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import sys
sys.path.append("/Users/puyuan/code/LightZero/")
from functools import partial
import torch
from ding.config import compile_config
from ding.envs import create_env_manager
from ding.envs import get_vec_env_setting
from ding.policy import create_policy
from ding.utils import set_pkg_seed
from zoo.board_games.gomoku.config.gomoku_alphazero_bot_mode_config import main_config, create_config
import numpy as np
class Agent:
def __init__(self, seed=0):
# model_path = './ckpt/ckpt_best.pth.tar'
model_path = None
# If True, you can play with the agent.
# main_config.env.agent_vs_human = True
main_config.env.agent_vs_human = False
# main_config.env.render_mode = 'image_realtime_mode'
main_config.env.render_mode = 'image_savefile_mode'
main_config.env.replay_path = './video'
create_config.env_manager.type = 'base'
main_config.env.alphazero_mcts_ctree = False
main_config.policy.mcts_ctree = False
main_config.env.evaluator_env_num = 1
main_config.env.n_evaluator_episode = 1
cfg, create_cfg = [main_config, create_config]
create_cfg.policy.type = create_cfg.policy.type
if cfg.policy.cuda and torch.cuda.is_available():
cfg.policy.device = 'cuda'
else:
cfg.policy.device = 'cpu'
cfg = compile_config(cfg, seed=seed, env=None, auto=True, create_cfg=create_cfg, save_cfg=True)
# Create main components: env, policy
env_fn, collector_env_cfg, evaluator_env_cfg = get_vec_env_setting(cfg.env)
collector_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in collector_env_cfg])
evaluator_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in evaluator_env_cfg])
collector_env.seed(cfg.seed)
evaluator_env.seed(cfg.seed, dynamic_seed=False)
set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda)
self.policy = create_policy(cfg.policy, model=None, enable_field=['learn', 'collect', 'eval'])
# load pretrained model
if model_path is not None:
self.policy.learn_mode.load_state_dict(torch.load(model_path, map_location=cfg.policy.device))
def compute_action(self, obs):
# print(obs)
policy_output = self.policy.eval_mode.forward({'0': obs})
actions = {env_id: output['action'] for env_id, output in policy_output.items()}
return actions['0']
if __name__ == '__main__':
from easydict import EasyDict
from zoo.board_games.gomoku.envs.gomoku_env import GomokuEnv
cfg = EasyDict(
prob_random_agent=0,
board_size=15,
battle_mode='self_play_mode', # NOTE
channel_last=False,
scale=False,
agent_vs_human=False,
bot_action_type='v1', # {'v0', 'v1', 'alpha_beta_pruning'}
prob_random_action_in_bot=0.,
check_action_to_connect4_in_bot_v0=False,
render_mode='state_realtime_mode',
replay_path=None,
screen_scaling=9,
alphazero_mcts_ctree=False,
)
env = GomokuEnv(cfg)
obs = env.reset()
agent = Agent()
while True:
# 更新游戏环境
observation, reward, done, info = env.step(env.random_action())
# 如果游戏没有结束，获取 bot 的动作
if not done:
# agent_action = env.random_action()
agent_action = agent.compute_action(observation)
# 更新环境状态
_, _, done, _ = env.step(agent_action)
# 准备响应数据
print('orig bot action: {}'.format(agent_action))
agent_action = {'i': int(agent_action // 15), 'j': int(agent_action % 15)}
print('bot action: {}'.format(agent_action))
else:
break |