|Title||Human Learning in Atari|
|Year of Publication||2017|
|Authors||Tsividis, P, Pouncy, T, Xu, JL, Tenenbaum, JB, Gershman, SJ|
Atari games are an excellent testbed for studying intelligent behavior, as they offer a range of tasks that differ widely in their visual representation, game dynamics, and goals presented to an agent. The last two years have seen a spate of research into artificial agents that use a single algorithm to learn to play these games. The best of these artificial agents perform at better-than-human levels on most games, but require hundreds of hours of game-play experience to produce such behavior. Humans, on the other hand, can learn to perform well on these tasks in a matter of minutes. In this paper we present data on human learning trajectories for several Atari games, and test several hypotheses about the mecha- nisms that lead to such rapid learning.
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