Adaptive ε-Greedy Exploration in Reinforcement Learning Based on Value Differences

  • Michel Tokic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6359)


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.


Exploration Rate Bandit Problem Large State Space Cumulative Reward Reward Distribution 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Michel Tokic
    • 1
    • 2
  1. 1.Institute of Applied ResearchUniversity of Applied Sciences Ravensburg-WeingartenWeingartenGermany
  2. 2.Institute of Neural Information ProcessingUniversity of UlmUlmGermany

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