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arXiv 2511.08078cs.LOcs.AI
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基于可满足性模概率模型检查的约束鲁棒策略合成

Constrained and Robust Policy Synthesis with Satisfiability-Modulo-Probabilistic-Model-Checking

Radboud University(拉德堡德大学);Brno University of Technology(布尔诺理工大学)

Linus Heck, Filip Macák, Milan Češka, Sebastian Junges

AI总结:

针对有限MDP上带结构约束的鲁棒奖励最优策略计算难题,本文提出结合可满足性求解器与概率模型检查的合成框架,实验证明其可行且性能媲美现有最优方法。

摘要:

针对给定已知有限马尔可夫决策过程(MDP)计算奖励最优策略的能力,是规划、控制器合成与验证等领域诸多应用的基础。但我们通常需要策略满足两点要求:一是具备鲁棒性,即在MDP受扰动时仍能表现良好;二是满足额外结构约束,例如与策略表示或实现成本相关的约束。计算这类带约束的鲁棒策略在计算层面确实更具挑战性。本文提出首个可高效计算满足任意结构约束的鲁棒策略的灵活高效框架。我们通过允许在MDP集合上的一阶理论中表述约束来实现灵活性,而效率的根源在于将处理问题组合特性的可满足性求解器与处理MDP分析的概率模型检查算法紧密结合。在数百个基准上的实验证明了该约束鲁棒策略合成方法的可行性,且在该问题的多个子场景下性能与当前最优方法相当。

英文摘要:

The ability to compute reward-optimal policies for given and known finite Markov decision processes (MDPs) underpins a variety of applications across planning, controller synthesis, and verification. However, we often want policies (1) to be robust, i.e., they perform well on perturbations of the MDP and (2) to satisfy additional structural constraints regarding, e.g., their representation or implementation cost. Computing such robust and constrained policies is indeed computationally more challenging. This paper contributes the first approach to effectively compute robust policies subject to arbitrary structural constraints using a flexible and efficient framework. We achieve flexibility by allowing to express our constraints in a first-order theory over a set of MDPs, while the root for our efficiency lies in the tight integration of satisfiability solvers to handle the combinatorial nature of the problem and probabilistic model checking algorithms to handle the analysis of MDPs. Experiments on a few hundred benchmarks demonstrate the feasibility for constrained and robust policy synthesis and the competitiveness with state-of-the-art methods for various fragments of the problem.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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