21 citations · 65 across the 31 of their papers we have counts for
6 papers · 1 filter
Policy Optimization for Markovian Jump Linear Quadratic Control: Gradient-Based Methods and Global Convergence
Joao Paulo Jansch-Porto, Bin Hu, Geir Dullerud
Recently, policy optimization for control purposes has received renewed attention due to the increasing interest in reinforcement learning. In this paper, we investigate the global…
Verifying Stochastic Hybrid Systems with Temporal Logic Specifications via Model Reduction
Yu Wang, Nima Roohi, Matthew West +2
We present a scalable methodology to verify stochastic hybrid systems. Using the Mori-Zwanzig reduction method, we construct a finite state Markov chain reduction of a given stocha…
Policy Learning of MDPs with Mixed Continuous/Discrete Variables: A Case Study on Model-Free Control of Markovian Jump Systems
Joao Paulo Jansch-Porto, Bin Hu, Geir Dullerud
Markovian jump linear systems (MJLS) are an important class of dynamical systems that arise in many control applications. In this paper, we introduce the problem of controlling unk…
Statistically Model Checking PCTL Specifications on Markov Decision Processes via Reinforcement Learning
Yu Wang, Nima Roohi, Matthew West +2
Probabilistic Computation Tree Logic (PCTL) is frequently used to formally specify control objectives such as probabilistic reachability and safety. In this work, we focus on model…
Differentially Private Algorithms for Statistical Verification of Cyber-Physical Systems
Yu Wang, Hussein Sibai, Mark Yen +2
Statistical model checking is a class of sequential algorithms that can verify specifications of interest on an ensemble of cyber-physical systems (e.g., whether 99% of cars from a…
Convergence Guarantees of Policy Optimization Methods for Markovian Jump Linear Systems
Joao Paulo Jansch-Porto, Bin Hu, Geir Dullerud
Recently, policy optimization for control purposes has received renewed attention due to the increasing interest in reinforcement learning. In this paper, we investigate the conver…