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A novel Q-learning algorithm with function approximation for constrained Markov decision processes

Lakshmanan, K and Bhatnagar, Shalabh (2012) A novel Q-learning algorithm with function approximation for constrained Markov decision processes. In: 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 1-5 Oct. 2012 , Monticello, IL, USA.

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Official URL: http://dx.doi.org/10.1109/Allerton.2012.6483246

Abstract

We present a novel multi-timescale Q-learning algorithm for average cost control in a Markov decision process subject to multiple inequality constraints. We formulate a relaxed version of this problem through the Lagrange multiplier method. Our algorithm is different from Q-learning in that it updates two parameters - a Q-value parameter and a policy parameter. The Q-value parameter is updated on a slower time scale as compared to the policy parameter. Whereas Q-learning with function approximation can diverge in some cases, our algorithm is seen to be convergent as a result of the aforementioned timescale separation. We show the results of experiments on a problem of constrained routing in a multistage queueing network. Our algorithm is seen to exhibit good performance and the various inequality constraints are seen to be satisfied upon convergence of the algorithm.

Item Type: Conference Paper
Additional Information: Copyright of this article belongs to IEEE.
Keywords: Q-Learning with Linear Function Approximation; Constrained MDP; Lagrange Multiplier Method; Reinforcement Learning; Multi-Stage Stochastic Shortest Path Problem
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Depositing User: Id for Latest eprints
Date Deposited: 02 Jul 2013 08:37
Last Modified: 02 Jul 2013 08:37
URI: http://eprints.iisc.ac.in/id/eprint/46678

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