The Arcade Learning Environment was introduced in “The Arcade Learning Environment: An Evaluation Platform for General Agents,” posted to arXiv on July 19, 2012 by Marc G. Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling at the University of Alberta, and later published in the Journal of Artificial Intelligence Research. It wraps hundreds of Atari 2600 games behind a common programming interface so that a single agent design can be tested across many distinct tasks.
The point of ALE was to push toward general agents rather than systems hand-built for one problem. Each game presents the same kind of input, raw screen pixels, and a score to maximize, but the games themselves demand very different skills, from reflexes to long-term planning. The original paper benchmarked both reinforcement learning and planning agents across more than 55 games, establishing an evaluation methodology that the field adopted.
ALE became the benchmark that defined a decade of deep reinforcement learning. The 2013 DQN result, and nearly every value-based advance after it, was measured on these games. For a general reader, ALE is a good illustration of how a well-chosen shared benchmark can organize an entire research community and make progress comparable.