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Temporal Difference-Based Policy Iteration for Optimal Control of Stochastic Systems

Author

Listed:
  • Kang Cheng

    (Southeast University)

  • Shumin Fei

    (Southeast University)

  • Kanjian Zhang

    (Southeast University)

  • Xiaomei Liu

    (Southeast University)

  • Haikun Wei

    (Southeast University)

Abstract

In this paper, a unified policy iteration approach is presented for the optimal control problem of stochastic system with discounted average cost and continuous state space. The approach consists of temporal difference learning-based potential function approximation algorithms and performance difference formula-based policy improvement. The approximation algorithms are derived by solving the Poisson equation-based fixed-point equation, which can be viewed as continuous versions of least squares policy evaluation algorithm and least squares temporal difference algorithm. The simulations are provided to illustrate the effectiveness of the approach.

Suggested Citation

  • Kang Cheng & Shumin Fei & Kanjian Zhang & Xiaomei Liu & Haikun Wei, 2014. "Temporal Difference-Based Policy Iteration for Optimal Control of Stochastic Systems," Journal of Optimization Theory and Applications, Springer, vol. 163(1), pages 165-180, October.
    • Handle: RePEc:spr:joptap:v:163:y:2014:i:1:d:10.1007_s10957-013-0418-1
    DOI: 10.1007/s10957-013-0418-1
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    References listed on IDEAS

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    1. X. R. Cao, 1999. "Single Sample Path-Based Optimization of Markov Chains," Journal of Optimization Theory and Applications, Springer, vol. 100(3), pages 527-548, March.
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    Cited by:

    1. Kang Cheng & Kanjian Zhang & Shumin Fei & Haikun Wei, 2016. "Potential-Based Least-Squares Policy Iteration for a Parameterized Feedback Control System," Journal of Optimization Theory and Applications, Springer, vol. 169(2), pages 692-704, May.

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    1. Kang Cheng & Kanjian Zhang & Shumin Fei & Haikun Wei, 2016. "Potential-Based Least-Squares Policy Iteration for a Parameterized Feedback Control System," Journal of Optimization Theory and Applications, Springer, vol. 169(2), pages 692-704, May.

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