DQN的目标网络模型无法更新权重，不知道是怎么回事了。
求万能的网友解答，谢谢！
下面是DQN的代码：
import numpy as np
import tensorflow as tf
from tensorflow import keras
# 创建经验池
class ExperienceReplayBuffer:
def __init__(self, capacity=20000):
self.capacity = capacity
self.buffer = []
self.position = 0
self.length = len(self.buffer)
def push(self, state, action, reward, next_state, done):
if len(self.buffer) < self.capacity:
self.buffer.append(None)
self.buffer[self.position] = (state, action, reward, next_state, done)
self.position = (self.position + 1) % self.capacity
def sample(self, batch_size):
# batch = random.sample(self.buffer, batch_size)
batch = np.random.choice(self.buffer, batch_size, replace=False)
states, action, rewards, next_states, dones = zip(*batch)
return np.array(states), np.array(action), np.array(rewards, dtype=np.float32), np.array(
next_states), np.array(dones)
def __len__(self):
# return len(self.buffer)
return self.length
# 创建DQN模型
class DQNModel(tf.keras.Model):
def __init__(self, num_action):
super(DQNModel, self).__init__()
self.dense1 = keras.layers.Dense(256, kernel_regularizer=keras.regularizers.L2(0.001), activation='relu')
self.dense2 = keras.layers.Dense(64, kernel_regularizer=keras.regularizers.L2(0.001), activation='relu')
self.dense3 = keras.layers.Dense(num_action, kernel_regularizer=keras.regularizers.L2(0.001), activation='linear')
def call(self, inputs):
x = self.dense1(inputs)
x = self.dense2(x)
outputs = self.dense3(x)
return outputs
# 创建DQN Agent
class DQNAgent:
def __init__(self, num_action):
self.num_actions = num_action
self.model = DQNModel(num_action)
self.target_model = DQNModel(num_action)
self.target_model.set_weights(weights=self.model.get_weights())
self.optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
self.loss_function = tf.keras.losses.MeanSquaredError()
def update_target_model(self):
self.target_model.set_weights(self.model.get_weights())
# pass
def get_action(self, state, epsilon):
if np.random.rand() <= epsilon:
return np.random.choice([-1, 1])
else:
q_values = self.model.predict(state)
return np.argmax(q_values[0])
def train(self, replay_buffer, batch_size, discount_factor):
if len(replay_buffer) < batch_size:
return
states, action, rewards, next_states, dones = replay_buffer.sample(batch_size)
next_q_values = self.target_model.predict(next_states)
max_next_q_values = np.max(next_q_values, axis=1)
target_q_values = rewards + (1 - dones) * discount_factor * max_next_q_values
mask = tf.one_hot(action, self.num_actions)
with tf.GradientTape() as tape:
q_values = self.model(states)
q_action = tf.reduce_sum(tf.multiply(q_values, mask), axis=1)
loss = self.loss_function(target_q_values, q_action)
grads = tape.gradient(loss, self.model.trainable_variables)
self.optimizer.apply_gradients(zip(grads, self.model.trainable_variables))
报错是：You called set_weights(weights) on layer "dqn_model_1" with a weight list of length 6, but the layer was expecting 0 weights. Provided weights:[......]
【以下回答由 GPT 生成】
解决方案:
从提供的代码中可以看出，目标网络模型的权重没有进行更新。在DQNAgent类中，虽然定义了两个模型（self.model和self.target_model），但是没有定义更新目标网络权重的逻辑。
要解决这个问题，需要添加一些代码来更新目标网络模型的权重。
以下是一个可能的解决方案：
首先，在DQNAgent类中添加一个方法来更新目标网络模型的权重：
class DQNAgent:
def __init__(self, num_action):
self.num_actions = num_action
self.model = DQNModel(num_action)
self.target_model = DQNModel(num_action) # 添加目标网络模型
self.update_target_model() # 初始化时更新目标网络模型的权重
def update_target_model(self):
self.target_model.set_weights(self.model.get_weights())
然后，在训练循环中，每隔一定时间更新目标网络模型的权重：
def train_dqn(agent, replay_buffer, target_update_interval=100):
# 训练循环
for episode in range(num_episodes):
for step in range(max_steps_per_episode):
# 执行动作、观察环境、更新经验池等
# ...
# 每隔一定时间更新目标网络模型的权重
if step % target_update_interval == 0:
agent.update_target_model()
# 使用经验池中的样本进行训练、更新模型等
# ...
通过添加以上代码，可以在训练循环中定期更新目标网络模型的权重，以确保它与主网络模型保持一致。
希望以上解决方案对你有帮助！如果问题仍然存在，请提供更多相关代码和错误信息，以便我能够更进一步地帮助你解决问题。