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。

PyTAG：用于多智能体强化学习的桌面游戏

PyTAG: Tabletop Games for Multi-Agent Reinforcement Learning

In recent years, Game AI research has made important breakthroughs using
Reinforcement Learning (RL). Despite this, RL for modern tabletop games has
gained little to no attention, even when they offer a range of unique
challenges compared to video games. To bridge this gap, we introduce PyTAG, a
Python API for interacting with the Tabletop Games framework (TAG). TAG
contains a growing set of more than 20 modern tabletop games, with a common API
for AI agents. We present techniques for training RL agents in these games and
introduce baseline results after training Proximal Policy Optimisation
algorithms on a subset of games. Finally, we discuss the unique challenges
complex modern tabletop games provide, now open to RL research through PyTAG.

近年来，游戏人工智能研究在使用强化学习方面取得了重要突破，然而现代桌面游戏的强化学习领域却鲜有关注，尽管相较于电子游戏，桌面游戏提供了一系列独特的挑战。为填补这一空白，我们引入了 PyTAG，一个用于与 Tabletop Games 框架（TAG）交互的 Python API。TAG 框架涵盖了一系列超过 20 个现代桌面游戏，并提供了一种通用的 AI 代理 API。我们提出了在这些游戏中训练强化学习代理的技术，并在对游戏子集进行 Proximal Policy Optimisation 算法训练后介绍了基准结果。最后，我们讨论了现代桌面游戏提供的独特挑战，通过 PyTAG 面向强化学习研究的现在也就随之开放。