class VacuumEnv(gym.Env):
def __init__(self, size=8, max_episodes=100, gen_proba=0.5, collide='sym'):
self.viewer = None
self.done = None
self.game = Game(max_episodes, gen_proba, size, collide)
self.action_space = spaces.Discrete(4)
self.observation_space = spaces.Box(0, 1, shape=(4, ), dtype=np.uint8)
def step(self, action=None):
obs = self.game.step(action)
self.done = self.game.finished()
reward = -self.game.garbage_count if self.done else 0
info = {
'garbage_count': self.game.garbage_count,
'gathered': self.game.agent.gathered,
'episodes_left': self.game.episodes_left
}
return np.array(obs), reward, self.done, info
def reset(self):
obs = self.game.reset()
self.done = False
return np.array(obs)
def render(self, mode='window', size='small'):
if self.viewer is None:
if mode == 'window':
self.viewer = WindowVisualizer(self.game, size)
elif mode == 'terminal':
self.viewer = TerminalVisualizer(self.game)
self.viewer.render()
def close(self):
if self.viewer:
self.viewer.close()
self.viewer = None
# -*- coding:utf-8 -*-
import random
import time
from core import Game, Grid
from agents import RandomAgent, MinMaxAgent
if __name__ == '__main__':
env = Game()
# agent = RandomAgent(action_num=4, env=env)
agent = MinMaxAgent(env=env, maxSearchTime=100)
while not env.isOver:
act, move = agent.step()
print("当前下法：", move)
env.step(act)
print("当前棋盘:\n{}".format(env.state))
print("当前最大值:{}\n".format(env.maxValue))
print("最终得分：", env.score)