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Offline minimax Q-function learning for undiscounted indefinite-horizon MDPs

Author

Listed:
  • Fengying Li

    (Ningxia Normal University)

  • Yuqiang Li

    (East China Normal University)

  • Xianyi Wu

    (East China Normal University)

  • Wei Bai

    (Ningxia Normal University)

Abstract

This work considers the offline evaluation problem for indefinite-horizon Markov Decision Processes. A minimax Q-function learning algorithm is proposed, which, instead of i.i.d. tuples $$(s,a,s',r)$$ ( s , a , s ′ , r ) , evaluates undiscounted expected return based by i.i.d. trajectories truncated at a given time step. The confidence error bounds are developed. Experiments using Open AI’s Cart Pole environment are employed to demonstrate the algorithm.

Suggested Citation

  • Fengying Li & Yuqiang Li & Xianyi Wu & Wei Bai, 2025. "Offline minimax Q-function learning for undiscounted indefinite-horizon MDPs," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 77(4), pages 535-562, August.
    • Handle: RePEc:spr:aistmt:v:77:y:2025:i:4:d:10.1007_s10463-025-00924-1
    DOI: 10.1007/s10463-025-00924-1
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