8 citations · 17 across the 13 of their papers we have counts for
3 papers · 1 filter
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…
Data-Driven Abstraction-Based Control Synthesis
Milad Kazemi, Rupak Majumdar, Mahmoud Salamati +2
This paper studies formal synthesis of controllers for continuous-space systems with unknown dynamics to satisfy requirements expressed as linear temporal logic formulas. Formal ab…
Formal Policy Synthesis for Continuous-Space Systems via Reinforcement Learning
Milad Kazemi, Sadegh Soudjani
This paper studies satisfaction of temporal properties on unknown stochastic processes that have continuous state spaces. We show how reinforcement learning (RL) can be applied for…