  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.
