(
    observations: torch.Tensor,
    actions: torch.Tensor,
    next_state: torch.Tensor,
    raise_error: bool = False,
) -> bool:
    """
    Check for NaN values in transition data.
    Args:
        observations: Dictionary of observation tensors
        actions: Action tensor
        next_state: Dictionary of next state tensors
        raise_error: If True, raises ValueError when NaN is detected
    Returns:
        bool: True if NaN values were detected, False otherwise
    """
    nan_detected = False

    # Check observations
    for key, tensor in observations.items():
        if torch.isnan(tensor).any():
            logging.error(f"observations[{key}] contains NaN values")
            nan_detected = True
            if raise_error:
                raise ValueError(f"NaN detected in observations[{key}]")

    # Check next state
    for key, tensor in next_state.items():
        if torch.isnan(tensor).any():
            logging.error(f"next_state[{key}] contains NaN values")
            nan_detected = True
            if raise_error:
                raise ValueError(f"NaN detected in next_state[{key}]")

    # Check actions
    if torch.isnan(actions).any():
        logging.error("actions contains NaN values")
        nan_detected = True
        if raise_error:
            raise ValueError("NaN detected in actions")

    return nan_detected


def push_actor_policy_to_queue(parameters_queue: Queue, algorithm: RLAlgorithm) -> None:
    logging.debug("[LEARNER] Pushing actor policy to the queue")

    # Create a dictionary to hold all the state dicts
    state_dicts = algorithm.get_weights()
    state_bytes = state_to_bytes(state_dicts)
    parameters_queue.put(state_bytes)


def process_interaction_message(
    message, interaction_step_shift: int, wandb_logger: WandBLogger | None = None
):
    """Process a single interaction message with consistent handling."""
    message = bytes_to_python_object(message)
    # Shift interaction step for consistency with checkpointed state
    message["Interaction step"] += interaction_step_shift

    # Log if logger available
    if wandb_logger:
        wandb_logger.log_dict(d=message, mode="train", custom_step_key="Interaction step")

    return message


def process_transitions(
    transition_queue: Queue,
    replay_buffer: ReplayBuffer,
    offline_replay_buffer: ReplayBuffer,
    dataset_repo_id: str | None,
    shutdown_event: Any,  # Event
):
    """Process all available transitions from the queue.
    Args:
        transition_queue: Queue for receiving transitions from the actor
        replay_buffer: Replay buffer to add transitions to
        offline_replay_buffer: Offline replay buffer to add transitions to
        dataset_repo_id: Repository ID for dataset
        shutdown_event: Event to signal shutdown
    """
    while not transition_queue.empty() and not shutdown_event.is_set():
        transition_list = transition_queue.get()
        transition_list = bytes_to_transitions(buffer=transition_list)

        for transition in transition_list:
            # Skip transitions with NaN values
            if check_nan_in_transition(
                observations=transition["state"],
                actions=transition[ACTION],
                next_state=transition["next_state"],
            ):
                logging.warning("[LEARNER] NaN detected in transition, skipping")
                continue

            replay_buffer.add(**transition)

            # Add to offline buffer if it's an intervention
            if dataset_repo_id is not None and transition.get("complementary_info", {}).get(
                TeleopEvents.IS_INTERVENTION.value
            ):
                offline_replay_buffer.add(**transition)


def process_interaction_messages(
    interaction_message_queue: Queue,
    interaction_step_shift: int,
    wandb_logger: WandBLogger | None,
    shutdown_event: Any,  # Event
) -> dict | None:
    """Process all available interaction messages from the queue.
    Args:
        interaction_message_queue: Queue for receiving interaction messages
        interaction_step_shift: Amount to shift interaction step by
        wandb_logger: Logger for tracking progress
        shutdown_event: Event to signal shutdown
    Returns:
        dict | None: The last interaction message processed, or None if none were processed
    """
    last_message = None
    while not interaction_message_queue.empty() and not shutdown_event.is_set():
        message = interaction_message_queue.get()
        last_message = process_interaction_message(
            message=message,
            interaction_step_shift=interaction_step_shift,
            wandb_logger=wandb_logger,
        )

    return last_message


if __name__ == "__main__":
    train_cli()
    logging.info("[LEARNER] main finished")