The Stackelberg Equilibrium for One-sided Zero-sum Partially Observable Stochastic Games
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Abstract (arXiv)
Formulating cyber-security problems with attackers and defenders as a partially observable stochastic game has become a trend recently. Among them, the one-sided two-player zero-sum partially observable stochastic game (OTZ-POSG) has emerged as a popular model because it allows players to compete for multiple stages based on partial knowledge of the system. All existing work on OTZ-POSG has focused on the simultaneous move scenario and assumed that one player's actions are private in the execution process. However, this assumption may become questionable since one player's action may be detected by the opponent through deploying action detection strategies. Hence, in this paper, we propose a turn-based OTZ-POSG with the assumption of public actions and investigate the existence and properties of a Stackelberg equilibrium for this game. We first prove the existence of the Stackelberg equilibrium for the one-stage case and show that the one-stage game can be converted into a linear-fractional programming problem and therefore solved by linear programming. For multiple stages, the main challenge is the information leakage issue as the public run-time action reveals certain private information to the opponent and allows the opponent to achieve more rewards in the future. To deal with this issue, we adopt the concept of ε-Stackelberg equilibrium and prove that this equilibrium can be achieved for finite-horizon OTZ-POSGs. We propose a space partition approach to solve the game iteratively and show that the value function of the leader is piece-wise linear and the value function of the follower is piece-wise constant for multiple stages. Finally, examples are given to illustrate the space partition approach and show that value functions are piece-wise linear and piece-wise constant.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Sep 2021.
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Citing papers per year: 2023: 2; 2024: 3; 2025: 0; 2026: 1.
6 papers.
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- 2023Partially Observable Stochastic Games with Neural Perception Mechanismscited by 2
- 2026Finite-State Controllers for (Hidden-Model) POMDPs using Deep Reinforcement Learningcited by 1
- 2024HSVI-based Online Minimax Strategies for Partially Observable Stochastic Games with Neural Perception Mechanismscited by 0
- 2026Finite-State Controllers for (Hidden-Model) POMDPs using Deep Reinforcement Learning9 Feb 2026
- 2024Games for AI Control: Models of Safety Evaluations of AI Deployment Protocols12 Sep 2024
- 2024Pessimistic Iterative Planning with RNNs for Robust POMDPs16 Aug 2024
- 2024HSVI-based Online Minimax Strategies for Partially Observable Stochastic Games with Neural Perception Mechanisms16 Apr 2024
- 2023Optimal Attack and Defense for Reinforcement Learning30 Nov 2023
- 2023Partially Observable Stochastic Games with Neural Perception Mechanisms17 Oct 2023