Most existing deep reinforcement learning (DRL) frameworks consider either
discrete action space or continuous action space solely. Motivated by
applications in computer games, we consider the scenario with
discrete-continuous hybrid action space. To handle hybrid action space,
previous works either approximate the hybrid space by discretization, or relax
it into a continuous set. In this paper, we propose a parametrized deep
Q-network (P- DQN) framework for the hybrid action space without approximation
or relaxation. Our algorithm combines the spirits of both DQN (dealing with
discrete action space) and DDPG (dealing with continuous action space) by
seamlessly integrating them. Empirical results on a simulation example, scoring
a goal in simulated RoboCup soccer and the solo mode in game King of Glory
(KOG) validate the efficiency and effectiveness of our method.

在本文中，我们提出了一种带参数的深度 Q 网络（P-DQN）框架，用于处理混合行动空间，此算法无需任何逼近或者弛豫，充分发挥 DQN 和 DDPG 精神，并且在 RoboCup 足球和王者荣耀游戏中的仿真实验证明了我们方法的有效性。