A sampling-based method constructs interval Markov decision process abstractions for nonlinear stochastic systems, enabling synthesis of control policies with PAC reach-avoid guarantees.
Data-driven abstractions via adaptive refinements and a Kantorovich metric [extended version]
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abstract
We introduce an adaptive refinement procedure for smart, and scalable abstraction of dynamical systems. Our technique relies on partitioning the state space depending on the observation of future outputs. However, this knowledge is dynamically constructed in an adaptive, asymmetric way. In order to learn the optimal structure, we define a Kantorovich-inspired metric between Markov chains, and we use it as a loss function. Our technique is prone to data-driven frameworks, but not restricted to. We also study properties of the above mentioned metric between Markov chains, which we believe could be of application for wider purpose. We propose an algorithm to approximate it, and we show that our method yields a much better computational complexity than using classical linear programming techniques.
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Data-Driven Yet Formal Policy Synthesis for Stochastic Nonlinear Dynamical Systems
A sampling-based method constructs interval Markov decision process abstractions for nonlinear stochastic systems, enabling synthesis of control policies with PAC reach-avoid guarantees.