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20212024
most citedConstructing MDP Abstractions Using Data with Formal Guarantees

1 citations · 2 across the 10 of their papers we have counts for

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10 papers

eess.SY20241 cited

From a Single Trajectory to Safety Controller Synthesis of Discrete-Time Nonlinear Polynomial Systems

Behrad Samari, Omid Akbarzadeh, Mahdieh Zaker +1

This work is concerned with developing a data-driven approach for learning control barrier certificates (CBCs) and associated safety controllers for discrete-time nonlinear polynom…

eess.SY2024

IMPaCT: Interval MDP Parallel Construction for Controller Synthesis of Large-Scale Stochastic Systems

Ben Wooding, Abolfazl Lavaei

This paper is concerned with developing a software tool, called IMPaCT, for the parallelized verification and controller synthesis of large-scale stochastic systems using interval…

eess.SY2023

MDP Abstractions from Data: Large-Scale Stochastic Networks

Abolfazl Lavaei

This work proposes a compositional data-driven technique for the construction of finite Markov decision processes (MDPs) for large-scale stochastic networks with unknown mathematic…

eess.SY2023

Symbolic Abstractions with Guarantees: A Data-Driven Divide-and-Conquer Strategy

Abolfazl Lavaei

This article is concerned with a data-driven divide-and-conquer strategy to construct symbolic abstractions for interconnected control networks with unknown mathematical models. We…

eess.SY2023

Safety Barrier Certificates for Stochastic Control Systems with Wireless Communication Networks

Omid Akbarzadeh, Sadegh Soudjani, Abolfazl Lavaei

This work is concerned with a formal approach for safety controller synthesis of stochastic control systems with both process and measurement noises while considering wireless comm…

eess.SY2022

Compositional Reinforcement Learning for Discrete-Time Stochastic Control Systems

Abolfazl Lavaei, Mateo Perez, Milad Kazemi +4

We propose a compositional approach to synthesize policies for networks of continuous-space stochastic control systems with unknown dynamics using model-free reinforcement learning…