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Nearly Optimal Exploration-Exploitation Decision Thresholds

Christos Dimitrakakis (Institutionen för data- och informationsteknik, Datavetenskap, Algoritmer (Chalmers))
Artificial Neural Networks – ICANN 2006 (2006)
[Konferensbidrag, refereegranskat]

While in general trading off exploration and exploitation in reinforcement learning is hard, under some formulations relatively simple solutions exist. Optimal decision thresholds for the multi-armed bandit problem, one for the infinite horizon discounted reward case and one for the finite horizon undiscounted reward case are derived, which make the link between the reward horizon, uncertainty and the need for exploration explicit. From this result follow two practical approximate algorithms, which are illustrated experimentally.



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Denna post skapades 2013-12-18. Senast ändrad 2015-01-08.
CPL Pubid: 189778