Application of Deep Reinforcement Learning in Werewolf Game Agents

Abstract

Werewolf, also known as Mafia, is a kind of game with imperfect information. Werewolf game agents must cope with two kinds of problems, "decision on who to trust or to kill", and "decision on information exchange". In this paper, we focus on the first problem. We apply techniques in Deep Q Network in building werewolf agents. We also improve representation of states and actions based on existing agents trained by Q learning method. Our proposed agents were compared with existing agents trained by Q learning method and with existing agents submitted to the AIWolf Contest, the most famous werewolf game agents contest in Japan. For every role, we prepared four agents with proposed method and investigated average win ratio of four agents in our experiments. Experimental results showed that when agents learned and played with the same group of players, our proposed agents have better player performances than existing agents trained by Q learning method and a part of agents submitted to the AIWolf Contest. We obtained promising results by using reinforcement learning method to solve "decision on who to trust or to kill" problem without using heuristic methods.