def __init__(self, **kwargs):
self.game = FlappyBird()
self.p = PLE(self.game, **kwargs)
self.action_set = self.p.getActionSet()
# 3个输入状态：见函数self._get_obs
self.observation_space = spaces.Discrete(3)
# 两个输出状态：跳或者不跳
self.action_space = spaces.Discrete(2)
def __init__(self, reward_values=None, reward_discount=0.99, pip_gap=100,
display_screen=True, fps=30, force_fps=True):
if reward_values is None:
reward_values = {}
self.game = PLE(FlappyBird(pipe_gap=pip_gap), reward_values=reward_values,
fps=fps, force_fps=force_fps, display_screen=display_screen)
self.game.init()
self.actions = self.game.getActionSet()
self.reward_discount = reward_discount
class FlappyBirdWrapper(Env):
# 如果想把画面渲染出来，就传参display_screen=True
def __init__(self, **kwargs):
self.game = FlappyBird()
self.p = PLE(self.game, **kwargs)
self.action_set = self.p.getActionSet()
# 3个输入状态：见函数self._get_obs
self.observation_space = spaces.Discrete(3)
# 两个输出状态：跳或者不跳
self.action_space = spaces.Discrete(2)
def _get_obs(self):
# 获取游戏的状态
state = self.game.getGameState()
# 小鸟与它前面一对水管中下面那根水管的水平距离
dist_to_pipe_horz = state["next_pipe_dist_to_player"]
# 小鸟与它前面一对水管中下面那根水管的顶端的垂直距离
dist_to_pipe_bottom = state["player_y"] - state["next_pipe_top_y"]
# 获取小鸟的水平速度
velocity = state['player_vel']
# 将这些信息封装成一个数据返回
return np.array([dist_to_pipe_horz, dist_to_pipe_bottom, velocity])
def reset(self):
self.p.reset_game()
return self._get_obs()
def step(self, action):
reward = self.p.act(self.action_set[action])
obs = self._get_obs()
done = self.p.game_over()
return obs, reward, done, dict()
def seed(self, *args, **kwargs):
pass
def render(self, *args, **kwargs):
pass
target_f = self.model.predict(state)
target_f[0][action] = target
self.model.fit(state, target_f, epochs=1, verbose=0)
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay
def load(self, name):
self.model.load_weights(name)
def save(self, name):
self.model.save_weights(name)
if __name__ == "__main__":
game = FlappyBird()
game.allowed_fps = 30
p = PLE(game, fps=30, display_screen=True)
agent = DQNAgent(8, 2)
if os.path.exists('memory.h5py'):
agent.load('memory.h5py')
atexit.register(agent.save, 'memory.h5py')
p.init()
reward = 0.0
max_score = 0
current_score = 0
while True:
state = parse_game_state(game.getGameState())
for frame in range(1000):
action = np.argmax(agent.model.predict(state)[0])
pipe_1_top_y = list[3]
pipe_1_bottom_y = list[4]
list[3] = pipe_1_top_y - y_position
list[4] = pipe_1_bottom_y - y_position
pipe_2_top_y = list[6]
pipe_2_bottom_y = list[7]
list[6] = pipe_2_top_y - y_position
list[7] = pipe_2_bottom_y - y_position
if (len(list) == 8):
out = np.array([list])
return out
game = FlappyBird()
p = PLE(game, fps=30, display_screen=False, force_fps=False)
p.init()
scores = []
length = []
agent = DQL.deep_learner(8, 2, p.getActionSet())
nb_games = 0
nb_max = 1000
for layer in agent.model.layers:
print(layer.get_config())
step = 0
reward = 0
self.is_test = True
self.p.reset_game()
total_reward = 0
while not self.p.game_over():
action = self.select_action(self.state())
total_reward += self.step(action)
logger.info("Total-Reward: %s" % total_reward)
def seed_torch(seed_):
torch.manual_seed(seed_)
if torch.backends.cudnn.enabled:
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
if __name__ == '__main__':
hyper = HYPER_PARAMS.copy()
hyper['obs_dim'] = 8
hyper['action_dim'] = 2
hyper['epoch_log'] = 1000
hyper['epochs'] = 1000000
game = FlappyBird()
agent = Agent(hyper, game)
agent.train()
agent.test()
def act(self, p, action):
"""
执行动作
:param p: 通过p来向游戏发出动作命令
:param action: 动作
:return: 奖励
"""
r = p.act(self.action_set[action])
return r
if __name__ == "__main__":
# 训练次数
episodes = 20000
# 实例化游戏对象
game = FlappyBird()
# 类似游戏的一个接口，可以为我们提供一些功能
p = PLE(game, fps=30, display_screen=True)
# 初始化
p.init()
# 实例化Agent，将动作集传进去
agent = Agent(p.getActionSet())
for episode in range(episodes):
# 重置游戏
p.reset_game()
# 获得状态
state = agent.get_state(game.getGameState())
while True:
# 获得最佳动作
import matplotlib.pyplot as plt
import numpy as np
import rl.deep_Q_learning as DQL
import h5py
def process_obs(obs):
list = []
for item in obs:
list.append(obs[item])
if (len(list) == 8):
out = np.array(list)
return out
game = FlappyBird()
p = PLE(game, fps=30, display_screen=True, force_fps=False)
p.init()
scores = []
agent = DQL.deep_learner(1, 2, p.getActionSet())
agent.learning_rate = 0.001
agent.epsilon = 0.001
agent.epsilon_decay = 1.0
agent.epsilon_min = 0.00001
#agent.load_model_json()
agent.model.load_weights('my_model.h5')
nb_games = 0