  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.
