Deep Reinforcement Learning

Deep reinforcement learning (DRL) aims to train agents to make optimal decisions in complex environments by learning through trial and error. Current research focuses on improving DRL's robustness, sample efficiency, and interpretability, often employing architectures like Proximal Policy Optimization (PPO), deep Q-networks (DQNs), and graph neural networks (GNNs) to address challenges in diverse applications such as robotics, game playing, and resource management. The resulting advancements have significant implications for various fields, enabling the development of more adaptable and efficient autonomous systems across numerous domains.

Papers

August 20, 2023

Deep Reinforcement Learning for Artificial Upwelling Energy Management
Yiyuan Zhang, Wei Fan
Deep Reinforcement Learning Underwater Environment Carbon Sequestration

August 19, 2023

ILCAS: Imitation Learning-Based Configuration-Adaptive Streaming for Live Video Analytics with Cross-Camera Collaboration
Duo Wu, Dayou Zhang, Miao Zhang, Ruoyu Zhang, Fangxin Wang, Shuguang Cui
Deep Reinforcement Learning Feature Imitation Video Analytics Video Coding Video Dynamic Adaptive Streaming Camera Collaboration

August 18, 2023

August 15, 2023

August 14, 2023

Adaptive Tracking of a Single-Rigid-Body Character in Various Environments
Taesoo Kwon, Taehong Gu, Jaewon Ahn, Yoonsang Lee
Deep Reinforcement Learning Rigid Body Centroidal Dynamic Adaptive Tracking

August 12, 2023

CoverNav: Cover Following Navigation Planning in Unstructured Outdoor Environment with Deep Reinforcement Learning
Jumman Hossain, Abu-Zaher Faridee, Nirmalya Roy, Anjan Basak, Derrik E. Asher
Deep Reinforcement Learning Autonomous Vehicle Autonomous Navigation Unmanned Ground Vehicle Unstructured Environment Cover Text Road Scenario

August 11, 2023

Learning Team-Based Navigation: A Review of Deep Reinforcement Learning Techniques for Multi-Agent Pathfinding
Jaehoon Chung, Jamil Fayyad, Younes Al Younes, Homayoun Najjaran
Deep Reinforcement Learning Narrative Review Multi Agent System Multi Agent Path Finding Multi Agent Setting Multi Agent Pathfinding Multi Agent Navigation

August 10, 2023

Commodities Trading through Deep Policy Gradient Methods
Jonas Hanetho
Deep Reinforcement Learning Policy Gradient Algorithmic Trading Commodity Trade

August 9, 2023

Adversarial Deep Reinforcement Learning for Cyber Security in Software Defined Networks
Luke Borchjes, Clement Nyirenda, Louise Leenen
Deep Reinforcement Learning Deep Q Network Software Defined Adversarial Reinforcement Learning Cyber Security Double Deep Q Network

August 5, 2023

Surrogate Empowered Sim2Real Transfer of Deep Reinforcement Learning for ORC Superheat Control
Runze Lin, Yangyang Luo, Xialai Wu, Junghui Chen, Biao Huang, Lei Xie, Hongye Su
Deep Reinforcement Learning Formality Transfer Optimal Controller Energy Management Surrogate Assisted Version Deep Reinforcement Learning Based

August 4, 2023

Deep Reinforcement Learning for Autonomous Spacecraft Inspection using Illumination
David van Wijk, Kyle Dunlap, Manoranjan Majji, Kerianne L. Hobbs
Reinforcement Learning Deep Reinforcement Learning Agent Performance Natural Illumination Unmanned Spacecraft Autonomous Spacecraft

August 3, 2023

Job Shop Scheduling via Deep Reinforcement Learning: a Sequence to Sequence approach
Giovanni Bonetta, Davide Zago, Rossella Cancelliere, Andrea Grosso
Deep Reinforcement Learning End to End Sequence of Sequence Task Scheduling Job Shop Scheduling Job Scheduling

August 2, 2023

Controlling the Solo12 Quadruped Robot with Deep Reinforcement Learning
Michel Aractingi, Pierre-Alexandre Léziart, Thomas Flayols, Julien Perez, Tomi Silander, Philippe Souères
Deep Reinforcement Learning Learning Based Quadruped Robot Learned Controller

August 1, 2023

Target Search and Navigation in Heterogeneous Robot Systems with Deep Reinforcement Learning
Yun Chen, Jiaping Xiao
Reinforcement Learning Deep Reinforcement Learning Local Navigation Heterogeneous Robot Maze Environment

July 31, 2023

July 28, 2023

Autonomous Payload Thermal Control
Alejandro D. Mousist
Deep Reinforcement Learning Thermal Control Autonomous Spacecraft Soft Actor Critic Algorithm Power Dissipation

Deep Reinforcement Learning

Papers

Deep Reinforcement Learning for Artificial Upwelling Energy Management

ILCAS: Imitation Learning-Based Configuration-Adaptive Streaming for Live Video Analytics with Cross-Camera Collaboration

DoCRL: Double Critic Deep Reinforcement Learning for Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Medium Transition

Learning Computational Efficient Bots with Costly Features

Integrating Expert Guidance for Efficient Learning of Safe Overtaking in Autonomous Driving Using Deep Reinforcement Learning

Deep reinforcement learning for process design: Review and perspective

Learning to Identify Critical States for Reinforcement Learning from Videos

Adaptive Tracking of a Single-Rigid-Body Character in Various Environments

CoverNav: Cover Following Navigation Planning in Unstructured Outdoor Environment with Deep Reinforcement Learning

Learning Team-Based Navigation: A Review of Deep Reinforcement Learning Techniques for Multi-Agent Pathfinding

Commodities Trading through Deep Policy Gradient Methods

Adversarial Deep Reinforcement Learning for Cyber Security in Software Defined Networks

Surrogate Empowered Sim2Real Transfer of Deep Reinforcement Learning for ORC Superheat Control

Deep Reinforcement Learning for Autonomous Spacecraft Inspection using Illumination

Job Shop Scheduling via Deep Reinforcement Learning: a Sequence to Sequence approach

Controlling the Solo12 Quadruped Robot with Deep Reinforcement Learning

Target Search and Navigation in Heterogeneous Robot Systems with Deep Reinforcement Learning

Deep Reinforcement Learning of Dexterous Pre-grasp Manipulation for Human-like Functional Categorical Grasping

Distributionally Robust Safety Filter for Learning-Based Control in Active Distribution Systems

Autonomous Payload Thermal Control