Created
May 2, 2023 09:13
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def update_q(self, states, actions, rewards, dones, next_states): | |
rewards = tf.clip_by_value(tf.reshape(rewards, (-1, 1)), -1.0, 1.0) | |
dones = tf.reshape(dones, (-1, 1)) | |
target_q = rewards + self.gamma * (1.0 - dones) * self.valuenet(next_states) | |
with tf.GradientTape() as tape: | |
q1, q2 = self.qnet(states, actions) | |
loss = tf.reduce_mean( | |
tf.square(target_q - q1) + tf.square(target_q - q2) | |
) | |
variables = self.qnet.trainable_variables | |
grads = tape.gradient(loss, variables) | |
self.q_optimizer.apply_gradients(zip(grads, variables)) | |
return loss |
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