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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Towards Finite-Sample Convergence of Direct Reinforcement Learning
verfasst von : Shiau Hong Lim, Gerald DeJong
Erschienen in: Machine Learning: ECML 2005
Verlag: Springer Berlin Heidelberg
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While direct, model-free reinforcement learning often performs better than model-based approaches in practice, only the latter have yet supported theoretical guarantees for finite-sample convergence. A major difficulty in analyzing the direct approach in an online setting is the absence of a definitive exploration strategy. We extend the notion of admissibility to direct reinforcement learning and show that standard Q-learning with optimistic initial values and constant learning rate is admissible. The notion justifies the use of a greedy strategy that we believe performs very well in practice and holds theoretical significance in deriving finite-sample convergence for direct reinforcement learning. We present empirical evidence that supports our idea.