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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Adaptive ε-Greedy Exploration in Reinforcement Learning Based on Value Differences
verfasst von : Michel Tokic
Erschienen in: KI 2010: Advances in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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This paper presents “Value-Difference Based Exploration” (VDBE), a method for balancing the exploration/exploitation dilemma inherent to reinforcement learning. The proposed method adapts the exploration parameter of
ε
-greedy in dependence of the
temporal-difference error
observed from value-function backups, which is considered as a measure of the agent’s uncertainty about the environment. VDBE is evaluated on a multi-armed bandit task, which allows for insight into the behavior of the method. Preliminary results indicate that VDBE seems to be more parameter robust than commonly used ad hoc approaches such as
ε
-greedy or softmax.