问题描述
这是我的神经网络模型代码。
self.gamma = 0.95
self.epsilon = 1.0
self.epsilon_min = 0.01
self.epsilon_decay = 0.999
tf.reset_default_graph()
self.sess = tf.InteractiveSession()
self.X = tf.placeholder(tf.float32,[None,self.state_space])
self.Y = tf.placeholder(tf.float32,self.action_space])
layer1 = tf.layers.dense(self.X,128,activation = tf.nn.leaky_relu)
layer2 = tf.layers.dense(layer1,64,activation = tf.nn.leaky_relu)
layer3 = tf.layers.dense(layer2,32,activation = tf.nn.leaky_relu)
self.outputs = tf.layers.dense(layer3,self.action_space,activation=tf.nn.softmax)
self.loss = tf.reduce_mean(tf.square(self.Y - self.outputs))
self.optimizer = tf.train.AdamOptimizer(1e-4).minimize(self.loss)
self.sess.run(tf.global_variables_initializer())
这是 Q 值的代码片段。
X = np.empty((replay_size,self.state_space))
Y = np.empty((replay_size,self.action_space))
states = np.array([a[0][0] for a in mini_batch])
new_states = np.array([a[3][0] for a in mini_batch])
Q = self.sess.run(self.outputs,Feed_dict = {self.X: states})
print("Q[0][0] = ",Q[0][0])
Q_new = self.sess.run(self.outputs,Feed_dict = {self.X: new_states})
for i in range(len(mini_batch)):
state,action,reward,next_state,done = mini_batch[i]
target = Q[i]
target[action] = reward
if not done:
target[action] += self.gamma * np.amax(Q_new[i])
X[i] = state
Y[i] = target
loss,O = self.sess.run([self.loss,self.optimizer],Feed_dict = {self.X: X,self.Y: Y})
被这个问题困扰了一个星期。任何帮助将不胜感激！
解决方法
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