Two-person zero-sum discounted semi-Markov games with incomplete reward information on one side

Abstract We consider two-person zero-sum semi-Markov games with incomplete reward information on one side under the expected discount criterion. First, we prove that the value function exists and satisfies the Shapley equation. From the Shapley equation, we construct an optimal policy for the informed player. Second, to show the existence of an optimal policy for the uninformed player, we introduce an auxiliary dual game and establish the relationship between the primal game and the dual game. By this relationship, we also prove the existence of the value function of the dual game, and then construct an optimal policy for the uninformed player in the primal game. Finally, we develop two iterative algorithms to compute $\varepsilon$ -optimal policies for the informed player and the uninformed player, respectively.

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Publication Details

Journal
Journal of Applied Probability
Published
2026-01-26
DOI
https://doi.org/10.1017/jpr.2025.10052
Primary Topic
Reinforcement Learning in Robotics
Type
article
Field-Weighted Citation Impact
0.00
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article

Two-person zero-sum discounted semi-Markov games with incomplete reward information on one side

Zhong-Wei Liao, Xianping Guo, Fang Chen
Journal of Applied Probability
Reinforcement Learning in Robotics
article

Two-person zero-sum discounted semi-Markov games with incomplete reward information on one side

Zhong-Wei Liao, Xianping Guo, Fang Chen
article en

Abstract

Abstract We consider two-person zero-sum semi-Markov games with incomplete reward information on one side under the expected discount criterion. First, we prove that the value function exists and satisfies the Shapley equation. From the Shapley equation, we construct an optimal policy for the informed player. Second, to show the existence of an optimal policy for the uninformed player, we introduce an auxiliary dual game and establish the relationship between the primal game and the dual game. By this relationship, we also prove the existence of the value function of the dual game, and then construct an optimal policy for the uninformed player in the primal game. Finally, we develop two iterative algorithms to compute $\varepsilon$ -optimal policies for the informed player and the uninformed player, respectively.

Journal of Applied Probability
Sun Yat-sen University (CN), Peking University (CN), Beijing Normal University (CN)
Openalex Percentile: Top 78%
Reinforcement Learning in Robotics
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Two-person zero-sum discounted semi-Markov games with incomplete reward information on one side — Zhong-Wei Liao, Xianping Guo, et al. · Journal of Applied Probability (2026) | TGRS Research Map | TGRS