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A LEARNING ALGORITHM FOR DISCRETE-TIME STOCHASTIC CONTROL

Published online by Cambridge University Press:  01 April 2000

V. S. Borkar
V. S. Borkar
Affiliation:
School of Technology and Computer Science, Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai 400005, India, E-mail: [email protected]
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Abstract

A simulation-based algorithm for learning good policies for a discrete-time stochastic control process with unknown transition law is analyzed when the state and action spaces are compact subsets of Euclidean spaces. This extends the Q-learning scheme of discrete state/action problems along the lines of Baker [4]. Almost sure convergence is proved under suitable conditions.

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 14 , Issue 2 , April 2000 , pp. 243 - 258
Copyright
© 2000 Cambridge University Press

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