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a guest Mar 25th, 2019 66 Never
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  1. def update(self, states, actions, rewards, values):
  2.     # Calculate values (or advantage) at outside of update process.
  3.     advantage = reward - values
  4.     action_probs = self.actor(states)
  5.     selected_action_probs = action_probs[self.to_one_hot(actions)]
  6.     neg_logs = - log(selected_action_probs)
  7.     policy_loss = reduce_mean(neg_logs * advantages)
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