PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games
This work addresses a gap in AI research for tabletop games, providing tools and benchmarks for future studies, though it is incremental in applying existing methods to a new domain.
The authors tackled the lack of reinforcement learning research in modern tabletop games by introducing PyTAG, a Python API for the Tabletop Games framework, and presented baseline results from training Proximal Policy Optimization algorithms on a subset of games.
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.