PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games

• URL: http://arxiv.org/abs/2307.09905v1
• Date: Wed, 19 Jul 2023 11:08:59 GMT
• Title: PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games
• Authors: Martin Balla, George E.M. Long, Dominik Jeurissen, James Goodman, Raluca D. Gaina, Diego Perez-Liebana
• Abstract summary: 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.
• Score: 0.880802134366532
• License: http://creativecommons.org/licenses/by/4.0/
• 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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