reference/generated/torchrl.trainers.algorithms.SACTrainer

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SACTrainer

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

A trainer class for Soft Actor-Critic (SAC) algorithm.

See also SACTrainerConfig for the Hydra configuration counterpart.

This trainer implements the SAC algorithm, an off-policy actor-critic method that optimizes a stochastic policy in an off-policy way, forming a bridge between stochastic policy optimization and DDPG-style approaches. SAC incorporates the entropy measure of the policy into the reward to encourage exploration.

The trainer handles: - Replay buffer management for off-policy learning - Target network updates with configurable update frequency - Policy weight updates to the data collector - Comprehensive logging of training metrics - Gradient clipping and optimization steps

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 SAC loss module or a callable that computes losses.

• optimizer (optim.Optimizer, optional) – The optimizer for training. If None, must be configured elsewhere.

• 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 during training. 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. Defaults to None.

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

• batch_size (int, optional) – Batch size for sampling from replay buffer. 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 for soft updates. Defaults to None.

• 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”.

Example

>>> from torchrl.collectors import Collector
>>> from torchrl.objectives import SACLoss
>>> from torchrl.data import ReplayBuffer, LazyTensorStorage
>>> from torch import optim
>>>
>>> # Set up collector, loss, and replay buffer
>>> collector = Collector(env, policy, frames_per_batch=1000)
>>> loss_module = SACLoss(actor_network, qvalue_network)
>>> optimizer = optim.Adam(loss_module.parameters(), lr=3e-4)
>>> replay_buffer = ReplayBuffer(storage=LazyTensorStorage(100000))
>>>
>>> # Create and run trainer
>>> trainer = SACTrainer(
...     collector=collector,
...     total_frames=1000000,
...     frame_skip=1,
...     optim_steps_per_batch=100,
...     loss_module=loss_module,
...     optimizer=optimizer,
...     replay_buffer=replay_buffer,
... )
>>> trainer.train()

Note

This is an experimental/prototype feature. The API may change in future versions. SAC is particularly effective for continuous control tasks and environments where exploration is crucial due to its entropy regularization.

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.