A paper which was published this week by DeepMind, Google parent company Alphabet’s U.K.-based research division, a team of scientists explain Agent57, which they say the first system that performs better than humans on all 57 Atari games in the Arcade Learning Environment data set.
Assuming the claim holds water, Agent57 could lay the groundwork for more capable AI decision-making models than have been previously released. This could be a boon for enterprises looking to boost productivity through workplace automation; imagine AI that automatically completes not only mundane, repetitive tasks like data entry, but which reasons about its environment.
“With Agent57, we have succeeded in building a more generally intelligent agent that has above-human performance on all tasks in the Atari57 benchmark,” wrote the study’s coauthors. “Agent57 was able to scale with increasing amounts of computation: the longer it trained, the higher its score got.”
Arcade Learning Environment
As the researchers explain, the Arcade Learning Environment (ALE) was proposed as a platform for empirically assessing agents designed for general competency across a range of games. To this end, it offers an interface to a diverse set of Atari 2600 game environments intended to be engaging and challenging for human players.
Why Atari 2600 games? Chiefly because they’re (1) varied enough to claim generality, (2) interesting enough to be representative of settings that might be faced in practice, and (3) created by an independent party and thus free of experimenter’s bias. Agents are expected to perform well in as many games as possible, making minimal assumptions about the domain at hand and without the use of game-specific information.