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An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes

Bhatnagar, Shalabh (2010) An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes. In: Systems & Control Letters, 59 (12). pp. 760-766.

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Official URL: http://dx.doi.org/10.1016/j.sysconle.2010.08.013

Abstract

We develop in this article the first actor-critic reinforcement learning algorithm with function approximation for a problem of control under multiple inequality constraints. We consider the infinite horizon discounted cost framework in which both the objective and the constraint functions are suitable expected policy-dependent discounted sums of certain sample path functions. We apply the Lagrange multiplier method to handle the inequality constraints. Our algorithm makes use of multi-timescale stochastic approximation and incorporates a temporal difference (TD) critic and an actor that makes a gradient search in the space of policy parameters using efficient simultaneous perturbation stochastic approximation (SPSA) gradient estimates. We prove the asymptotic almost sure convergence of our algorithm to a locally optimal policy. (C) 2010 Elsevier B.V. All rights reserved.

Item Type: Journal Article
Additional Information: Copyright of this article belongs to Elsevier Science B.V.
Keywords: Constrained Markov decision processes; Infinite horizon discounted cost criterion; Function approximation; Actor-critic algorithm; Simultaneous perturbation stochastic approximation
Department/Centre: Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)
Date Deposited: 30 Mar 2011 07:33
Last Modified: 30 Mar 2011 07:33
URI: http://eprints.iisc.ernet.in/id/eprint/36331

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