torchrl.trainers.algorithms.configs.trainers.TD3TrainerConfig#
- class torchrl.trainers.algorithms.configs.trainers.TD3TrainerConfig(collector: Any, total_frames: int, loss_module: Any, logger: Any, replay_buffer: Any, save_trainer_file: Any, optim_steps_per_batch: int | None = 1, optimizer: Any | None = None, optimizer_actor: Any | None = None, optimizer_critic: Any | None = None, optimization_stepper: Any | None = None, batch_size: int | None = None, learner_backend: str = 'local', learner_backend_options: dict[str, Any] | None = None, learner_poll_interval: float = 0.05, actor_network: Any = None, qvalue_network: Any = None, exploration_module: Any = None, seed: int | None = None, clip_grad_norm: bool = True, clip_norm: float | None = None, frame_skip: int = 1, progress_bar: bool = True, save_trainer_interval: int = 10000, log_interval: int = 10000, num_epochs: int = 1, async_collection: bool = False, log_timings: bool = False, auto_log_optim_steps: bool = True, enable_logging: bool = True, log_rewards: bool = True, log_actions: bool = True, log_observations: bool = False, create_env_fn: Any = None, target_net_updater: Any = None, policy_update_delay: int = 2, value_estimator_gamma: float | None = None, hooks: list[Any] | None = None, checkpoint: Any = None, _target_: str = 'torchrl.trainers.algorithms.configs.trainers._make_td3_trainer')[source]#
Hydra configuration for
TD3Trainer.Every kwarg accepted by
TD3Trainer.__init__is exposed as a field here.