  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.
