PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games

1 code implementation • 19 Jul 2023 • Martin Balla, George E. M. Long, Dominik Jeurissen, James Goodman, Raluca D. Gaina, Diego Perez-Liebana

To bridge this gap, we introduce PyTAG, a Python API for interacting with the Tabletop Games framework (TAG).

Board Games Card Games +3

Task Relabelling for Multi-task Transfer using Successor Features

1 code implementation • 20 May 2022 • Martin Balla, Diego Perez-Liebana

Successor Features (SFs) proposes a mechanism that allows learning policies that are not tied to any particular reward function.

Design and Implementation of TAG: A Tabletop Games Framework

1 code implementation • 25 Sep 2020 • Raluca D. Gaina, Martin Balla, Alexander Dockhorn, Raul Montoliu, Diego Perez-Liebana

This document describes the design and implementation of the Tabletop Games framework (TAG), a Java-based benchmark for developing modern board games for AI research.

Board Games TAG

Evaluating Generalisation in General Video Game Playing

no code implementations • 22 May 2020 • Martin Balla, Simon M. Lucas, Diego Perez-Liebana

This paper focuses on the challenge of the GVGAI learning track in which 3 games are selected and 2 levels are given for training, while 3 hidden levels are left for evaluation.

Reinforcement Learning (RL)