def main():
NUM_OF_GAMES = 100
ai = RandomAgent()
s = Sim()
all_records = []
for i in tqdm(range(NUM_OF_GAMES)):
records = []
# preparation of simulator
s.reset_s() # 引数のなしの時は何も置かれていない状態となる
while True:
# ban を上書きする前に，勝敗を保存する？
reshape_self, reshape_opp, reshape_ban, kou = s.get_s()
if reshape_ban != 2:
act_num = ai.act(reshape_self, reshape_opp, reshape_ban, kou)
s.act(act_num)
else:
# 黒の勝ち+1,負け-1
outcome = 2 * is_black_win(s) - 1
break
records.append(
[reshape_self[1:], reshape_opp[1:], reshape_ban, act_num])
#outcomeを付け足し
for j in range(len(records)):
records[j].append((1. - 2. * (j % 2)) * outcome)
all_records.append(records)
# stackしたレコードを書き出す
w_fnc(all_records, 'all_records.csv')
rec = [records[0][-1] for records in all_records]
kuro_win = NUM_OF_GAMES / 2 + sum(rec) / 2
siro_win = NUM_OF_GAMES - kuro_win
print('黒の勝ち:{0}回, 白の勝ち:{1}回'.format(kuro_win, siro_win))
def main():
# preparation of pygame
pygame.init() # 初期化
AIis = 2 #１が黒，2が白
ai = RandomAgent()
bp = [115, 15]
g = 40
mar = 20
x, y = 0, 0
pixels = [bp, g, mar]
screen = pygame.display.set_mode((600, 400)) # ウィンドウサイズの指定
pygame.display.set_caption("GoSimulator") # ウィンドウの上の方に出てくるアレの指定
sysfont = pygame.font.SysFont(None, 40)
# preparation of simulator
s = Sim()
s.reset_s() # 引数のなしの時は何も置かれていない状態となる
while True:
screen.fill((
0,
100,
0,
)) # 背景色の指定。
# state,ban = s.get_s()
# gui用に整形をかませる
reshape_self, reshape_opp, reshape_ban, kou = s.get_s()
ban = 2 - int(reshape_ban)
# game over すなわち ban == 0の時の処理
if ban != 0:
state = ban * reshape_self + (3. - ban) * reshape_opp
bl, wh = s.get_eval()
# TODO for debug
draw(pygame, screen, sysfont, pixels, state, ban, x, y, bl, wh)
pygame.display.update() # 画面更新
if ban == AIis:
s.act(ai.act(reshape_self, reshape_opp, reshape_ban, kou))
else:
for event in pygame.event.get(): # 終了処理
if event.type == QUIT:
pygame.quit()
sys.exit()
if event.type == MOUSEBUTTONDOWN and event.button == 1:
x, y = event.pos
num = convert_to_num(pixels, x, y)
if num == -1:
s.reset_s()
else:
if num in s.regal_acts() and ban != 0:
s.act(num)
bl, wh = s.get_eval()
if len(bl) != len(set(bl)):
print('[error] get_eval has over lap')
def train_chaser(env_name, chaser_need_restore, episode_count, close=False):
"""
Use Actor-Critic TD(0) method to train runner.
Cannot use batch method to optimize model.
"""
env = gym.make(env_name)
features_n = 4
chaser = ActorCriticAgent(300,
300,
30,
color=(1.0, 0.0, 0.0),
agent_type=AgentType.Chaser,
features_n=features_n,
discounted_value=0.9,
learning_rate=1e-6,
need_restore=chaser_need_restore)
runner = RandomAgent(500,
500,
30,
color=(0.0, 1.0, 0.0),
agent_type=AgentType.Runner,
env=env)
env.add_agent(chaser)
env.add_agent(runner)
total_steps = 0
losses = []
for epi in range(episode_count):
chaser_state = env.reset()
step = 0
while True:
env.render(close=close)
action = chaser.act(chaser_state)
chaser_state_, reward, done, _ = env.step(chaser.type, action)
chaser.memory.append(
(chaser_state, action, chaser_state_, reward, done))
chaser_state = chaser_state_
step += 1
total_steps += 1
if step >= 500:
# When episode's steps above 500, break up this episode
chaser.memory.clear()
break
if done:
loss = chaser.optimize_model()
print('Episode: %d\tsteps: %d\tloss: %f' %
(epi + 1, step + 1, loss))
losses.append(loss)
break
else:
runner_action = runner.act(chaser_state)
_, _, done, _ = env.step(runner.type, runner_action)
env.render(close=close)
step += 1
total_steps += 1
if done:
chaser.memory.clear()
print('Episode: %d\tsteps: %d' % (epi + 1, step + 1))
break
# 每个回合结束保存模型
if epi % 100 == 0:
chaser.save()
if __name__ == "__main__":
sample_batch_size = 32
observation_shape = env.observation_space.shape
action_shape = env.action_space.n
first_episode = True
episode_rewards = list()
steps = 0
PRE_TRAINLENGTH = 3000
## Random Agent
ragent = RandomAgent(action_shape)
obs = env.reset()
for step in range(PRE_TRAINLENGTH):
#env.render()
action = ragent.act(obs)
next_obs, reward, done, info = env.step(action)
next_obs = np.reshape(next_obs, [1, observation_shape[0]])
ragent.observe((obs, action, reward, next_obs, done))
obs = next_obs
agent = Shallow_Q_Learner(observation_shape,
action_shape,
memory=ragent.get_memory())
for episode in range(MAX_NUM_EPISODES):
obs = env.reset()
obs = np.reshape(obs, [1, observation_shape[0]])
cum_reward = 0.0 # Cumulative reward
for step in range(MAX_STEPS_PER_EPISODE):
env.seed(38)
return env
# test env:
if __name__ == '__main__':
env = env_fn()
obs = env.reset()
env.render()
agent = RandomAgent(env.action_space)
reward = 0
done = False
n_steps = 20
for step in range(n_steps):
print("Step {}".format(step + 1))
action = agent.act(obs, reward, done)
obs, reward, done, info = env.step(action)
print('action=', action, 'obs=', obs, 'reward=', reward, 'done=', done)
env.render()
if done:
print("Goal reached!", "reward=", reward)
break
env.close()
game_reward_p2 = 0
# p1 starts.
turn = 1
state = game.get_state()
while not is_terminal:
if turn == 1:
action = p1.act(state)
states_actions_p1.append((deepcopy(state), action))
state, game_reward_p1, is_terminal = \
game.step(player=1, action=action, display=False)
game_reward_p2 = game_reward_p1 * -1
turn = 2
elif turn == 2:
action = p2.act(state)
# states_actions_p2.append((deepcopy(state), action))
state, game_reward_p2, is_terminal = \
game.step(player=2, action=action, display=False)
game_reward_p1 = game_reward_p2 * -1
turn = 1
p1.learn(states_actions_p1, game_reward_p1)
#p2.learn(states_actions_p2, game_reward_p2)
if game_reward_p1 == 1:
win_p1 += 1
elif game_reward_p1 == 0:
draw += 1
if game_num % BATCH_SIZE == 0:
