[2O5-E-3] Agents: reasoning and integration

[2O5-E-3-03] Using Q-learning and Estimation of Role in Werewolf Game

〇Makoto Hagiwara¹, Ahmed Moustafa¹, Takayuki Ito¹ (1. Nagoya Institute of Technology)

Keywords:reinforcement learning, multi-agent, AIWerewolf

This paper introduces a novel construction strategy in Werewolf Game using reinforcement learning(RL). Were- wolf Game is a type of incomplete information games in which the final results of the game is linked to the success or failure in communication. In this paper, we propose a model that uses RL and estimating other agent’s role in order to learn playing strategy in Werewolf Game. In the proposed model, RL is used for deciding the actions of the learning agent and Naive Bayes classifier is used in order to estimate other agent’s role. Up till now, there is no previous research that has effectively applied RL in Werewolf Game among existing AIwolfs in large scale environ- ments. Therefore, by combining RL and estimation of other agent’s role, we demonstrate through experimentation that the proposed approach achieved high level of performance in 11 people Werewolf Game.