Most existing deep reinforcement learning (DRL) frameworks consider action spaces that are either
 discrete or continuous space. Motivated by the project of design Game AI for King of Glory
 (KOG), one the world’s most popular mobile game, we consider the scenario with the discrete-continuous
 hybrid action space. To directly apply existing DLR frameworks, existing approaches
 either approximate the hybrid space by a discrete set or relaxing it into a continuous set, which is
 usually less efficient and robust. In this paper, we propose a parametrized deep Q-network (P-DQN)
 for the hybrid action space without approximation or relaxation. Our algorithm combines DQN and
 DDPG and can be viewed as an extension of the DQN to hybrid actions. The empirical study on the
 game KOG validates the efficiency and effectiveness of our method.
A DQN and DDPG hybrid algorithm is proposed to deal with the discrete-continuous hybrid action space.