Reinforcement Learning with Guarantees

Mario Zanon () and Sébastien Gros ()
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Mario Zanon: IMT School for Advanced Studies Lucca
Sébastien Gros: Norwegian University of Science and Technology (NTNU)

Chapter Chapter 8 in Model Predictive Control, 2025, pp 191-224 from Springer

Abstract: 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.

Keywords: Reinforcement learning; Model predictive control; Markov decision process; Optimality (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:dymchp:978-3-031-85256-5_8

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DOI: 10.1007/978-3-031-85256-5_8

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