Deep Reinforcement Learning for General Video Game AI
Abstract
The General Video Game AI (GVGAI) competition and its associated software framework provides a way of benchmarking AI algorithms on a large number of games written in a domain-specific description language. While the competition has seen plenty of interest, it has so far focused on online planning, providing a forward model that allows the use of algorithms such as Monte Carlo Tree Search. In this paper, we describe how we interface GVGAI to the OpenAI Gym environment, a widely used way of connecting agents to reinforcement learning problems. Using this interface, we characterize how widely used implementations of several deep reinforcement learning algorithms fare on a number of GVGAI games. We further analyze the results to provide a first indication of the relative difficulty of these games relative to each other, and relative to those in the Arcade Learning Environment under similar conditions.
Keywords
Cite
@article{arxiv.1806.02448,
title = {Deep Reinforcement Learning for General Video Game AI},
author = {Ruben Rodriguez Torrado and Philip Bontrager and Julian Togelius and Jialin Liu and Diego Perez-Liebana},
journal= {arXiv preprint arXiv:1806.02448},
year = {2018}
}
Comments
8 pages, 4 figures, Accepted at the conference on Computational Intelligence and Games 2018 IEEE