Question

我试图将keras-rl与OpenAIGym一起使用，并且当我将环境设置为Cartpole游戏时，它可以工作。问题是，当我尝试将环境设置为RecoGYM（在this paper呈现）时，我无法使其正常工作。这是我的代码：

lr = 1e-3
window_length = 2
emb_size = 100
look_back = 10

# "Expert" (regular dqn) model architecture
expert_model = Sequential()
expert_model.add(Embedding(env.action_space.n, emb_size, input_length=look_back, mask_zero=True))
expert_model.add(LSTM(100))
expert_model.add(Dense(env.action_space.n, activation='softmax'))

# memory
memory = PrioritizedMemory(limit=5000,  window_length=window_length)

# policy
policy = BoltzmannQPolicy()

# agent
dqn = DQNAgent(model=expert_model, nb_actions=env.action_space.n, policy=policy, memory=memory, 
               enable_double_dqn=False, enable_dueling_network=False, #gamma=.3, 
               target_model_update=1e-2, nb_steps_warmup=100)

dqn.compile(Adam(lr), metrics=['mae'])

env.reset()
train = dqn.fit(env, nb_steps=50000, visualize=False, verbose=1, nb_max_episode_steps = None)

但是只有在这种环境下，我才会收到此错误：

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-5-fe4e63a38263> in <module>
     27 
     28 env.reset()
---> 29 train = dqn.fit(env, nb_steps=50000, visualize=False, verbose=1, nb_max_episode_steps = None)
     30 np.savetxt(fichero_train_history, 
     31            np.array(train.history["episode_reward"]), delimiter=",")

c:\users\myuser\src\keras-rl\rl\core.py in fit(self, env, nb_steps, action_repetition, callbacks, verbose, visualize, nb_max_start_steps, start_step_policy, log_interval, nb_max_episode_steps)
    133                     if self.processor is not None:
    134                         observation = self.processor.process_observation(observation)
--> 135                     assert observation is not None
    136 
    137                     # Perform random starts at beginning of episode and do not record them into the experience.

AssertionError:

环境定义如下：

from reco_gym import env_1_args

# you can overwrite environment arguments here:
env_1_args['random_seed'] = 1234
env_1_args['num_products'] = 42

ENV_NAME = "reco-gym-v1"
env = gym.make(ENV_NAME)
env.init_gym(env_1_args)
np.random.seed(123)
env.reset()

OpenAIGym环境观察为无

0 个答案: