Adaptive ε-Greedy Exploration in Reinforcement Learning Based on Value Differences
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.
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- 1.Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)Google Scholar
- 2.Watkins, C.: Learning from Delayed Rewards. PhD thesis, University of Cambridge, Cambridge, England (1989)Google Scholar
- 3.Thrun, S.B.: Efficient exploration in reinforcement learning. Technical Report CMU-CS-92-102, Carnegie Mellon University, Pittsburgh, PA, USA (1992)Google Scholar
- 6.Heidrich-Meisner, V.: Interview with Richard S. Sutton. Künstliche Intelligenz 3, 41–43 (2009)Google Scholar
- 9.Rummery, G.A., Niranjan, M.: On-line Q-learning using connectionist systems. Technical Report CUED/F-INFENG/TR 166, Cambridge University (1994)Google Scholar
- 13.Awerbuch, B., Kleinberg, R.D.: Adaptive routing with end-to-end feedback: Distributed learning and geometric approaches. In: Proceedings of the 36th Annual ACM Symposium on Theory of Computing, Chicago, IL, USA, pp. 45–53. ACM, New York (2004)Google Scholar