94bfb32cc1
1. add policy.eval() in all test scripts' "watch performance" 2. remove dict return support for collector preprocess_fn 3. add `__contains__` and `pop` in batch: `key in batch`, `batch.pop(key, deft)` 4. exact n_episode for a list of n_episode limitation and save fake data in cache_buffer when self.buffer is None (#184) 5. fix tensorboard logging: h-axis stands for env step instead of gradient step; add test results into tensorboard 6. add test_returns (both GAE and nstep) 7. change the type-checking order in batch.py and converter.py in order to meet the most often case first 8. fix shape inconsistency for torch.Tensor in replay buffer 9. remove `**kwargs` in ReplayBuffer 10. remove default value in batch.split() and add merge_last argument (#185) 11. improve nstep efficiency 12. add max_batchsize in onpolicy algorithms 13. potential bugfix for subproc.wait 14. fix RecurrentActorProb 15. improve the code-coverage (from 90% to 95%) and remove the dead code 16. fix some incorrect type annotation The above improvement also increases the training FPS: on my computer, the previous version is only ~1800 FPS and after that, it can reach ~2050 (faster than v0.2.4.post1).
177 lines
7.0 KiB
Python
177 lines
7.0 KiB
Python
import os
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import torch
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import argparse
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import numpy as np
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from copy import deepcopy
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from typing import Optional, Tuple
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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.utils.net.common import Net
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from tianshou.trainer import offpolicy_trainer
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from tianshou.data import Collector, ReplayBuffer
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from tianshou.policy import BasePolicy, DQNPolicy, RandomPolicy, \
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MultiAgentPolicyManager
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from tic_tac_toe_env import TicTacToeEnv
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def get_parser() -> argparse.ArgumentParser:
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parser = argparse.ArgumentParser()
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parser.add_argument('--seed', type=int, default=1626)
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parser.add_argument('--eps-test', type=float, default=0.05)
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parser.add_argument('--eps-train', type=float, default=0.1)
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parser.add_argument('--buffer-size', type=int, default=20000)
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parser.add_argument('--lr', type=float, default=1e-3)
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parser.add_argument('--gamma', type=float, default=0.9,
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help='a smaller gamma favors earlier win')
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parser.add_argument('--n-step', type=int, default=3)
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parser.add_argument('--target-update-freq', type=int, default=320)
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parser.add_argument('--epoch', type=int, default=20)
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parser.add_argument('--step-per-epoch', type=int, default=500)
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parser.add_argument('--collect-per-step', type=int, default=10)
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parser.add_argument('--batch-size', type=int, default=64)
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parser.add_argument('--layer-num', type=int, default=3)
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parser.add_argument('--training-num', type=int, default=8)
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parser.add_argument('--test-num', type=int, default=100)
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parser.add_argument('--logdir', type=str, default='log')
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parser.add_argument('--render', type=float, default=0.1)
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parser.add_argument('--board_size', type=int, default=6)
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parser.add_argument('--win_size', type=int, default=4)
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parser.add_argument('--win_rate', type=float, default=0.9,
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help='the expected winning rate')
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parser.add_argument('--watch', default=False, action='store_true',
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help='no training, '
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'watch the play of pre-trained models')
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parser.add_argument('--agent_id', type=int, default=2,
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help='the learned agent plays as the'
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' agent_id-th player. choices are 1 and 2.')
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parser.add_argument('--resume_path', type=str, default='',
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help='the path of agent pth file '
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'for resuming from a pre-trained agent')
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parser.add_argument('--opponent_path', type=str, default='',
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help='the path of opponent agent pth file '
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'for resuming from a pre-trained agent')
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parser.add_argument(
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'--device', type=str,
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default='cuda' if torch.cuda.is_available() else 'cpu')
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return parser
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def get_args() -> argparse.Namespace:
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parser = get_parser()
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args = parser.parse_known_args()[0]
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return args
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def get_agents(args: argparse.Namespace = get_args(),
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agent_learn: Optional[BasePolicy] = None,
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agent_opponent: Optional[BasePolicy] = None,
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optim: Optional[torch.optim.Optimizer] = None,
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) -> Tuple[BasePolicy, torch.optim.Optimizer]:
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env = TicTacToeEnv(args.board_size, args.win_size)
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args.state_shape = env.observation_space.shape or env.observation_space.n
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args.action_shape = env.action_space.shape or env.action_space.n
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if agent_learn is None:
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# model
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net = Net(args.layer_num, args.state_shape, args.action_shape,
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args.device).to(args.device)
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if optim is None:
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optim = torch.optim.Adam(net.parameters(), lr=args.lr)
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agent_learn = DQNPolicy(
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net, optim, args.gamma, args.n_step,
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target_update_freq=args.target_update_freq)
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if args.resume_path:
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agent_learn.load_state_dict(torch.load(args.resume_path))
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if agent_opponent is None:
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if args.opponent_path:
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agent_opponent = deepcopy(agent_learn)
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agent_opponent.load_state_dict(torch.load(args.opponent_path))
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else:
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agent_opponent = RandomPolicy()
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if args.agent_id == 1:
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agents = [agent_learn, agent_opponent]
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else:
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agents = [agent_opponent, agent_learn]
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policy = MultiAgentPolicyManager(agents)
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return policy, optim
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def train_agent(args: argparse.Namespace = get_args(),
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agent_learn: Optional[BasePolicy] = None,
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agent_opponent: Optional[BasePolicy] = None,
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optim: Optional[torch.optim.Optimizer] = None,
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) -> Tuple[dict, BasePolicy]:
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def env_func():
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return TicTacToeEnv(args.board_size, args.win_size)
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train_envs = DummyVectorEnv([env_func for _ in range(args.training_num)])
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test_envs = DummyVectorEnv([env_func for _ in range(args.test_num)])
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# seed
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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train_envs.seed(args.seed)
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test_envs.seed(args.seed)
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policy, optim = get_agents(
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args, agent_learn=agent_learn,
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agent_opponent=agent_opponent, optim=optim)
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# collector
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train_collector = Collector(
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policy, train_envs, ReplayBuffer(args.buffer_size))
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test_collector = Collector(policy, test_envs)
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# policy.set_eps(1)
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train_collector.collect(n_step=args.batch_size)
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# log
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if not hasattr(args, 'writer'):
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log_path = os.path.join(args.logdir, 'tic_tac_toe', 'dqn')
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writer = SummaryWriter(log_path)
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args.writer = writer
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else:
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writer = args.writer
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def save_fn(policy):
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if hasattr(args, 'model_save_path'):
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model_save_path = args.model_save_path
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else:
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model_save_path = os.path.join(
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args.logdir, 'tic_tac_toe', 'dqn', 'policy.pth')
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torch.save(
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policy.policies[args.agent_id - 1].state_dict(),
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model_save_path)
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def stop_fn(x):
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return x >= args.win_rate
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def train_fn(x):
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policy.policies[args.agent_id - 1].set_eps(args.eps_train)
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def test_fn(x):
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policy.policies[args.agent_id - 1].set_eps(args.eps_test)
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# trainer
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result = offpolicy_trainer(
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policy, train_collector, test_collector, args.epoch,
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args.step_per_epoch, args.collect_per_step, args.test_num,
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args.batch_size, train_fn=train_fn, test_fn=test_fn,
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stop_fn=stop_fn, save_fn=save_fn, writer=writer,
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test_in_train=False)
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return result, policy.policies[args.agent_id - 1]
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def watch(args: argparse.Namespace = get_args(),
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agent_learn: Optional[BasePolicy] = None,
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agent_opponent: Optional[BasePolicy] = None,
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) -> None:
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env = TicTacToeEnv(args.board_size, args.win_size)
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policy, optim = get_agents(
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args, agent_learn=agent_learn, agent_opponent=agent_opponent)
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policy.eval()
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policy.set_eps(args.eps_test)
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collector = Collector(policy, env)
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result = collector.collect(n_episode=1, render=args.render)
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print(f'Final reward: {result["rew"]}, length: {result["len"]}')
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