Potential-Based Least-Squares Policy Iteration for a Parameterized Feedback Control System

Kang Cheng (), Kanjian Zhang (), Shumin Fei () and Haikun Wei ()
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Kang Cheng: Southeast University
Kanjian Zhang: Southeast University
Shumin Fei: Southeast University
Haikun Wei: Southeast University

Journal of Optimization Theory and Applications, 2016, vol. 169, issue 2, No 17, 692-704

Abstract: Abstract In the paper, a potential-based policy iteration method is proposed for optimal control of a stochastic dynamic system with an average cost criterion and a parameterized control law. In this method, the potential function and the optimal control parameters are obtained via a least-squares-based approach. The potential estimation algorithm is derived from a temporal difference learning method, which can be viewed as a continuous version of the least-squares policy evaluation algorithm. The policy iteration algorithm is validated by solving a linear quadratic gaussian problem in the simulation.

Keywords: Stochastic system; Markov decision processes; Performance potential; Least-squares policy evaluation; Policy iteration; 49K45; 93E20; 93C55 (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10957-015-0809-6

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