import time
from typing import Any, Callable, Dict, Optional, Union
import numpy as np
from tianshou.data import Collector
from tianshou.policy import BasePolicy
from tianshou.utils import BaseLogger
[docs]def test_episode(
policy: BasePolicy,
collector: Collector,
test_fn: Optional[Callable[[int, Optional[int]], None]],
epoch: int,
n_episode: int,
logger: Optional[BaseLogger] = None,
global_step: Optional[int] = None,
reward_metric: Optional[Callable[[np.ndarray], np.ndarray]] = None,
) -> Dict[str, Any]:
"""A simple wrapper of testing policy in collector."""
collector.reset_env()
collector.reset_buffer()
policy.eval()
if test_fn:
test_fn(epoch, global_step)
result = collector.collect(n_episode=n_episode)
if reward_metric:
rew = reward_metric(result["rews"])
result.update(rews=rew, rew=rew.mean(), rew_std=rew.std())
if logger and global_step is not None:
logger.log_test_data(result, global_step)
return result
[docs]def gather_info(
start_time: float,
train_c: Optional[Collector],
test_c: Collector,
best_reward: float,
best_reward_std: float,
) -> Dict[str, Union[float, str]]:
"""A simple wrapper of gathering information from collectors.
:return: 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 transitions in the \
training collector;
* ``train_time/model`` the time for training models;
* ``train_speed`` the speed of training (env_step 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 (env_step per second);
* ``best_reward`` the best reward over the test results;
* ``duration`` the total elapsed time.
"""
duration = time.time() - start_time
model_time = duration - test_c.collect_time
test_speed = test_c.collect_step / test_c.collect_time
result: Dict[str, Union[float, str]] = {
"test_step": test_c.collect_step,
"test_episode": test_c.collect_episode,
"test_time": f"{test_c.collect_time:.2f}s",
"test_speed": f"{test_speed:.2f} step/s",
"best_reward": best_reward,
"best_result": f"{best_reward:.2f} ± {best_reward_std:.2f}",
"duration": f"{duration:.2f}s",
"train_time/model": f"{model_time:.2f}s",
}
if train_c is not None:
model_time -= train_c.collect_time
train_speed = train_c.collect_step / (duration - test_c.collect_time)
result.update(
{
"train_step": train_c.collect_step,
"train_episode": train_c.collect_episode,
"train_time/collector": f"{train_c.collect_time:.2f}s",
"train_time/model": f"{model_time:.2f}s",
"train_speed": f"{train_speed:.2f} step/s",
}
)
return result