most citedLearning to Communicate: A Machine Learning Framework for Heterogeneous Multi-Agent Robotic Systems

1 citations · 2 across the 5 of their papers we have counts for

collaborators

8 papers

math.OC20211 cited

On the Semidefinite Duality of Finite-Horizon LQG Problem

Donghwan Lee

In this paper, our goal is to study fundamental foundations of linear quadratic Gaussian (LQG) control problems for stochastic linear time-invariant systems via Lagrangian duality…

math.OC2021

Convergence of Dynamic Programming on the Semidefinite Cone

Donghwan Lee

The goal of this paper is to investigate new and simple convergence analysis of dynamic programming for linear quadratic regulator problem of discrete-time linear time-invariant sy…

math.OC2021

Data-Driven Control Design with LMIs and Dynamic Programming

Donghwan Lee, Do Wan Kim

The goal of this paper is to develop data-driven control design and evaluation strategies based on linear matrix inequalities (LMIs) and dynamic programming. We consider determinis…

math.OC2021

Multi-Objective LQG Design with Primal-Dual Method

Donghwan Lee, Do Wan Kim

The goal of this paper is to study a multi-objective linear quadratic Gaussian (LQG) control problem. In particular, we consider an optimal control problem minimizing a quadratic c…

cs.RO20181 cited

Learning to Communicate: A Machine Learning Framework for Heterogeneous Multi-Agent Robotic Systems

Hyung-Jin Yoon, Huaiyu Chen, Kehan Long +4

We present a machine learning framework for multi-agent systems to learn both the optimal policy for maximizing the rewards and the encoding of the high dimensional visual observat…

math.OC2018

Supplemental Material For "Primal-Dual Q-Learning Framework for LQR Design"

Donghwan Lee, Jianghai Hu

Recently, reinforcement learning (RL) is receiving more and more attentions due to its successful demonstrations outperforming human performance in certain challenging tasks. In ou…