@conference {3639, title = {Human Learning in Atari}, booktitle = {AAAI Spring Symposium Series}, year = {2017}, abstract = {

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 mechanisms that lead to such rapid learning.\ 

}, author = {Pedro Tsividis and Thomas Pouncy and Jacqueline L. Xu and Joshua B. Tenenbaum and Samuel J Gershman} }