Bridging Impulse Control of Piecewise Deterministic Markov Processes and Markov Decision Processes: Frameworks, Extensions, and Open Challenges
Alice Cleynen, Beno\^ite de Saporta, Orlane Rossini, R\'egis Sabbadin,, Am\'elie Vernay

TL;DR
This paper explores the connection between PDMPs and MDPs, aiming to unify their frameworks for better analysis and solution of complex stochastic control problems, especially involving impulse controls.
Contribution
It provides a comprehensive review and formalization of the relationship between PDMPs and MDPs, highlighting new integration methods and open challenges.
Findings
Embedding impulse control problems of PDMPs into MDP framework enables new analysis tools.
Transforming continuous-time problems into MDPs broadens solution applicability.
The medical example illustrates the practical relevance of the integrated frameworks.
Abstract
Control theory plays a pivotal role in understanding and optimizing the behavior of complex dynamical systems across various scientific and engineering disciplines. Two key frameworks that have emerged for modeling and solving control problems in stochastic systems are piecewise deterministic Markov processes (PDMPs) and Markov decision processes (MDPs). Each framework has its unique strengths, and their intersection offers promising opportunities for tackling a broad class of problems, particularly in the context of impulse controls and decision-making in complex systems. The relationship between PDMPs and MDPs is a natural subject of exploration, as embedding impulse control problems for PDMPs into the MDP framework could open new avenues for their analysis and resolution. Specifically, this integration would allow leveraging the computational and theoretical tools developed for…
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Taxonomy
TopicsReinforcement Learning in Robotics
