Mullet's Gambit: Explaining Learned Strategies in the Chef's Hat Multiplayer Card Game

Reinforcement learning (RL)-based agents have demonstrated remarkable performance in multiplayer card game environments such as Chef's Hat. However, understanding why these agents excel in such dynamic and competitive settings remains a challenging endeavor. In this paper, we propose a novel method, named the 'Mullet's Gambit' to elucidate the strategies employed by RL-based agents within the context of the Chef's Hat card game. This method aims to provide insights into how RL-based agents navigate the complexities of multiplayer dynamics and assess their impact on opponents. By employing Mullet's Gambit, this investigation reveals the unique traits and efficacy of RL-based strategies compared to heuristic methodologies. This leads to the inference that RL-based agents not only acquire the skills to win but also to disrupt their opponents, thereby minimizing their potential actions.