%0 Conference Paper %B AAAI Spring Symposium Series %D 2017 %T Human Learning in Atari %A Pedro Tsividis %A Thomas Pouncy %A Jacqueline L. Xu %A Joshua B. Tenenbaum %A Samuel J Gershman %X

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. 

%B AAAI Spring Symposium Series %G eng %0 Conference Proceedings %B Annual Meeting of the Cognitive Science Society (CogSci) %D 2015 %T Hypothesis-Space Constraints in Causal Learning %A Pedro Tsividis %A Joshua B. Tenenbaum %A Laura Schulz %B Annual Meeting of the Cognitive Science Society (CogSci) %C Pasadena, CA %8 07/2015 %G eng %U https://mindmodeling.org/cogsci2015/papers/0418/index.html %0 Generic %D 2015 %T Information Selection in Noisy Environments with Large Action Spaces %A Pedro Tsividis %A Samuel J Gershman %A Joshua B. Tenenbaum %A Laura Schulz %B 9th Biennial Conference of the Cognitive Development Society %V Columbus, OH %G eng