Created
March 19, 2019 23:15
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| def update(self, states, actions, rewards, values): | |
| # Calculate values (or advantage) at outside of update process. | |
| advantage = reward - values | |
| 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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