  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.
