1 citations · 1 across the 4 of their papers we have counts for
4 papers
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…
Constructing MDP Abstractions Using Data with Formal Guarantees
Abolfazl Lavaei, Sadegh Soudjani, Emilio Frazzoli +1
This paper is concerned with a data-driven technique for constructing finite Markov decision processes (MDPs) as finite abstractions of discrete-time stochastic control systems wit…
Data-driven Safety Verification of Stochastic Systems via Barrier Certificates
Ali Salamati, Abolfazl Lavaei, Sadegh Soudjani +1
In this paper, we propose a data-driven approach to formally verify the safety of (potentially) unknown discrete-time continuous-space stochastic systems. The proposed framework is…
A small-gain theory for infinite networks via infinite-dimensional gain operators
Christoph Kawan, Majid Zamani
In this paper, we develop a new approach to study gain operators built from the interconnection gains of infinite networks of dynamical systems. Our focus is on the construction of…