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.

翻译：现代桌面游戏为多智能体强化学习提出了诸多具有挑战性的研究课题。本文提出PyTAG——一个支持与大量基于Tabletop Games框架实现的游戏进行交互的新型框架。本研究从游戏智能体的视角，系统阐述了桌面游戏带来的技术挑战及其为未来研究创造的机遇。同时，我们深入探讨了在这些游戏上训练强化学习智能体所涉及的技术难点。为探索PyTAG提供的多智能体环境，我们在部分游戏上采用自我对弈方式训练了经典的近端策略优化强化学习算法，并将训练所得策略与Tabletop Games框架中实现的简单智能体及蒙特卡洛树搜索算法进行了对比评估。