reference/generated/torchrl.trainers.algorithms.DDPGTrainer

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DDPGTrainer

class torchrl.trainers.algorithms.DDPGTrainer(*args, **kwargs)

A trainer class for Deep Deterministic Policy Gradient (DDPG) algorithm.

See also DDPGTrainerConfig for the Hydra configuration counterpart.

This trainer implements the DDPG algorithm, an off-policy actor-critic method that learns a deterministic policy for continuous action spaces.

The trainer handles: - Replay buffer management for off-policy learning - Target network updates (typically SoftUpdate) for stable training - Policy weight updates to the data collector - Comprehensive logging of training metrics

Parameters:

• collector (BaseCollector) – The data collector used to gather environment interactions.

• total_frames (int) – Total number of frames to collect during training.

• frame_skip (int) – Number of frames to skip between policy updates.

• optim_steps_per_batch (int) – Number of optimization steps per collected batch.

• loss_module (LossModule | Callable) – The DDPG loss module.

• optimizer (optim.Optimizer, optional) – The optimizer for training.

• logger (Logger, optional) – Logger for recording training metrics. Defaults to None.

• clip_grad_norm (bool, optional) – Whether to clip gradient norms. Defaults to True.

• clip_norm (float, optional) – Maximum gradient norm for clipping. Defaults to None.

• progress_bar (bool, optional) – Whether to show a progress bar. Defaults to True.

• seed (int, optional) – Random seed for reproducibility. Defaults to None.

• save_trainer_interval (int, optional) – Interval for saving trainer state. Defaults to 10000.

• log_interval (int, optional) – Interval for logging metrics. Defaults to 10000.

• save_trainer_file (str | pathlib.Path, optional) – File path for saving trainer state.

• replay_buffer (ReplayBuffer, optional) – Replay buffer for storing experiences. Defaults to None.

• enable_logging (bool, optional) – Whether to enable metric logging. Defaults to True.

• log_rewards (bool, optional) – Whether to log reward statistics. Defaults to True.

• log_actions (bool, optional) – Whether to log action statistics. Defaults to True.

• log_observations (bool, optional) – Whether to log observation statistics. Defaults to False.

• target_net_updater (TargetNetUpdater, optional) – Target network updater (typically SoftUpdate).

• async_collection (bool, optional) – Whether to use async data collection. Defaults to False.

• log_timings (bool, optional) – Whether to log timing information for hooks. Defaults to False.

• done_key (NestedKey, optional) – Done key used by losses and logging. Defaults to “done”.

• terminated_key (NestedKey, optional) – Terminated key used by losses and logging. Defaults to “terminated”.

• reward_key (NestedKey, optional) – Reward key used by losses and logging. Defaults to “reward”.

• episode_reward_key (NestedKey, optional) – Episode reward key used for cumulative reward logging. Defaults to “reward_sum”.

• action_key (NestedKey, optional) – Action key used by losses and logging. Defaults to “action”.

• observation_key (NestedKey, optional) – Observation key used for logging. Defaults to “observation”.

Note

This is an experimental/prototype feature. The API may change in future versions. DDPG is designed for continuous action spaces. For discrete actions, use DQNTrainer.

load_from_file(file: str | Path, **kwargs) → Trainer

Loads a file and its state-dict in the trainer.

Keyword arguments are passed to the load() function. They are ignored when CKPT_BACKEND=memmap.

Note

When CKPT_BACKEND=torch, weights_only=True is set by default for safer deserialization. Pass weights_only=False explicitly only if you have custom (non-stdlib) objects in your state dict.

request_stop(reason: str | None = None) → None

Signal that training should stop at the next loop boundary.