3 papers
eess.SY2024
Reinforcement Learning-based Control of Nonlinear Systems using Carleman Approximation: Structured and Unstructured Designs
Jishnudeep Kar, He Bai, Aranya Chakrabortty
We develop data-driven reinforcement learning (RL) control designs for input-affine nonlinear systems. We use Carleman linearization to express the state-space representation of th…
eess.SY2024
Asynchronous Distributed Reinforcement Learning for LQR Control via Zeroth-Order Block Coordinate Descent
Gangshan Jing, He Bai, Jemin George +2
Recently introduced distributed zeroth-order optimization (ZOO) algorithms have shown their utility in distributed reinforcement learning (RL). Unfortunately, in the gradient estim…
cs.LG2024
Distributed Multi-Agent Reinforcement Learning Based on Graph-Induced Local Value Functions
Gangshan Jing, He Bai, Jemin George +2
Achieving distributed reinforcement learning (RL) for large-scale cooperative multi-agent systems (MASs) is challenging because: (i) each agent has access to only limited informati…