113 lines
5.4 KiB
Python
113 lines
5.4 KiB
Python
import time
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import tqdm
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import numpy as np
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from collections import defaultdict
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from typing import Dict, Union, Callable, Optional
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from tianshou.policy import BasePolicy
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from tianshou.utils import tqdm_config, MovAvg, BaseLogger, LazyLogger
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from tianshou.data import Collector, ReplayBuffer
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from tianshou.trainer import test_episode, gather_info
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def offline_trainer(
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policy: BasePolicy,
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buffer: ReplayBuffer,
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test_collector: Collector,
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max_epoch: int,
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update_per_epoch: int,
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episode_per_test: int,
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batch_size: int,
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test_fn: Optional[Callable[[int, Optional[int]], None]] = None,
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stop_fn: Optional[Callable[[float], bool]] = None,
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save_fn: Optional[Callable[[BasePolicy], None]] = None,
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save_checkpoint_fn: Optional[Callable[[int, int, int], None]] = None,
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resume_from_log: bool = False,
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reward_metric: Optional[Callable[[np.ndarray], np.ndarray]] = None,
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logger: BaseLogger = LazyLogger(),
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verbose: bool = True,
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) -> Dict[str, Union[float, str]]:
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"""A wrapper for offline trainer procedure.
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The "step" in offline trainer means a gradient step.
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:param policy: an instance of the :class:`~tianshou.policy.BasePolicy` class.
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:param Collector test_collector: the collector used for testing.
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:param int max_epoch: the maximum number of epochs for training. The training
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process might be finished before reaching ``max_epoch`` if ``stop_fn`` is set.
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:param int update_per_epoch: the number of policy network updates, so-called
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gradient steps, per epoch.
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:param episode_per_test: the number of episodes for one policy evaluation.
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:param int batch_size: the batch size of sample data, which is going to feed in
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the policy network.
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:param function test_fn: a hook called at the beginning of testing in each epoch.
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It can be used to perform custom additional operations, with the signature ``f(
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num_epoch: int, step_idx: int) -> None``.
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:param function save_fn: a hook called when the undiscounted average mean reward in
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evaluation phase gets better, with the signature ``f(policy: BasePolicy) ->
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None``.
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:param function save_checkpoint_fn: a function to save training process, with the
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signature ``f(epoch: int, env_step: int, gradient_step: int) -> None``; you can
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save whatever you want. Because offline-RL doesn't have env_step, the env_step
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is always 0 here.
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:param bool resume_from_log: resume gradient_step and other metadata from existing
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tensorboard log. Default to False.
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:param function stop_fn: a function with signature ``f(mean_rewards: float) ->
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bool``, receives the average undiscounted returns of the testing result,
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returns a boolean which indicates whether reaching the goal.
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:param function reward_metric: a function with signature ``f(rewards: np.ndarray
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with shape (num_episode, agent_num)) -> np.ndarray with shape (num_episode,)``,
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used in multi-agent RL. We need to return a single scalar for each episode's
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result to monitor training in the multi-agent RL setting. This function
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specifies what is the desired metric, e.g., the reward of agent 1 or the
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average reward over all agents.
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:param BaseLogger logger: A logger that logs statistics during updating/testing.
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Default to a logger that doesn't log anything.
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:param bool verbose: whether to print the information. Default to True.
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:return: See :func:`~tianshou.trainer.gather_info`.
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"""
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start_epoch, gradient_step = 0, 0
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if resume_from_log:
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start_epoch, _, gradient_step = logger.restore_data()
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stat: Dict[str, MovAvg] = defaultdict(MovAvg)
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start_time = time.time()
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test_collector.reset_stat()
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test_result = test_episode(policy, test_collector, test_fn, start_epoch,
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episode_per_test, logger, gradient_step, reward_metric)
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best_epoch = start_epoch
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best_reward, best_reward_std = test_result["rew"], test_result["rew_std"]
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for epoch in range(1 + start_epoch, 1 + max_epoch):
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policy.train()
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with tqdm.trange(
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update_per_epoch, desc=f"Epoch #{epoch}", **tqdm_config
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) as t:
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for i in t:
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gradient_step += 1
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losses = policy.update(batch_size, buffer)
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data = {"gradient_step": str(gradient_step)}
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for k in losses.keys():
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stat[k].add(losses[k])
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losses[k] = stat[k].get()
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data[k] = f"{losses[k]:.3f}"
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logger.log_update_data(losses, gradient_step)
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t.set_postfix(**data)
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# test
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test_result = test_episode(
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policy, test_collector, test_fn, epoch, episode_per_test,
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logger, gradient_step, reward_metric)
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rew, rew_std = test_result["rew"], test_result["rew_std"]
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if best_epoch < 0 or best_reward < rew:
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best_epoch, best_reward, best_reward_std = epoch, rew, rew_std
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if save_fn:
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save_fn(policy)
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logger.save_data(epoch, 0, gradient_step, save_checkpoint_fn)
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if verbose:
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print(f"Epoch #{epoch}: test_reward: {rew:.6f} ± {rew_std:.6f}, best_rew"
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f"ard: {best_reward:.6f} ± {best_reward_std:.6f} in #{best_epoch}")
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if stop_fn and stop_fn(best_reward):
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break
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return gather_info(start_time, None, test_collector, best_reward, best_reward_std)
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