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train.py
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train.py
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from smac.env import StarCraft2Env
from env_wrapper import SC2EnvWrapper
from replay_buffer import EpisodeExperience, EpisodeReplayBuffer
from qmixer_model import QMixerModel
from rnn_model import RNNModel
from parl.algorithms import QMIX
from qmix_agent import QMixAgent
import parl
from parl.utils import logger, summary
import numpy as np
from copy import deepcopy
from qmix_config import QMixConfig
logger.set_dir('./log_path')
def run_train_episode(env, agent, rpm, config):
episode_limit = config['episode_limit']
agent.reset_agent()
episode_reward = 0.0
episode_step = 0
terminated = False
state, obs = env.reset()
episode_experience = EpisodeExperience(episode_limit)
while not terminated:
available_actions = env.get_available_actions()
actions = agent.sample(obs, available_actions)
#next_state, next_obs, reward, terminated = env.step(actions)
next_state, next_obs, reward, terminated = env.step(actions)
episode_reward += reward
episode_step += 1
episode_experience.add(state, actions, [reward], [terminated], obs,
available_actions, [0])
state = next_state
obs = next_obs
# fill the episode
state_zero = np.zeros_like(state, dtype=state.dtype)
actions_zero = np.zeros_like(actions, dtype=actions.dtype)
obs_zero = np.zeros_like(obs, dtype=obs.dtype)
available_actions_zero = np.zeros_like(
available_actions, dtype=available_actions.dtype)
reward_zero = 0
terminated_zero = True
for _ in range(episode_step, episode_limit):
episode_experience.add(state_zero, actions_zero, [reward_zero],
[terminated_zero], obs_zero,
available_actions_zero, [1])
rpm.add(episode_experience)
is_win = env.win_counted
mean_loss = []
mean_td_error = []
if rpm.count > config['memory_warmup_size']:
for _ in range(2):
s_batch, a_batch, r_batch, t_batch, obs_batch, available_actions_batch,\
filled_batch = rpm.sample_batch(config['batch_size'])
loss, td_error = agent.learn(s_batch, a_batch, r_batch, t_batch,
obs_batch, available_actions_batch,
filled_batch)
mean_loss.append(loss)
mean_td_error.append(td_error)
mean_loss = np.mean(mean_loss) if mean_loss else None
mean_td_error = np.mean(mean_td_error) if mean_td_error else None
return episode_reward, episode_step, is_win, mean_loss, mean_td_error
def run_evaluate_episode(env, agent):
agent.reset_agent()
episode_reward = 0.0
episode_step = 0
terminated = False
state, obs = env.reset()
while not terminated:
available_actions = env.get_available_actions()
actions = agent.predict(obs, available_actions)
state, obs, reward, terminated = env.step(actions)
episode_step += 1
episode_reward += reward
is_win = env.win_counted
return episode_reward, episode_step, is_win
def main():
config = deepcopy(QMixConfig)
env = StarCraft2Env(
map_name=config['scenario'], difficulty=config['difficulty'])
env = SC2EnvWrapper(env)
config['episode_limit'] = env.episode_limit
config['obs_shape'] = env.obs_shape
config['state_shape'] = env.state_shape
config['n_agents'] = env.n_agents
config['n_actions'] = env.n_actions
rpm = EpisodeReplayBuffer(config['replay_buffer_size'])
agent_model = RNNModel(config['obs_shape'], config['n_actions'],
config['rnn_hidden_dim'])
qmixer_model = QMixerModel(
config['n_agents'], config['state_shape'], config['mixing_embed_dim'],
config['hypernet_layers'], config['hypernet_embed_dim'])
algorithm = QMIX(agent_model, qmixer_model, config['double_q'],
config['gamma'], config['lr'], config['clip_grad_norm'])
qmix_agent = QMixAgent(
algorithm, config['exploration_start'], config['min_exploration'],
config['exploration_decay'], config['update_target_interval'])
while rpm.count < config['memory_warmup_size']:
train_reward, train_step, train_is_win, train_loss, train_td_error\
= run_train_episode(env, qmix_agent, rpm, config)
total_steps = 0
last_test_step = -1e10
while total_steps < config['training_steps']:
train_reward, train_step, train_is_win, train_loss, train_td_error\
= run_train_episode(env, qmix_agent, rpm, config)
total_steps += train_step
if total_steps - last_test_step >= config['test_steps']:
last_test_step = total_steps
eval_is_win_buffer = []
eval_reward_buffer = []
eval_steps_buffer = []
for _ in range(3):
eval_reward, eval_step, eval_is_win = run_evaluate_episode(
env, qmix_agent)
eval_reward_buffer.append(eval_reward)
eval_steps_buffer.append(eval_step)
eval_is_win_buffer.append(eval_is_win)
summary.add_scalar('train_loss', train_loss, total_steps)
summary.add_scalar('eval_reward', np.mean(eval_reward_buffer),
total_steps)
summary.add_scalar('eval_steps', np.mean(eval_steps_buffer),
total_steps)
summary.add_scalar('eval_win_rate', np.mean(eval_is_win_buffer),
total_steps)
summary.add_scalar('exploration', qmix_agent.exploration,
total_steps)
summary.add_scalar('replay_buffer_size', rpm.count, total_steps)
summary.add_scalar('target_update_count',
qmix_agent.target_update_count, total_steps)
summary.add_scalar('train_td_error:', train_td_error, total_steps)
if __name__ == '__main__':
main()