Optimistic Agents are Asymptotically Optimal

Published 29 Sep 2012 in cs.AI and cs.LG | (1210.0077v1)

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.

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