Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
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ECML '02 Proceedings of the 13th European Conference on Machine Learning
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Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
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Artificial Intelligence - special issue on planning with uncertainty and incomplete information
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ICML '04 Proceedings of the twenty-first international conference on Machine learning
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ICML '05 Proceedings of the 22nd international conference on Machine learning
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Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
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Journal of Artificial Intelligence Research
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Applied Intelligence
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This paper employs state similarity to improve reinforcement learning performance. This is achieved by first identifying states with similar sub-policies. Then, a tree is constructed to be used for locating common action sequences of states as derived from possible optimal policies. Such sequences are utilized for defining a similarity function between states, which is essential for reflecting updates on the action-value function of a state onto all similar states. As a result, the experience acquired during learning can be applied to a broader context. Effectiveness of the method is demonstrated empirically.