49dea39d34a562a46daa562051a66846326f66f4,rl_coach/agents/pal_agent.py,PALAgent,learn_from_batch,#PALAgent#Any#,53

Before Change

                TD_targets[i, batch.actions()[i]] -= self.alpha * advantage_learning_update

            // mixing monte carlo updates
            monte_carlo_target = batch.total_returns()[i]
            TD_targets[i, batch.actions()[i]] = (1 - self.monte_carlo_mixing_rate) * TD_targets[i, batch.actions()[i]] \
                                        + self.monte_carlo_mixing_rate * monte_carlo_target

After Change

        // calculate TD error
        TD_targets = np.copy(q_st_online)
        total_returns = batch.n_step_discounted_rewards()
        for i in range(self.ap.network_wrappers["main"].batch_size):
            TD_targets[i, batch.actions()[i]] = batch.rewards()[i] + \
                                        (1.0 - batch.game_overs()[i]) * self.ap.algorithm.discount * \
                                                     q_st_plus_1_target[i][selected_actions[i]]

Instances