57 citations · 147 across the 21 of their papers we have counts for
21 papers
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…
Asynchronous Bayesian Learning over a Network
Kinjal Bhar, He Bai, Jemin George +1
We present a practical asynchronous data fusion model for networked agents to perform distributed Bayesian learning without sharing raw data. Our algorithm uses a gossip-based appr…
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…
Distributed Cooperative Multi-Agent Reinforcement Learning with Directed Coordination Graph
Gangshan Jing, He Bai, Jemin George +2
Existing distributed cooperative multi-agent reinforcement learning (MARL) frameworks usually assume undirected coordination graphs and communication graphs while estimating a glob…
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…
Variance Reduction of Quadcopter Trajectory Tracking in Turbulent Wind
Asma Tabassum, Rohit K. S. S. Vuppala, He Bai +1
We consider a quadcopter operating in a turbulent windy environment. The turbulent environment may be imposed on a quadcopter by structures, landscapes, terrains and most important…