Modern Tabletop Games present various interesting challenges for Multi-agent
Reinforcement Learning. In this paper, we introduce PyTAG, a new framework that
supports interacting with a large collection of games implemented in the
Tabletop Games framework. In this work we highlight the challenges tabletop
games provide, from a game-playing agent perspective, along with the
opportunities they provide for future research. Additionally, we highlight the
technical challenges that involve training Reinforcement Learning agents on
these games. To explore the Multi-agent setting provided by PyTAG we train the
popular Proximal Policy Optimisation Reinforcement Learning algorithm using
self-play on a subset of games and evaluate the trained policies against some
simple agents and Monte-Carlo Tree Search implemented in the Tabletop Games
framework.

本研究提出了 PyTAG 框架，支持与桌面游戏接口的互动，并探讨了桌面游戏对多智能体强化学习所带来的挑战和研究机会。通过在 PyTAG 上进行自我对弈训练，我们使用近端策略优化算法训练了模型，在一部分游戏上评估了训练得到的策略与某些简单智能体以及 Monte-Carlo Tree Search 的差异。