Communication is a fundamental aspect of human society, facilitating the
exchange of information and beliefs among people. Despite the advancements in
large language models (LLMs), recent agents built with these often neglect the
control over discussion tactics, which are essential in communication scenarios
and games. As a variant of the famous communication game Werewolf, One Night
Ultimate Werewolf (ONUW) requires players to develop strategic discussion
policies due to the potential role changes that increase the uncertainty and
complexity of the game. In this work, we first present the existence of the
Perfect Bayesian Equilibria (PBEs) in two scenarios of the ONUW game: one with
discussion and one without. The results showcase that the discussion greatly
changes players' utilities by affecting their beliefs, emphasizing the
significance of discussion tactics. Based on the insights obtained from the
analyses, we propose an RL-instructed language agent framework, where a
discussion policy trained by reinforcement learning (RL) is employed to
determine appropriate discussion tactics to adopt. Our experimental results on
several ONUW game settings demonstrate the effectiveness and generalizability
of our proposed framework.

通过研究 One Night Ultimate Werewolf (ONUW) 游戏中的讨论策略对玩家效用的影响，本研究发现讨论对于改变玩家信念和提升讨论策略的重要性，并提出了基于强化学习的讨论策略训练框架，通过实验证明了该框架的效果和泛化能力。