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Reinforcement Learning with Guarantees

Author

Listed:
  • Mario Zanon

    (IMT School for Advanced Studies Lucca)

  • Sébastien Gros

    (Norwegian University of Science and Technology (NTNU))

Abstract

Markov Decision Processes formalize many problems of interest and have been tackled using a variety of techniques, including Reinforcement Learning (RL) and Model Predictive Control (MPC). While each approach has both advantages and disadvantages, RL and MPC have been very successful in the respective domains. RL makes it possible to obtain optimality for the real system, without the need for a model. MPC requires a model, but makes it possible to provide strict stability and safety guarantees, as well as to promote explainability. In this regard, the two techniques are complementary, and this chapter focuses on how they can be combined in order to leverage the advantages of both.

Suggested Citation

  • Mario Zanon & Sébastien Gros, 2025. "Reinforcement Learning with Guarantees," Dynamic Modeling and Econometrics in Economics and Finance,, Springer.
    • Handle: RePEc:spr:dymchp:978-3-031-85256-5_8
    DOI: 10.1007/978-3-031-85256-5_8
    as

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