reference/generated/torchrl.trainers.algorithms.configs.trainers.ReinforceTrainerConfig

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torchrl.trainers.algorithms.configs.trainers.ReinforceTrainerConfig

class torchrl.trainers.algorithms.configs.trainers.ReinforceTrainerConfig(collector: Any, total_frames: int, optim_steps_per_batch: int | None, loss_module: Any, optimizer: Any, logger: Any, save_trainer_file: Any, replay_buffer: Any, frame_skip: int = 1, clip_grad_norm: bool = True, clip_norm: float | None = None, progress_bar: bool = True, seed: int | None = None, save_trainer_interval: int = 10000, log_interval: int = 10000, create_env_fn: Any = None, actor_network: Any = None, critic_network: Any = None, num_epochs: int = 1, async_collection: bool = False, add_gae: bool = True, gae: Any = None, lr_scheduler: Any = None, weight_update_map: dict[str, str] | None = None, log_timings: bool = False, auto_log_optim_steps: bool = True, batch_size: int | None = None, gamma: float = 0.99, lmbda: float = 0.95, enable_logging: bool = True, log_rewards: bool = True, log_actions: bool = True, log_observations: bool = False, done_key: Any = 'done', terminated_key: Any = 'terminated', reward_key: Any = 'reward', episode_reward_key: Any = 'reward', action_key: Any = 'action', observation_key: Any = 'observation', hooks: list[Any] | None = None, checkpoint: Any = None, _target_: str = 'torchrl.trainers.algorithms.configs.trainers._make_reinforce_trainer')

Hydra configuration for ReinforceTrainer.

Every kwarg accepted by ReinforceTrainer.__init__ is exposed as a field here; see OnPolicyTrainerConfig for the full field list. REINFORCE performs a single optimization pass over each collected batch (num_epochs=1).