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Teaching Stratego to Play Ball: Optimal Synthesis for Continuous Space MDPs

  • Manfred Jaeger
  • Peter Gjøl JensenEmail author
  • Kim Guldstrand Larsen
  • Axel Legay
  • Sean Sedwards
  • Jakob Haahr Taankvist
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11781)

Abstract

Formal models of cyber-physical systems, such as priced timed Markov decision processes, require a state space with continuous and discrete components. The problem of controller synthesis for such systems then can be cast as finding optimal strategies for Markov decision processes over a Euclidean state space. We develop two different reinforcement learning strategies that tackle the problem of continuous state spaces via online partition refinement techniques. We provide theoretical insights into the convergence of partition refinement schemes. Our techniques are implemented in Open image in new window. Experimental results show the advantages of our new techniques over previous optimization algorithms of Open image in new window.

Notes

Acknowledgements

This work is partly supported by the Innovation Fund Denmark center DiCyPS, the ERC Advanced Grant LASSO, and the JST ERATO project: HASUO Metamathematics for Systems Design (JPMJER1603).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Manfred Jaeger
    • 1
  • Peter Gjøl Jensen
    • 1
    Email author
  • Kim Guldstrand Larsen
    • 1
  • Axel Legay
    • 1
    • 2
  • Sean Sedwards
    • 3
  • Jakob Haahr Taankvist
    • 1
  1. 1.Department of Computer ScienceAalborg UniversityAalborgDenmark
  2. 2.Université catholique de LouvainOttignies-Louvain-la-NeuveBelgium
  3. 3.University of WaterlooWaterlooCanada

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