4 citations · 4 across the 5 of their papers we have counts for
5 papers · 1 filter
Learning Distributed Stabilizing Controllers for Multi-Agent Systems
Gangshan Jing, He Bai, Jemin George +2
We address the problem of model-free distributed stabilization of heterogeneous multi-agent systems using reinforcement learning (RL). Two algorithms are developed. The first algor…
Decomposability and Parallel Computation of Multi-Agent LQR
Gangshan Jing, He Bai, Jemin George +1
Individual agents in a multi-agent system (MAS) may have decoupled open-loop dynamics, but a cooperative control objective usually results in coupled closed-loop dynamics thereby m…
Model-Free Optimal Control of Linear Multi-Agent Systems via Decomposition and Hierarchical Approximation
Gangshan Jing, He Bai, Jemin George +1
Designing the optimal linear quadratic regulator (LQR) for a large-scale multi-agent system (MAS) is time-consuming since it involves solving a large-size matrix Riccati equation.…
Hierarchical Control of Multi-Agent Systems using Online Reinforcement Learning
He Bai, Jemin George, Aranya Chakrabortty
We propose a new reinforcement learning based approach to designing hierarchical linear quadratic regulator (LQR) controllers for heterogeneous linear multi-agent systems with unkn…
Reduced-Dimensional Reinforcement Learning Control using Singular Perturbation Approximations
Sayak Mukherjee, He Bai, Aranya Chakrabortty
We present a set of model-free, reduced-dimensional reinforcement learning (RL) based optimal control designs for linear time-invariant singularly perturbed (SP) systems. We first…