PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Nearly optimal exploration-exploitation decision thresholds
Christos Dimitrakakis
In: ICANN 2006, 10-14 September 2006, Athens, Greece.


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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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2134
Deposited By:Christos Dimitrakakis
Deposited On:28 June 2006