SACTrainer#

class torchrl.trainers.algorithms.SACTrainer(*args, **kwargs)[source]#

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.
learner_backend (str) – Optimization placement, "local" or "ray".
learner_backend_options (dict, optional) – Ray world size and resources.
learner_poll_interval (float) – Remote replay polling interval.
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) – Target network updater for soft updates.
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 torchrl.objectives.utils import SoftUpdate
>>> 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))
>>> target_net_updater = SoftUpdate(loss_module, eps=0.995)
>>>
>>> # 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,
...     target_net_updater=target_net_updater,
... )
>>> 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.

compute_loss(sub_batch: TensorDictBase, method: str | None = None) → TensorDictBase | tuple[Any, ...]#: Evaluate the configured loss through the active execution boundary.

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 for legacy torch checkpoints and unified components explicitly saved with the torch state-dict payload format. Unified checkpoints additionally accept strict to control missing or incompatible components. Arguments are ignored when CKPT_BACKEND=memmap.

Note

Unified state-dict components use TensorDict storage by default and do not invoke the pickle loader. For explicit torch payloads and CKPT_BACKEND=torch checkpoints, weights_only=True is the default for safer deserialization. Pass weights_only=False explicitly only if the state dict contains custom objects. On torch < 2.4 the default is weights_only=False because the weights-only unpickler of those versions cannot deserialize the torch.device instances contained in TensorDict state-dicts.

Note

Explicit torch payloads and CKPT_BACKEND=torch checkpoints use mmap=True by default. Pass mmap=False for legacy pre-zipfile torch.save files or file-like objects. On Windows the default is mmap=False because a mapped checkpoint keeps the file locked, preventing deletion or re-save.

Note

Unified checkpoint tensors are mapped to CPU by default. Pass an explicit map_location to select another device mapping.

Note

After restoring an independently registered policy component, the trainer synchronizes the collector once so local policy copies and remote workers observe the restored learner weights.

request_stop(reason: str | None = None) → None#: Signal that training should stop at the next loop boundary.

SACTrainer#

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