用户对问题“使用MultiDiscrete ActionSpace AttributeError的健身房RL：'MultiDiscrete‘对象没有属性’空格‘”的回答 |
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我正在尝试构建一个强化学习算法，它可以播放MasterMind Game。我使用的是MultiDiscrete功能和观测空间。动作空间有4个插槽，每个有6种颜色，观察空间是2x4。我创建了一个自定义环境来连接我的程序游戏。由于出现错误，环境还没有准备好。也许有人能帮我解决这个问题。 import gym as gym from gym import Env from gym.spaces import Discrete, Box, MultiDiscrete, Dict from stable_baselines3.common.policies import MultiInputActorCriticPolicy action_space = MultiDiscrete(np.array([6,6,6,6]), dtype=int) observation_space = MultiDiscrete(np.array([4,4]), dtype=int) ... class MasterMindEnv(Env): def __init__(self) -> None: super(MasterMindEnv, self).__init__() self.action_space = action_space self.observation_space = observation_space def step(self, action:np.ndarray): pass_action(action) output = get_output() print(output) reward = output[0] + output[1] print(reward) done = False info = {} return observation_space.sample(), 1, done, info def reset(self): return self.observation_space.sample() ... model = A2C(MultiInputActorCriticPolicy, env) model.learn(total_timesteps=1000)错误是： AttributeError Traceback (most recent call last) c:\...\model.ipynb Zelle 10 in ----> 1 model = A2C(MultiInputActorCriticPolicy, env) 2 model.learn(total_timesteps=1000) File c:\...\Python310\lib\site-packages\stable_baselines3\a2c\a2c.py:126, in A2C.__init__(self, policy, env, learning_rate, n_steps, gamma, gae_lambda, ent_coef, vf_coef, max_grad_norm, rms_prop_eps, use_rms_prop, use_sde, sde_sample_freq, normalize_advantage, tensorboard_log, create_eval_env, policy_kwargs, verbose, seed, device, _init_setup_model) 123 self.policy_kwargs["optimizer_kwargs"] = dict(alpha=0.99, eps=rms_prop_eps, weight_decay=0) 125 if _init_setup_model: --> 126 self._setup_model() File c:\...\Python310\lib\site-packages\stable_baselines3\common\on_policy_algorithm.py:123, in OnPolicyAlgorithm._setup_model(self) 112 buffer_cls = DictRolloutBuffer if isinstance(self.observation_space, gym.spaces.Dict) else RolloutBuffer 114 self.rollout_buffer = buffer_cls( 115 self.n_steps, 116 self.observation_space, (...) 121 n_envs=self.n_envs, 122 ) --> 123 self.policy = self.policy_class( # pytype:disable=not-instantiable ... --> 258 for key, subspace in observation_space.spaces.items(): 259 if is_image_space(subspace): 260 extractors[key] = NatureCNN(subspace, features_dim=cnn_output_dim) AttributeError: 'MultiDiscrete' object has no attribute 'spaces' 更新 class MasterMindEnv(Env): def __init__(self) -> None: super(MasterMindEnv, self).__init__() self.action_space = MultiDiscrete(np.array([6,6,6,6]), dtype=int) self.observation_space = MultiDiscrete(np.array([4,4]), dtype=int) def step(self, action:np.ndarray): output = observation_space.sample() reward = output[0] + output[1] done = False if (reward == 8): done = True info = {} return output, float(reward), done, info def reset(self): return self.observation_space.sample() env = MasterMindEnv() model = A2C("MlpPolicy", env) model.learn(total_timesteps=1000)它产生： RuntimeError Traceback (most recent call last) c:\...\model.ipynb Zelle 9 in 1 model = A2C("MlpPolicy", env) ----> 2 model.learn(total_timesteps=1000) File c:\...\Python310\lib\site-packages\stable_baselines3\a2c\a2c.py:203, in A2C.learn(self, total_timesteps, callback, log_interval, eval_env, eval_freq, n_eval_episodes, tb_log_name, eval_log_path, reset_num_timesteps, progress_bar) 189 def learn( 190 self: A2CSelf, 191 total_timesteps: int, (...) 200 progress_bar: bool = False, 201 ) -> A2CSelf: --> 203 return super().learn( 204 total_timesteps=total_timesteps, 205 callback=callback, 206 log_interval=log_interval, 207 eval_env=eval_env, 208 eval_freq=eval_freq, 209 n_eval_episodes=n_eval_episodes, 210 tb_log_name=tb_log_name, 211 eval_log_path=eval_log_path, 212 reset_num_timesteps=reset_num_timesteps, 213 progress_bar=progress_bar, 214 ) ... --> 464 return th.as_tensor(obs).to(device) 465 elif isinstance(obs, dict): 466 return {key: th.as_tensor(_obs).to(device) for (key, _obs) in obs.items()} RuntimeError: Could not infer dtype of numpy.int32 |
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