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Strategy Synthesis in Markov Decision Processes Under Limited Sampling Access

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NASA Formal Methods (NFM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13903))

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Abstract

A central task in control theory, artificial intelligence, and formal methods is to synthesize reward-maximizing strategies for agents that operate in partially unknown environments. In environments modeled by gray-box Markov decision processes (MDPs), the impact of the agents’ actions are known in terms of successor states but not the stochastics involved. In this paper, we devise a strategy synthesis algorithm for gray-box MDPs via reinforcement learning that utilizes interval MDPs as internal model. To compete with limited sampling access in reinforcement learning, we incorporate two novel concepts into our algorithm, focusing on rapid and successful learning rather than on stochastic guarantees and optimality: lower confidence bound exploration reinforces variants of already learned practical strategies and action scoping reduces the learning action space to promising actions. We illustrate benefits of our algorithms by means of a prototypical implementation applied on examples from the AI and formal methods communities.

The authors are supported by the DFG through the Cluster of Excellence EXC 2050/1 (CeTI, project ID 390696704, as part of Germany’s Excellence Strategy) and the TRR 248 (see https://perspicuous-computing.science, project ID 389792660).

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Author information

Authors and Affiliations

  1. Centre for Tactile Internet with Human-in-the-Loop (CeTI), Dresden, Germany

    Christel Baier, Clemens Dubslaff, Patrick Wienhöft & Stefan J. Kiebel

  2. Department of Computer Science, Technische Universität Dresden, Dresden, Germany

    Christel Baier & Patrick Wienhöft

  3. Department of Psychology, Technische Universität Dresden, Dresden, Germany

    Stefan J. Kiebel

  4. Eindhoven University of Technology, Eindhoven, The Netherlands

    Clemens Dubslaff

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  1. Christel Baier
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  2. Clemens Dubslaff
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  3. Patrick Wienhöft
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  4. Stefan J. Kiebel
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Correspondence to Patrick Wienhöft .

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Editors and Affiliations

  1. Iowa State University, Ames, IA, USA

    Kristin Yvonne Rozier

  2. University of Texas at Austin, Austin, TX, USA

    Swarat Chaudhuri

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Baier, C., Dubslaff, C., Wienhöft, P., Kiebel, S.J. (2023). Strategy Synthesis in Markov Decision Processes Under Limited Sampling Access. In: Rozier, K.Y., Chaudhuri, S. (eds) NASA Formal Methods. NFM 2023. Lecture Notes in Computer Science, vol 13903. Springer, Cham. https://doi.org/10.1007/978-3-031-33170-1_6

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  • DOI: https://doi.org/10.1007/978-3-031-33170-1_6

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