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Minimax weight learning for absorbing MDPs

Author

Listed:
  • Fengying Li

    (East China Normal University)

  • Yuqiang Li

    (East China Normal University)

  • Xianyi Wu

    (East China Normal University)

Abstract

Reinforcement learning policy evaluation problems are often modeled as finite or discounted/averaged infinite-horizon Markov Decision Processes (MDPs). In this paper, we study undiscounted off-policy evaluation for absorbing MDPs. Given the dataset consisting of i.i.d episodes under a given truncation level, we propose an algorithm (referred to as MWLA in the text) to directly estimate the expected return via the importance ratio of the state-action occupancy measure. The Mean Square Error (MSE) bound of the MWLA method is provided and the dependence of statistical errors on the data size and the truncation level are analyzed. The performance of the algorithm is illustrated by means of computational experiments under an episodic taxi environment

Suggested Citation

  • Fengying Li & Yuqiang Li & Xianyi Wu, 2024. "Minimax weight learning for absorbing MDPs," Statistical Papers, Springer, vol. 65(6), pages 3545-3582, August.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:6:d:10.1007_s00362-023-01491-4
    DOI: 10.1007/s00362-023-01491-4
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    References listed on IDEAS

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    3. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
    4. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
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