Title: 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.