233 citations · 785 across the 38 of their papers we have counts for
38 papers
Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning
Giseung Park, Sungho Choi, Youngchul Sung
This paper proposes a new sequential model learning architecture to solve partially observable Markov decision problems. Rather than compressing sequential information at every tim…
A Max-Min Entropy Framework for Reinforcement Learning
Seungyul Han, Youngchul Sung
In this paper, we propose a max-min entropy framework for reinforcement learning (RL) to overcome the limitation of the soft actor-critic (SAC) algorithm implementing the maximum e…
Training Signal Design for Sparse Channel Estimation in Intelligent Reflecting Surface-Assisted Millimeter-Wave Communication
Song Noh, Heejung Yu, Youngchul Sung
In this paper, the problem of training signal design for intelligent reflecting surface (IRS)-assisted millimeter-wave (mmWave) communication under a sparse channel model is consid…
A Reinforcement Learning Formulation of the Lyapunov Optimization: Application to Edge Computing Systems with Queue Stability
Sohee Bae, Seungyul Han, Youngchul Sung
In this paper, a deep reinforcement learning (DRL)-based approach to the Lyapunov optimization is considered to minimize the time-average penalty while maintaining queue stability.…
A Maximum Mutual Information Framework for Multi-Agent Reinforcement Learning
Woojun Kim, Whiyoung Jung, Myungsik Cho +1
In this paper, we propose a maximum mutual information (MMI) framework for multi-agent reinforcement learning (MARL) to enable multiple agents to learn coordinated behaviors by reg…
Cross-Domain Imitation Learning with a Dual Structure
Sungho Choi, Seungyul Han, Woojun Kim +1
In this paper, we consider cross-domain imitation learning (CDIL) in which an agent in a target domain learns a policy to perform well in the target domain by observing expert demo…