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Found 13 papers, 3 papers with code
Data-driven Interval MDP for Robust Control Synthesis
The abstraction of dynamical systems is a powerful tool that enables the design of feedback controllers using a correct-by-design framework.
A Stability-Based Abstraction Framework for Reach-Avoid Control of Stochastic Dynamical Systems with Unknown Noise Distributions
To address this issue, we propose a novel abstraction scheme for stochastic linear systems that exploits the system's stability to obtain significantly smaller abstract models.
Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of Multipliers
Many machine learning applications require operating on a spatially distributed dataset.
Correct-by-Construction Control for Stochastic and Uncertain Dynamical Models via Formal Abstractions
Such autonomous systems are naturally modeled as stochastic dynamical models.
Abstracting Linear Stochastic Systems via Knowledge Filtering
In this paper, we propose a new model reduction technique for linear stochastic systems that builds upon knowledge filtering and utilizes optimal Kalman filtering techniques.
Inner approximations of stochastic programs for data-driven stochastic barrier function design
In particular, we show that the stochastic program to synthesize a SBF can be relaxed into a chance-constrained optimisation problem on which scenario approach theory applies.
Distributionally Robust Optimal and Safe Control of Stochastic Systems via Kernel Conditional Mean Embedding
We present a novel distributionally robust framework for dynamic programming that uses kernel methods to design feedback control policies.
Data-driven abstractions via adaptive refinements and a Kantorovich metric [extended version]
In order to learn the optimal structure, we define a Kantorovich-inspired metric between Markov chains, and we use it as a loss function.
Robust Control for Dynamical Systems With Non-Gaussian Noise via Formal Abstractions
This iMDP is, with a user-specified confidence probability, robust against uncertainty in the transition probabilities, and the tightness of the probability intervals can be controlled through the number of samples.
Data-driven memory-dependent abstractions of dynamical systems
We propose a sample-based, sequential method to abstract a (potentially black-box) dynamical system with a sequence of memory-dependent Markov chains of increasing size.
Formal Controller Synthesis for Markov Jump Linear Systems with Uncertain Dynamics
We consider the cases where the transition probabilities of this MDP are either known up to an interval or completely unknown.
Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty
Stochastic noise causes aleatoric uncertainty, whereas imprecise knowledge of model parameters leads to epistemic uncertainty.
Bounded Robustness in Reinforcement Learning via Lexicographic Objectives
Policy robustness in Reinforcement Learning may not be desirable at any cost: the alterations caused by robustness requirements from otherwise optimal policies should be explainable, quantifiable and formally verifiable.
