# 使用包裹后的环境运行一些episodeforepisode in range(2):obs = env.reset()done = Falsestep =0whilenotdone:action = env.action_space.sample() # 随机选择动作obs, reward, done, info = env.step(action)print(f"Episode: {episode},...
def train(arglist): with U.single_threaded_session(): # 创建TensorFlow单线程会话 # 环境初始化 env = make_env(arglist.scenario, arglist) obs_shape_n = [env.observation_space[i].shape for i in range(env.n)] # 训练器初始化(区分对抗者和合作者) num_adversaries = min(env.n, arglist...
env = get_env(task) obs_dim = env.observation_space.shape action_space = env.action_space if len(obs_dim) == 1: obs_dim = obs_dim[0] if hasattr(env.action_space, 'n'): act_dim = env.action_space.n else: act_dim = action_space.shape[0] return obs_dim, act_dim references...
while not done: action = env.action_space.sample() # 随机选择动作 obs, reward, done, info = env.step(action) print(f"Episode: {episode}, Step: {step}, Observation Shape: {obs.shape}, Reward: {reward}, Done: {done}") step += 1 print(f"Episode {episode} finished after {step} ...
#同上classMyEnv(core.Env):def__init__(self):self.action_space=spaces.Box(low=-1,high=1,shape=(1,))# 动作空间 self.observation_space=spaces.Box(low=-1,high=1,shape=(1,))# 状态空间 # 其他成员 defreset(self):...obs=self.get_observation()returnobs ...
self.observation_space = spaces.Box(low=-1, high=1, shape=(1, )) # 状态空间 # 其他成员 def reset(self): ... obs = self.get_observation() return obs def step(self, action): ... reward = self._get_reward() done = self._get_done() ...
reset() # model.predict(test_obs) would through an error # because the number of test env is different from the number of training env # so we need to complete the observation with zeroes zero_completed_obs = np.zeros((n_training_envs,) + envs.observation_space.shape) zero_completed_...
action:这是env.step()的唯一输入参数,表示智能体在当前状态下选择的动作。动作的值必须是环境动作空间(env.action_space)中的一个有效值。env.step()函数执行后的返回内容: observation:执行动作后的环境观测值,类型为对象(Object),具体形式取决于环境。 reward:执行该动作后智能体获得的奖励,类型为浮点数(Float...
24 - "act_space": self.action_spaces, 25 - "state_shape": self.dim_state, 26 - "n_actions": self.action_spaces.shape[-1], 29 + "observation_space": self.observation_space, 30 + "action_space": self.action_space, 31 + "state_space": self.state_space, 27 32 "episode...
action=env.action_space.sample() obs,reward,done,info=env.step(action) time.sleep(0.01) env.close() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 运行后,Output如下: Observation Space: Box(0, 255, (210, 160, 3), uint8) ...