Reinforcement Learning Methods for Continuous-Time Markov Decision Problems (1994)
| Venue: | Advances in Neural Information Processing Systems |
| Citations: | 99 - 0 self |
BibTeX
@INPROCEEDINGS{Bradtke94reinforcementlearning,
author = {Steven J. Bradtke and Michael O. Duff},
title = {Reinforcement Learning Methods for Continuous-Time Markov Decision Problems},
booktitle = {Advances in Neural Information Processing Systems},
year = {1994},
pages = {393--400},
publisher = {MIT Press}
}
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Abstract
Semi-Markov Decision Problems are continuous time generalizations of discrete time Markov Decision Problems. A number of reinforcement learning algorithms have been developed recently for the solution of Markov Decision Problems, based on the ideas of asynchronous dynamic programming and stochastic approximation. Among these are TD(), Q-learning, and Real-time Dynamic Programming. After reviewing semi-Markov Decision Problems and Bellman's optimality equation in that context, we propose algorithms similar to those named above, adapted to the solution of semi-Markov Decision Problems. We demonstrate these algorithms by applying them to the problem of determining the optimal control for a simple queueing system. We conclude with a discussion of circumstances under which these algorithms may be usefully applied. 1 Introduction A number of reinforcement learning algorithms based on the ideas of asynchronous dynamic programming and stochastic approximation have been developed recently for...
