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78 lines
2.6 KiB
Python
78 lines
2.6 KiB
Python
# Lint as: python3
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# pylint: disable=g-bad-file-header
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# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""A simple training loop."""
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from absl import logging
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def _ema(base, val, decay=0.995):
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return base * decay + (1 - decay) * val
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def run(env, agent, num_episodes, report_every=200, num_eval_reps=1):
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"""Runs an agent on an environment.
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Args:
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env: The environment.
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agent: The agent.
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num_episodes: Number of episodes to train for.
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report_every: Frequency at which training progress are reported (episodes).
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num_eval_reps: Number of eval episodes to run per training episode.
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"""
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train_returns = []
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train_return_ema = 0.
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eval_returns = []
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eval_return_ema = 0.
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for episode_id in range(num_episodes):
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# Run a training episode.
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train_episode_return = run_episode(env, agent, is_training=True)
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train_returns.append(train_episode_return)
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train_return_ema = _ema(train_return_ema, train_episode_return)
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# Run an evaluation episode.
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for _ in range(num_eval_reps):
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eval_episode_return = run_episode(env, agent, is_training=False)
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eval_returns.append(eval_episode_return)
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eval_return_ema = _ema(eval_return_ema, eval_episode_return)
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if ((episode_id + 1) % report_every) == 0:
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logging.info("Episode %s, avg train return %.3f, avg eval return %.3f",
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episode_id + 1, train_return_ema, eval_return_ema)
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if hasattr(agent, "get_logs"):
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logging.info("Episode %s, agent logs: %s", episode_id + 1,
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agent.get_logs())
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def run_episode(environment, agent, is_training=False):
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"""Run a single episode."""
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timestep = environment.reset()
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while not timestep.last():
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action = agent.step(timestep, is_training)
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new_timestep = environment.step(action)
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if is_training:
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agent.update(timestep, action, new_timestep)
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timestep = new_timestep
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episode_return = environment.episode_return
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return episode_return
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