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Abstract: We present a new approach to reinforcement learning in which the policies considered by the learning process are constrained by hierarchies of partially specified machines. This allows for the use of prior knowledge to reduce the search space and provides a framework in which knowledge can be transferred across problems and in which component solutions can be recombined to solve larger and more complicated problems. Our approach can be seen as providing a link between reinforcement learning and ... (Update)
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BibTeX entry: (Update)
Parr, R. & Russell, S. (1997). Reinforcement learning with hierarchies of machines. In Proceedings of Advances in Neural Information Processing Systems 10. MIT Press. http://citeseer.ist.psu.edu/parr97reinforcement.html More
@inproceedings{ parr97reinforcement,
author = "Ronald Parr and Stuart Russell",
title = "Reinforcement Learning with Hierarchies of Machines",
booktitle = "Advances in Neural Information Processing Systems",
volume = "10",
publisher = "The {MIT} Press",
editor = "Michael I. Jordan and Michael J. Kearns and Sara A. Solla",
year = "1997",
url = "citeseer.ist.psu.edu/parr97reinforcement.html" }
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