double_dqn.py 文件源码

def _compute_target_values(self, exp_batch, gamma):

        batch_next_state = exp_batch['next_state']

        with chainer.using_config('train', False):
            with state_kept(self.q_function):
                next_qout = self.q_function(batch_next_state)

        target_next_qout = self.target_q_function(batch_next_state)

        next_q_max = target_next_qout.evaluate_actions(
            next_qout.greedy_actions)

        batch_rewards = exp_batch['reward']
        batch_terminal = exp_batch['is_state_terminal']

        return batch_rewards + self.gamma * (1.0 - batch_terminal) * next_q_max