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Abstract: MDP for a four-room example. Grey circles mark peripheral states of the original MDP, i.e. states of the abstract MDP. (Update)
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BibTeX entry: (Update)
Hauskrecht, M., Meuleau, N., Boutilier, C., Kaelbling, L. P. & Dean, T. (1998). Hierarchical solution of markov decision processes using macro-actions. In Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence. http://citeseer.ist.psu.edu/hauskrecht98hierarchical.html More
@inproceedings{ hauskrechthierarchical,
author = "Milos Hauskrecht and Nicolas Meuleau and Leslie Pack Kaelbling and Thomas Dean and Craig Boutilier",
title = "Hierarchical Solution of {Markov} Decision Processes using Macro-actions",
pages = "220--229",
url = "citeseer.ist.psu.edu/hauskrecht98hierarchical.html" }
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87 Reinforcement learning with hierarchies of machines - Parr, Russell - 1998
71 Macro-operators: A weak method for learning (context) - Korf - 1985
70 Abstraction and approximate decision theoretic planning - Dearden, Boutilier - 1997
63 Decomposition techniques for planning in stochastic domains - Dean, Lin - 1995
51 Model minimization in Markov decision processes - Dean, Givan - 1997
51 Finding structure in reinforcement learning - Thrun, Schwartz - 1995
40 Selectively generalizing plans for problem solving (context) - Minton - 1985
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25 TD models: Modeling the world at a mixture of time scales - Sutton - 1995
22 Flexible Decomposition Algorithms for Weakly Coupled Markov .. - Parr - 1998
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3 Hierarchical reinforcement learning: Preliminary results (context) - Kaelbling - 1993
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