a9f9940d17
- Refacor code to remove duplicate code - Enable async simulation for all vector envs - Remove `collector.close` and rename `VectorEnv` to `DummyVectorEnv` The abstraction of vector env changed. Prior to this pr, each vector env is almost independent. After this pr, each env is wrapped into a worker, and vector envs differ with their worker type. In fact, users can just use `BaseVectorEnv` with different workers, I keep `SubprocVectorEnv`, `ShmemVectorEnv` for backward compatibility. Co-authored-by: n+e <463003665@qq.com> Co-authored-by: magicly <magicly007@gmail.com>
85 lines
2.8 KiB
Python
85 lines
2.8 KiB
Python
import os
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import pprint
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import numpy as np
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from copy import deepcopy
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from torch.utils.tensorboard import SummaryWriter
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from tianshou.env import DummyVectorEnv
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from tianshou.data import Collector
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from tianshou.policy import RandomPolicy
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from tic_tac_toe_env import TicTacToeEnv
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from tic_tac_toe import get_parser, get_agents, train_agent, watch
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def get_args():
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parser = get_parser()
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parser.add_argument('--self_play_round', type=int, default=20)
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args = parser.parse_known_args()[0]
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return args
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def gomoku(args=get_args()):
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Collector._default_rew_metric = lambda x: x[args.agent_id - 1]
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if args.watch:
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watch(args)
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return
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policy, optim = get_agents(args)
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agent_learn = policy.policies[args.agent_id - 1]
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agent_opponent = policy.policies[2 - args.agent_id]
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# log
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log_path = os.path.join(args.logdir, 'Gomoku', 'dqn')
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args.writer = SummaryWriter(log_path)
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opponent_pool = [agent_opponent]
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def env_func():
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return TicTacToeEnv(args.board_size, args.win_size)
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test_envs = DummyVectorEnv([env_func for _ in range(args.test_num)])
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for r in range(args.self_play_round):
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rews = []
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agent_learn.set_eps(0.0)
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# compute the reward over previous learner
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for opponent in opponent_pool:
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policy.replace_policy(opponent, 3 - args.agent_id)
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test_collector = Collector(policy, test_envs)
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results = test_collector.collect(n_episode=100)
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rews.append(results['rew'])
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rews = np.array(rews)
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# weight opponent by their difficulty level
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rews = np.exp(-rews * 10.0)
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rews /= np.sum(rews)
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total_epoch = args.epoch
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args.epoch = 1
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for epoch in range(total_epoch):
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# sample one opponent
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opp_id = np.random.choice(len(opponent_pool), size=1, p=rews)
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print(f'selection probability {rews.tolist()}')
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print(f'selected opponent {opp_id}')
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opponent = opponent_pool[opp_id.item(0)]
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agent = RandomPolicy()
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# previous learner can only be used for forward
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agent.forward = opponent.forward
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args.model_save_path = os.path.join(
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args.logdir, 'Gomoku', 'dqn',
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f'policy_round_{r}_epoch_{epoch}.pth')
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result, agent_learn = train_agent(
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args, agent_learn=agent_learn,
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agent_opponent=agent, optim=optim)
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print(f'round_{r}_epoch_{epoch}')
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pprint.pprint(result)
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learnt_agent = deepcopy(agent_learn)
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learnt_agent.set_eps(0.0)
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opponent_pool.append(learnt_agent)
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args.epoch = total_epoch
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if __name__ == '__main__':
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# Let's watch its performance!
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opponent = opponent_pool[-2]
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watch(args, agent_learn, opponent)
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if __name__ == '__main__':
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gomoku(get_args())
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