14 citations · 22 across the 14 of their papers we have counts for
5 papers · 1 filter
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…
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…
Hidden Markov Model Estimation-Based Q-learning for Partially Observable Markov Decision Process
Hyung-Jin Yoon, Donghwan Lee, Naira Hovakimyan
The objective is to study an on-line Hidden Markov model (HMM) estimation-based Q-learning algorithm for partially observable Markov decision process (POMDP) on finite state and ac…
Dynamic Programming for POMDP with Jointly Discrete and Continuous State-Spaces
Donghwan Lee, Niao He, Jianghai Hu
In this work, we study dynamic programming (DP) algorithms for partially observable Markov decision processes with jointly continuous and discrete state-spaces. We consider a class…
Primal-Dual Algorithm for Distributed Reinforcement Learning: Distributed GTD
Donghwan Lee, Hyungjin Yoon, Naira Hovakimyan
The goal of this paper is to study a distributed version of the gradient temporal-difference (GTD) learning algorithm for multi-agent Markov decision processes (MDPs). The temporal…