Reinforcement learning: Theory and practice

Reinforcement learning: Theory and practice. Szepesvári, C. In Proceedings of the 2nd Slovak Conference on Artificial Neural Networks (SCANN'98), pages 29–39, 1998.

Paper abstract bibtex

We consider reinforcement learning methods for the solution of complex sequential optimization problems. In particular, the soundness of two methods proposed for the solution of partially observable problems will be shown. The first method suggests a state-estimation scheme and requires mild \em a priori knowledge, while the second method assumes that a significant amount of abstract knowledge is available about the decision problem and uses this knowledge to setup a macro-hierarchy to turn the partially observable problem into another one which can already be handled using methods worked out for observable problems. This second method is also illustrated with some experiments on a real-robot.

@inproceedings{szepesvari1998c,
	abstract = {We consider reinforcement learning methods for the solution of complex sequential optimization problems. In particular, the soundness of two methods proposed for the solution of partially observable problems will be shown. The first method suggests a state-estimation scheme and requires mild {\em a priori} knowledge, while the second method assumes that a significant amount of abstract knowledge is available about the decision problem and uses this knowledge to setup a macro-hierarchy to turn the partially observable problem into another one which can already be handled using methods worked out for observable problems. This second method is also illustrated with some experiments on a real-robot.},
	author = {Szepesv{\'a}ri, Cs.},
	booktitle = {Proceedings of the 2nd Slovak Conference on Artificial Neural Networks (SCANN'98)},
	date-added = {2010-08-28 17:38:14 -0600},
	date-modified = {2013-10-20 21:25:10 +0300},
	editor = {Hrehus, M.},
	keywords = {reinforcement learning, partial information, theory, MDP transformations},
	pages = {29--39},
	title = {Reinforcement learning: Theory and practice},
	url_paper = {scann98.ps.pdf},
	year = {1998}}

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