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arXiv · 2307.09905

PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games

Martin Balla·George E. M. Long·Dominik Jeurissen·James Goodman·Raluca D. Gaina·Diego Perez-Liebana
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Abstract

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.

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Martin Balla, George E. M. Long, Dominik Jeurissen, James Goodman, Raluca D. Gaina, Diego Perez-Liebana. 2023-07-19. PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games. https://arxiv.org/abs/2307.09905

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