Marc G. Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling. The Arcade Learning Environment: An Evaluation Platform for General Agents. Journal of Artificial Intelligence Research, 47:253–279, 2013.
In this article we introduce the Arcade Learning Environment (ALE): both a challenge problem and a platform and methodology for evaluating the development of general, domain-independent AI technology. ALE provides an interface to hundreds of Atari 2600 game environments, each one different, interesting, and designed to be a challenge for human players. ALE presents significant research challenges for reinforcement learning, model learning, model-based planning, imitation learning, transfer learning, and intrinsic motivation. Most importantly, it provides a rigorous testbed for evaluating and comparing approaches to these problems. We illustrate the promise of ALE by developing and benchmarking domain-independent agents designed using well-established AI techniques for both reinforcement learning and planning. In doing so, we also propose a methodology for evaluation made possible by ALE, reporting empirical results on over 55 different games. All of the software, including the benchmark agents, is publicly available.
@Article(13jair-ale, Title = "The Arcade Learning Environment: An Evaluation Platform for General Agents", Author = "Marc G. Bellemare and Yavar Naddaf and Joel Veness and Michael Bowling", Journal = "Journal of Artificial Intelligence Research", Volume = "47", Pages = "253--279", Year = "2013", DOI = "10.1613/jair.3912" )