Keras：AttributeError：“Adam”对象没有属性“_name”

Question

我想编译我的 DQN 代理，但出现错误： AttributeError: 'Adam' object has no attribute '_name'，

DQN = buildAgent(model, actions)
DQN.compile(Adam(lr=1e-3), metrics=['mae'])

我尝试添加假的_name，但它不起作用，我正在遵循教程并且它可以在导师的机器上运行，这可能是一些新的更新更改，但如何解决这个问题

这是我的完整代码：

from keras.layers import Dense, Flatten
import gym
from keras.optimizer_v1 import Adam
from rl.agents.dqn import DQNAgent
from rl.policy import BoltzmannQPolicy
from rl.memory import SequentialMemory

env = gym.make('CartPole-v0')
states = env.observation_space.shape[0]
actions = env.action_space.n

episodes = 10

def buildModel(statez, actiones):
    model = Sequential()
    model.add(Flatten(input_shape=(1, statez)))
    model.add(Dense(24, activation='relu'))
    model.add(Dense(24, activation='relu'))
    model.add(Dense(actiones, activation='linear'))
    return model

model = buildModel(states, actions)

def buildAgent(modell, actionz):
    policy = BoltzmannQPolicy()
    memory = SequentialMemory(limit=50000, window_length=1)
    dqn = DQNAgent(model=modell, memory=memory, policy=policy, nb_actions=actionz, nb_steps_warmup=10, target_model_update=1e-2)
    return dqn

DQN = buildAgent(model, actions)
DQN.compile(Adam(lr=1e-3), metrics=['mae'])
DQN.fit(env, nb_steps=50000, visualize=False, verbose=1)

Answer 1

您的错误来自于导入Adam，您可以使用以下方法from keras.optimizer_v1 import Adam解决您的问题：tf.keras.optimizers.AdamTensorFlow >= v2

（该lr参数已被弃用，最好使用它learning_rate。）

# !pip install keras-rl2
import tensorflow as tf
from keras.layers import Dense, Flatten
import gym
from rl.agents.dqn import DQNAgent
from rl.policy import BoltzmannQPolicy
from rl.memory import SequentialMemory

env = gym.make('CartPole-v0')
states = env.observation_space.shape[0]
actions = env.action_space.n
episodes = 10

def buildModel(statez, actiones):
    model = tf.keras.Sequential()
    model.add(Flatten(input_shape=(1, statez)))
    model.add(Dense(24, activation='relu'))
    model.add(Dense(24, activation='relu'))
    model.add(Dense(actiones, activation='linear'))
    return model

def buildAgent(modell, actionz):
    policy = BoltzmannQPolicy()
    memory = SequentialMemory(limit=50000, window_length=1)
    dqn = DQNAgent(model=modell, memory=memory, policy=policy, 
                   nb_actions=actionz, nb_steps_warmup=10, 
                   target_model_update=1e-2)
    return dqn

model = buildModel(states, actions)
DQN = buildAgent(model, actions)
DQN.compile(tf.keras.optimizers.Adam(learning_rate=1e-3), metrics=['mae'])
DQN.fit(env, nb_steps=50000, visualize=False, verbose=1)