PyTAG: Tabletop Games for Multi-Agent Reinforcement Learning

2024/05/28 by Martin Balla, Balla, Martin, George E.M. Long +7 · 1 citation
Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Transportation and Mobility Innovations

paper · pdf · doi:10.48550/arxiv.2405.18123

openalex publication_date 2024/05/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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