A policy improvement method for constrained average Markov decision processes

  • Authors:
  • Hyeong Soo Chang

  • Affiliations:
  • Department of Computer Science and Engineering, Sogang University, Seoul, Korea

  • Venue:
  • Operations Research Letters
  • Year:
  • 2007

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Abstract

This brief paper presents a policy improvement method for constrained Markov decision processes (MDPs) with average cost criterion under an ergodicity assumption, extending Howard's policy improvement for MDPs. The improvement method induces a policy iteration-type algorithm that converges to a local optimal policy.