Optimistic agents are asymptotically optimal
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Sunehag, Peter
Hutter, Marcus
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Springer Verlag
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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Book Title
AI 2012: Advances in Artificial Intelligence: 25th Australasian Joint Conference, Sydney, Australia, December 4-7, 2012. Proceedings