Chapter

AI 2012: Advances in Artificial Intelligence

Volume 7691 of the series Lecture Notes in Computer Science pp 15-26

Optimistic Agents Are Asymptotically Optimal

  • Peter SunehagAffiliated withResearch School of Computer Science, Australian National University
  • , Marcus HutterAffiliated withResearch School of Computer Science, Australian National University

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Abstract

We use optimism to introduce generic asymptotically optimal reinforcement learning agents. They achieve, with an arbitrary finite or compact class of environments, asymptotically optimal behavior. Furthermore, in the finite deterministic case we provide finite error bounds.

Keywords

Reinforcement Learning Optimism Optimality