discrete_action_conv_trainer.py 文件源码

def get_max_q_values(
        self,
        next_states: np.ndarray,
        possible_next_actions: Optional[np.ndarray] = None,
        use_target_network: Optional[bool] = True
    ) -> np.ndarray:
        q_values = self.get_q_values_all_actions(
            next_states, use_target_network
        )

        if possible_next_actions is not None:
            mask = np.multiply(
                np.logical_not(possible_next_actions),
                self.ACTION_NOT_POSSIBLE_VAL
            )
            q_values += mask

        return np.max(q_values, axis=1, keepdims=True)