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>
175 lines
6.9 KiB
Python
175 lines
6.9 KiB
Python
import os
|
|
import torch
|
|
import argparse
|
|
import numpy as np
|
|
from copy import deepcopy
|
|
from typing import Optional, Tuple
|
|
from torch.utils.tensorboard import SummaryWriter
|
|
|
|
from tianshou.env import DummyVectorEnv
|
|
from tianshou.utils.net.common import Net
|
|
from tianshou.trainer import offpolicy_trainer
|
|
from tianshou.data import Collector, ReplayBuffer
|
|
from tianshou.policy import BasePolicy, DQNPolicy, RandomPolicy, \
|
|
MultiAgentPolicyManager
|
|
|
|
from tic_tac_toe_env import TicTacToeEnv
|
|
|
|
|
|
def get_parser() -> argparse.ArgumentParser:
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--seed', type=int, default=1626)
|
|
parser.add_argument('--eps-test', type=float, default=0.05)
|
|
parser.add_argument('--eps-train', type=float, default=0.1)
|
|
parser.add_argument('--buffer-size', type=int, default=20000)
|
|
parser.add_argument('--lr', type=float, default=1e-3)
|
|
parser.add_argument('--gamma', type=float, default=0.9,
|
|
help='a smaller gamma favors earlier win')
|
|
parser.add_argument('--n-step', type=int, default=3)
|
|
parser.add_argument('--target-update-freq', type=int, default=320)
|
|
parser.add_argument('--epoch', type=int, default=20)
|
|
parser.add_argument('--step-per-epoch', type=int, default=500)
|
|
parser.add_argument('--collect-per-step', type=int, default=10)
|
|
parser.add_argument('--batch-size', type=int, default=64)
|
|
parser.add_argument('--layer-num', type=int, default=3)
|
|
parser.add_argument('--training-num', type=int, default=8)
|
|
parser.add_argument('--test-num', type=int, default=100)
|
|
parser.add_argument('--logdir', type=str, default='log')
|
|
parser.add_argument('--render', type=float, default=0.1)
|
|
parser.add_argument('--board_size', type=int, default=6)
|
|
parser.add_argument('--win_size', type=int, default=4)
|
|
parser.add_argument('--win_rate', type=float, default=0.9,
|
|
help='the expected winning rate')
|
|
parser.add_argument('--watch', default=False, action='store_true',
|
|
help='no training, '
|
|
'watch the play of pre-trained models')
|
|
parser.add_argument('--agent_id', type=int, default=2,
|
|
help='the learned agent plays as the'
|
|
' agent_id-th player. choices are 1 and 2.')
|
|
parser.add_argument('--resume_path', type=str, default='',
|
|
help='the path of agent pth file '
|
|
'for resuming from a pre-trained agent')
|
|
parser.add_argument('--opponent_path', type=str, default='',
|
|
help='the path of opponent agent pth file '
|
|
'for resuming from a pre-trained agent')
|
|
parser.add_argument(
|
|
'--device', type=str,
|
|
default='cuda' if torch.cuda.is_available() else 'cpu')
|
|
return parser
|
|
|
|
|
|
def get_args() -> argparse.Namespace:
|
|
parser = get_parser()
|
|
args = parser.parse_known_args()[0]
|
|
return args
|
|
|
|
|
|
def get_agents(args: argparse.Namespace = get_args(),
|
|
agent_learn: Optional[BasePolicy] = None,
|
|
agent_opponent: Optional[BasePolicy] = None,
|
|
optim: Optional[torch.optim.Optimizer] = None,
|
|
) -> Tuple[BasePolicy, torch.optim.Optimizer]:
|
|
env = TicTacToeEnv(args.board_size, args.win_size)
|
|
args.state_shape = env.observation_space.shape or env.observation_space.n
|
|
args.action_shape = env.action_space.shape or env.action_space.n
|
|
if agent_learn is None:
|
|
# model
|
|
net = Net(args.layer_num, args.state_shape, args.action_shape,
|
|
args.device).to(args.device)
|
|
if optim is None:
|
|
optim = torch.optim.Adam(net.parameters(), lr=args.lr)
|
|
agent_learn = DQNPolicy(
|
|
net, optim, args.gamma, args.n_step,
|
|
target_update_freq=args.target_update_freq)
|
|
if args.resume_path:
|
|
agent_learn.load_state_dict(torch.load(args.resume_path))
|
|
|
|
if agent_opponent is None:
|
|
if args.opponent_path:
|
|
agent_opponent = deepcopy(agent_learn)
|
|
agent_opponent.load_state_dict(torch.load(args.opponent_path))
|
|
else:
|
|
agent_opponent = RandomPolicy()
|
|
|
|
if args.agent_id == 1:
|
|
agents = [agent_learn, agent_opponent]
|
|
else:
|
|
agents = [agent_opponent, agent_learn]
|
|
policy = MultiAgentPolicyManager(agents)
|
|
return policy, optim
|
|
|
|
|
|
def train_agent(args: argparse.Namespace = get_args(),
|
|
agent_learn: Optional[BasePolicy] = None,
|
|
agent_opponent: Optional[BasePolicy] = None,
|
|
optim: Optional[torch.optim.Optimizer] = None,
|
|
) -> Tuple[dict, BasePolicy]:
|
|
def env_func():
|
|
return TicTacToeEnv(args.board_size, args.win_size)
|
|
train_envs = DummyVectorEnv([env_func for _ in range(args.training_num)])
|
|
test_envs = DummyVectorEnv([env_func for _ in range(args.test_num)])
|
|
# seed
|
|
np.random.seed(args.seed)
|
|
torch.manual_seed(args.seed)
|
|
train_envs.seed(args.seed)
|
|
test_envs.seed(args.seed)
|
|
|
|
policy, optim = get_agents(
|
|
args, agent_learn=agent_learn,
|
|
agent_opponent=agent_opponent, optim=optim)
|
|
|
|
# collector
|
|
train_collector = Collector(
|
|
policy, train_envs, ReplayBuffer(args.buffer_size))
|
|
test_collector = Collector(policy, test_envs)
|
|
# policy.set_eps(1)
|
|
train_collector.collect(n_step=args.batch_size)
|
|
# log
|
|
if not hasattr(args, 'writer'):
|
|
log_path = os.path.join(args.logdir, 'tic_tac_toe', 'dqn')
|
|
writer = SummaryWriter(log_path)
|
|
args.writer = writer
|
|
else:
|
|
writer = args.writer
|
|
|
|
def save_fn(policy):
|
|
if hasattr(args, 'model_save_path'):
|
|
model_save_path = args.model_save_path
|
|
else:
|
|
model_save_path = os.path.join(
|
|
args.logdir, 'tic_tac_toe', 'dqn', 'policy.pth')
|
|
torch.save(
|
|
policy.policies[args.agent_id - 1].state_dict(),
|
|
model_save_path)
|
|
|
|
def stop_fn(x):
|
|
return x >= args.win_rate
|
|
|
|
def train_fn(x):
|
|
policy.policies[args.agent_id - 1].set_eps(args.eps_train)
|
|
|
|
def test_fn(x):
|
|
policy.policies[args.agent_id - 1].set_eps(args.eps_test)
|
|
|
|
# trainer
|
|
result = offpolicy_trainer(
|
|
policy, train_collector, test_collector, args.epoch,
|
|
args.step_per_epoch, args.collect_per_step, args.test_num,
|
|
args.batch_size, train_fn=train_fn, test_fn=test_fn,
|
|
stop_fn=stop_fn, save_fn=save_fn, writer=writer,
|
|
test_in_train=False)
|
|
|
|
return result, policy.policies[args.agent_id - 1]
|
|
|
|
|
|
def watch(args: argparse.Namespace = get_args(),
|
|
agent_learn: Optional[BasePolicy] = None,
|
|
agent_opponent: Optional[BasePolicy] = None,
|
|
) -> None:
|
|
env = TicTacToeEnv(args.board_size, args.win_size)
|
|
policy, optim = get_agents(
|
|
args, agent_learn=agent_learn, agent_opponent=agent_opponent)
|
|
collector = Collector(policy, env)
|
|
result = collector.collect(n_episode=1, render=args.render)
|
|
print(f'Final reward: {result["rew"]}, length: {result["len"]}')
|