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loop.py
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loop.py
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import sys
import numpy as np
from utils.GymRender import GymRender
def battle(env, players):
raise NotImplementedError
def test_loop(env, players):
render = GymRender()
env.register(players[0].name, players[-1].name)
env.reset()
now_player = 0 # 用于记录当前执子的选手
move_step = 1 # 用于记录该玩家走了几步，2步需换对方执子
total_step = 0 # 用于记录总的步数
while True:
total_step += 1
env.render()
# render.render(env)
while True: # 如果选择的动作不合法，就重新选择。这个一般只对随机智能体或者手动输入时使用，实现自己的智能体时可以根据需求计算出每一步的可行动空间，避免重新选择动作
try:
if now_player == 0:
print('黑子')
else:
print('白子')
x, y = players[now_player].choose_action(env)
print(players[now_player].name, x, y)
# input()
# print(f'{players[now_player].name}选择, x: {x+1}, y: {y+1}')
is_ok, msg = env.can_place(x, y)
if not is_ok:
# print(f'{msg}, 请换个位置')
continue
break
except KeyboardInterrupt:
print('游戏中断')
sys.exit()
env.step(x, y)
move_step += 1
end, winner = env.is_over()
if end:
env.render()
render.render(env)
print(env.last_move)
print(f'游戏结束，{players[now_player].name}获胜')
input()
break
if move_step == 2:
move_step = 0
now_player = (now_player + 1) % 2
def train_loop(env, players):
info = ['黑子', '白子']
offset = [2, 1] # 在将可选动作转为one_hot时，需要把棋盘无子位置转换成己方一子，offset用于将无子转为自己的子，如黑子，2-offset[0]=0，即为黑子
wins = [0, 0] # 用于统计每位玩家获胜的次数
for episode in range(10000):
if np.random.rand() > 0.5:
players.reverse()
wins.reverse()
for i in range(2):
if isinstance(players[i], MyPolicy):
players[i].update_offset(offset[i])
env.register(players[0].name, players[-1].name)
state = env.reset()
now_player = 0 # 用于记录当前执子的选手
move_step = 1 # 用于记录该玩家走了几步，2步需换对方执子
total_step = 0 # 用于记录总的步数
states = [
[state, state], # 黑棋状态
[state, state], # 白棋状态
]
while True:
total_step += 1
# env.render()
while True: # 如果选择的动作不合法，就重新选择。这个一般只对随机智能体或者手动输入时使用，实现自己的智能体时可以根据需求计算出每一步的可行动空间，避免重新选择动作
try:
x, y = players[now_player].choose_action(state)
# print(f'{players[now_player].name}选择, x: {x+1}, y: {y+1}')
is_ok, msg = env.can_place(x, y)
if not is_ok:
# print(f'{msg}, 请换个位置')
continue
break
except KeyboardInterrupt:
print('游戏中断')
sys.exit()
state = env.step(x, y)
states[now_player][-1] = state
move_step += 1
if wins[now_player] - wins[(now_player + 1) % 2] < 6:
players[now_player].learn() # 如果想让AI每两步学习一次，可以将其放置在下方判断中
end, winner = env.is_over()
if end:
if winner == -1:
r0, r1 = 0.1, 0.1
print(f'episode: {episode}, step: {total_step:>3d}, 平局')
else:
r0, r1 = 1, -move_step
print(f'episode: {episode:>4d}, step: {total_step:>3d}, {info[now_player]}{players[now_player].name}获胜')
wins[now_player] += 1
players[now_player].store(s=states[now_player][0], r=r0, s_=states[now_player][1], done=True)
players[now_player].writer_loop_summary(episode, reward=r0, step=total_step)
next_player = (now_player + 1) % 2
players[next_player].store(s=states[next_player][0], r=r1, s_=states[next_player][1], done=True)
players[next_player].writer_loop_summary(episode, reward=r1, step=total_step)
# env.render()
# print(env.last_move + 1)
# print(f'游戏结束，{players[now_player].name}获胜')
break
if move_step == 2:
move_step = 0
now_player = (now_player + 1) % 2
players[now_player].store(s=states[now_player][0], r=0, s_=states[now_player][1], done=False)
states[now_player][0] = states[(now_player + 1) % 2][1]