55 lines
2.2 KiB
Python
55 lines
2.2 KiB
Python
import time
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import numpy as np
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def test_episode(policy, collector, test_fn, epoch, n_episode):
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"""A simple wrapper of testing policy in collector."""
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collector.reset_env()
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collector.reset_buffer()
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policy.eval()
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if test_fn:
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test_fn(epoch)
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if collector.get_env_num() > 1 and np.isscalar(n_episode):
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n = collector.get_env_num()
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n_ = np.zeros(n) + n_episode // n
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n_[:n_episode % n] += 1
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n_episode = list(n_)
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return collector.collect(n_episode=n_episode)
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def gather_info(start_time, train_c, test_c, best_reward):
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"""A simple wrapper of gathering information from collectors.
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:return: A dictionary with the following keys:
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* ``train_step`` the total collected step of training collector;
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* ``train_episode`` the total collected episode of training collector;
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* ``train_time/collector`` the time for collecting frames in the \
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training collector;
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* ``train_time/model`` the time for training models;
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* ``train_speed`` the speed of training (frames per second);
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* ``test_step`` the total collected step of test collector;
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* ``test_episode`` the total collected episode of test collector;
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* ``test_time`` the time for testing;
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* ``test_speed`` the speed of testing (frames per second);
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* ``best_reward`` the best reward over the test results;
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* ``duration`` the total elapsed time.
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"""
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duration = time.time() - start_time
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model_time = duration - train_c.collect_time - test_c.collect_time
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train_speed = train_c.collect_step / (duration - test_c.collect_time)
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test_speed = test_c.collect_step / test_c.collect_time
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return {
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'train_step': train_c.collect_step,
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'train_episode': train_c.collect_episode,
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'train_time/collector': f'{train_c.collect_time:.2f}s',
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'train_time/model': f'{model_time:.2f}s',
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'train_speed': f'{train_speed:.2f} step/s',
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'test_step': test_c.collect_step,
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'test_episode': test_c.collect_episode,
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'test_time': f'{test_c.collect_time:.2f}s',
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'test_speed': f'{test_speed:.2f} step/s',
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'best_reward': best_reward,
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'duration': f'{duration:.2f}s',
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}
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