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icoxfog417/actor_critic_example_imp.py

Created March 19, 2019 23:11
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actor_critic_example_imp.py
def update(self, states, actions, rewards):
values = self.critic(states)
advantage = reward - tf.stop_gradient(values) # Prevent gradient flows to critic
action_probs = self.actor(states)
selected_action_probs = action_probs[self.to_one_hot(actions)]
neg_logs = - log(selected_action_probs)
policy_loss = reduce_mean(neg_logs * advantages)
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