tianshou.trainer

tianshou.trainer.gather_info(start_time: float, train_c: Optional[tianshou.data.collector.Collector], test_c: tianshou.data.collector.Collector, best_reward: float, best_reward_std: float) → Dict[str, Union[float, str]][source]

A simple wrapper of gathering information from collectors.

Returns

A dictionary with the following keys:

  • train_step the total collected step of training collector;

  • train_episode the total collected episode of training collector;

  • train_time/collector the time for collecting frames in the training collector;

  • train_time/model the time for training models;

  • train_speed the speed of training (frames per second);

  • test_step the total collected step of test collector;

  • test_episode the total collected episode of test collector;

  • test_time the time for testing;

  • test_speed the speed of testing (frames per second);

  • best_reward the best reward over the test results;

  • duration the total elapsed time.

tianshou.trainer.offline_trainer(policy: tianshou.policy.base.BasePolicy, buffer: tianshou.data.buffer.ReplayBuffer, test_collector: tianshou.data.collector.Collector, max_epoch: int, step_per_epoch: int, episode_per_test: Union[int, List[int]], batch_size: int, test_fn: Optional[Callable[[int, Optional[int]], None]] = None, stop_fn: Optional[Callable[[float], bool]] = None, save_fn: Optional[Callable[[tianshou.policy.base.BasePolicy], None]] = None, writer: Optional[torch.utils.tensorboard.writer.SummaryWriter] = None, log_interval: int = 1, verbose: bool = True) → Dict[str, Union[float, str]][source]

A wrapper for offline trainer procedure.

The “step” in trainer means a policy network update.

Parameters

  • policy – an instance of the BasePolicy class.

  • test_collector (Collector) – the collector used for testing.

  • max_epoch (int) – the maximum number of epochs for training. The training process might be finished before reaching the max_epoch.

  • step_per_epoch (int) – the number of policy network updates, so-called gradient steps, per epoch.

  • episode_per_test – the number of episodes for one policy evaluation.

  • batch_size (int) – the batch size of sample data, which is going to feed in the policy network.

  • test_fn (function) – a hook called at the beginning of testing in each epoch. It can be used to perform custom additional operations, with the signature f(num_epoch: int, step_idx: int) -> None.

  • save_fn (function) – a hook called when the undiscounted average mean reward in evaluation phase gets better, with the signature f(policy: BasePolicy) -> None.

  • stop_fn (function) – a function with signature f(mean_rewards: float) -> bool, receives the average undiscounted returns of the testing result, returns a boolean which indicates whether reaching the goal.

  • writer (torch.utils.tensorboard.SummaryWriter) – a TensorBoard SummaryWriter; if None is given, it will not write logs to TensorBoard.

  • log_interval (int) – the log interval of the writer.

  • verbose (bool) – whether to print the information.

Returns

See gather_info().

tianshou.trainer.offpolicy_trainer(policy: tianshou.policy.base.BasePolicy, train_collector: tianshou.data.collector.Collector, test_collector: tianshou.data.collector.Collector, max_epoch: int, step_per_epoch: int, collect_per_step: int, episode_per_test: Union[int, List[int]], batch_size: int, update_per_step: int = 1, train_fn: Optional[Callable[[int, int], None]] = None, test_fn: Optional[Callable[[int, Optional[int]], None]] = None, stop_fn: Optional[Callable[[float], bool]] = None, save_fn: Optional[Callable[[tianshou.policy.base.BasePolicy], None]] = None, writer: Optional[torch.utils.tensorboard.writer.SummaryWriter] = None, log_interval: int = 1, verbose: bool = True, test_in_train: bool = True) → Dict[str, Union[float, str]][source]

A wrapper for off-policy trainer procedure.

The “step” in trainer means a policy network update.

Parameters

  • policy – an instance of the BasePolicy class.

  • train_collector (Collector) – the collector used for training.

  • test_collector (Collector) – the collector used for testing.

  • max_epoch (int) – the maximum number of epochs for training. The training process might be finished before reaching the max_epoch.

  • step_per_epoch (int) – the number of policy network updates, so-called gradient steps, per epoch.

  • collect_per_step (int) – the number of frames the collector would collect before the network update. In other words, collect some frames and do some policy network update.

  • episode_per_test – the number of episodes for one policy evaluation.

  • batch_size (int) – the batch size of sample data, which is going to feed in the policy network.

  • update_per_step (int) – the number of times the policy network would be updated after frames are collected, for example, set it to 256 means it updates policy 256 times once after collect_per_step frames are collected.

  • train_fn (function) – a hook called at the beginning of training in each epoch. It can be used to perform custom additional operations, with the signature f(num_epoch: int, step_idx: int) -> None.

  • test_fn (function) – a hook called at the beginning of testing in each epoch. It can be used to perform custom additional operations, with the signature f(num_epoch: int, step_idx: int) -> None.

  • save_fn (function) – a hook called when the undiscounted average mean reward in evaluation phase gets better, with the signature f(policy: BasePolicy) -> None.

  • stop_fn (function) – a function with signature f(mean_rewards: float) -> bool, receives the average undiscounted returns of the testing result, returns a boolean which indicates whether reaching the goal.

  • writer (torch.utils.tensorboard.SummaryWriter) – a TensorBoard SummaryWriter; if None is given, it will not write logs to TensorBoard.

  • log_interval (int) – the log interval of the writer.

  • verbose (bool) – whether to print the information.

  • test_in_train (bool) – whether to test in the training phase.

Returns

See gather_info().

tianshou.trainer.onpolicy_trainer(policy: tianshou.policy.base.BasePolicy, train_collector: tianshou.data.collector.Collector, test_collector: tianshou.data.collector.Collector, max_epoch: int, step_per_epoch: int, collect_per_step: int, repeat_per_collect: int, episode_per_test: Union[int, List[int]], batch_size: int, train_fn: Optional[Callable[[int, int], None]] = None, test_fn: Optional[Callable[[int, Optional[int]], None]] = None, stop_fn: Optional[Callable[[float], bool]] = None, save_fn: Optional[Callable[[tianshou.policy.base.BasePolicy], None]] = None, writer: Optional[torch.utils.tensorboard.writer.SummaryWriter] = None, log_interval: int = 1, verbose: bool = True, test_in_train: bool = True) → Dict[str, Union[float, str]][source]

A wrapper for on-policy trainer procedure.

The “step” in trainer means a policy network update.

Parameters

  • policy – an instance of the BasePolicy class.

  • train_collector (Collector) – the collector used for training.

  • test_collector (Collector) – the collector used for testing.

  • max_epoch (int) – the maximum number of epochs for training. The training process might be finished before reaching the max_epoch.

  • step_per_epoch (int) – the number of policy network updates, so-called gradient steps, per epoch.

  • collect_per_step (int) – the number of episodes the collector would collect before the network update. In other words, collect some episodes and do one policy network update.

  • repeat_per_collect (int) – the number of repeat time for policy learning, for example, set it to 2 means the policy needs to learn each given batch data twice.

  • episode_per_test (int or list of ints) – the number of episodes for one policy evaluation.

  • batch_size (int) – the batch size of sample data, which is going to feed in the policy network.

  • train_fn (function) – a hook called at the beginning of training in each epoch. It can be used to perform custom additional operations, with the signature f(num_epoch: int, step_idx: int) -> None.

  • test_fn (function) – a hook called at the beginning of testing in each epoch. It can be used to perform custom additional operations, with the signature f(num_epoch: int, step_idx: int) -> None.

  • save_fn (function) – a hook called when the undiscounted average mean reward in evaluation phase gets better, with the signature f(policy: BasePolicy) -> None.

  • stop_fn (function) – a function with signature f(mean_rewards: float) -> bool, receives the average undiscounted returns of the testing result, returns a boolean which indicates whether reaching the goal.

  • writer (torch.utils.tensorboard.SummaryWriter) – a TensorBoard SummaryWriter; if None is given, it will not write logs to TensorBoard.

  • log_interval (int) – the log interval of the writer.

  • verbose (bool) – whether to print the information.

  • test_in_train (bool) – whether to test in the training phase.

Returns

See gather_info().

tianshou.trainer.test_episode(policy: tianshou.policy.base.BasePolicy, collector: tianshou.data.collector.Collector, test_fn: Optional[Callable[[int, Optional[int]], None]], epoch: int, n_episode: Union[int, List[int]], writer: Optional[torch.utils.tensorboard.writer.SummaryWriter] = None, global_step: Optional[int] = None) → Dict[str, float][source]

A simple wrapper of testing policy in collector.