Donghwan Lee | DeepAI

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∙ 02/20/2023

Backstepping Temporal Difference Learning

Off-policy learning ability is an important feature of reinforcement lea...

0 Han-Dong Lim, et al. ∙

∙ 08/10/2022

Block Double-Submission Attack: Block Withholding Can Be Self-Destructive

Proof-of-Work (PoW) is a Sybil control mechanism adopted in blockchain-b...

0 Suhyeon Lee, et al. ∙

∙ 07/25/2022

Finite-Time Analysis of Asynchronous Q-learning under Diminishing Step-Size from Control-Theoretic View

Q-learning has long been one of the most popular reinforcement learning ...

0 Han-Dong Lim, et al. ∙

∙ 05/31/2022

Investigating the Role of Image Retrieval for Visual Localization – An exhaustive benchmark

Visual localization, i.e., camera pose estimation in a known scene, is a...

0 Martin Humenberger, et al. ∙

∙ 04/22/2022

Analysis of Temporal Difference Learning: Linear System Approach

The goal of this technical note is to introduce a new finite-time conver...

0 Donghwan Lee, et al. ∙

∙ 03/13/2022

A Single Correspondence Is Enough: Robust Global Registration to Avoid Degeneracy in Urban Environments

Global registration using 3D point clouds is a crucial technology for mo...

0 Hyungtae Lim, et al. ∙

∙ 03/10/2022

SelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning

Monocular depth estimation in the wild inherently predicts depth up to a...

13 Jaehoon Choi, et al. ∙

∙ 02/11/2022

Regularized Q-learning

Q-learning is widely used algorithm in reinforcement learning community....

0 Han-Dong Lim, et al. ∙

∙ 12/29/2021

Control Theoretic Analysis of Temporal Difference Learning

The goal of this paper is to investigate a control theoretic analysis of...

0 Donghwan Lee, et al. ∙

∙ 09/09/2021

Versions of Gradient Temporal Difference Learning

Sutton, Szepesvári and Maei introduced the first gradient temporal-diffe...

0 Donghwan Lee, et al. ∙

∙ 08/12/2021

DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes

We present a novel approach for estimating depth from a monocular camera...

11 Dongki Jung, et al. ∙

∙ 05/19/2021

Large-scale Localization Datasets in Crowded Indoor Spaces

Estimating the precise location of a camera using visual localization en...

26 Donghwan Lee, et al. ∙

∙ 03/14/2021

Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction

The building sector consumes the largest energy in the world, and there ...

0 Donghwan Lee, et al. ∙

∙ 02/17/2021

Finite-Time Error Analysis of Asynchronous Q-Learning with Discrete-Time Switching System Models

This paper develops a novel framework to analyze the convergence of Q-le...

0 Donghwan Lee, et al. ∙

∙ 11/10/2020

SelfDeco: Self-Supervised Monocular Depth Completion in Challenging Indoor Environments

We present a novel algorithm for self-supervised monocular depth complet...

4 Jaehoon Choi, et al. ∙

∙ 10/06/2020

SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature Extraction

Self-supervised monocular depth estimation has emerged as a promising me...

0 Jaehoon Choi, et al. ∙

∙ 06/25/2020

HARMer: Cyber-attacks Automation and Evaluation

With the increasing growth of cyber-attack incidences, it is important t...

0 Simon Yusuf Enoch, et al. ∙

∙ 02/23/2020

Periodic Q-Learning

The use of target networks is a common practice in deep reinforcement le...

0 Donghwan Lee, et al. ∙

∙ 12/04/2019

A Unified Switching System Perspective and O.D.E. Analysis of Q-Learning Algorithms

In this paper, we introduce a unified framework for analyzing a large fa...

0 Donghwan Lee, et al. ∙

∙ 12/01/2019

Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents

This article reviews recent advances in multi-agent reinforcement learni...

0 Donghwan Lee, et al. ∙

∙ 04/24/2019

Target-Based Temporal Difference Learning

The use of target networks has been a popular and key component of recen...

0 Donghwan Lee, et al. ∙

∙ 12/13/2018

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

We present a machine learning framework for multi-agent systems to learn...

0 Hyung-Jin Yoon, et al. ∙

∙ 09/17/2018

Hidden Markov Model Estimation-Based Q-learning for Partially Observable Markov Decision Process

The objective is to study an on-line Hidden Markov model (HMM) estimatio...

0 Hyung-Jin Yoon, et al. ∙