1 citations · 1 across the 3 of their papers we have counts for
7 papers
Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction
Donghwan Lee, Niao He, Seungjae Lee +2
The building sector consumes the largest energy in the world, and there have been considerable research interests in energy consumption and comfort management of buildings. Inspire…
SelfDeco: Self-Supervised Monocular Depth Completion in Challenging Indoor Environments
Jaehoon Choi, Dongki Jung, Yonghan Lee +3
We present a novel algorithm for self-supervised monocular depth completion. Our approach is based on training a neural network that requires only sparse depth measurements and cor…
SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature Extraction
Jaehoon Choi, Dongki Jung, Donghwan Lee +1
Self-supervised monocular depth estimation has emerged as a promising method because it does not require groundtruth depth maps during training. As an alternative for the groundtru…
Periodic Q-Learning
Donghwan Lee, Niao He
The use of target networks is a common practice in deep reinforcement learning for stabilizing the training; however, theoretical understanding of this technique is still limited.…
A Unified Switching System Perspective and O.D.E. Analysis of Q-Learning Algorithms
Donghwan Lee, Niao He
In this paper, we introduce a unified framework for analyzing a large family of Q-learning algorithms, based on switching system perspectives and ODE-based stochastic approximation…
Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents
Donghwan Lee, Niao He, Parameswaran Kamalaruban +1
This article reviews recent advances in multi-agent reinforcement learning algorithms for large-scale control systems and communication networks, which learn to communicate and coo…