title: PARAMETRIZED DEEP Q-NETWORKS LEARNING: PLAYING ONLINE BATTLE ARENA WITH DISCRETE-CONTINUOUS HYBRID ACTION SPACE
abstract: 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.
authors: Jiechao Xiong Qing Wang Zhuoran Yang Peng Sun Yang Zheng Lei Han Haobo Fu Xiangru Lian Carson Eisenach Haichuan Yang Emmanuel Ekwedike Bei Peng Haoyue Gao Tong Zhang Ji Liu Han Liu
hIndex: Ji Liu 38