Deep Reinforcement

Deep reinforcement learning (DRL) aims to train agents to make optimal decisions in complex environments by learning through trial and error, guided by reward signals. Current research focuses on improving the efficiency and robustness of DRL algorithms, particularly through the use of attention mechanisms, recurrent networks, and hybrid approaches combining DRL with traditional planning methods like A*. These advancements are driving progress in diverse applications, including autonomous robotics, vehicle control, and resource optimization, by enabling agents to learn effective strategies in challenging and dynamic settings. The interpretability of DRL models and the development of efficient training strategies remain active areas of investigation.

Papers

November 8, 2024

November 1, 2024

Enhancing Adaptive Mixed-Criticality Scheduling with Deep Reinforcement Learning
Bruno Mendes (1), Pedro F. Souto (1 and 2), Pedro C. Diniz (2) ((1) Department of Informatics Engineering (DEI) Faculty of Engineering of the University of Porto (FEUP) (2) CISTER Research Centre)
LeArning Abstract Deep Reinforcement Real Time Scheduling Adaptive Scheduling

October 27, 2024

Efficient Diversity-based Experience Replay for Deep Reinforcement Learning
Kaiyan Zhao, Yiming Wang, Yuyang Chen, Xiaoguang Niu, Yan Li, Leong Hou U
LeArning Abstract Deep Reinforcement Learning High Efficiency Sparse Reward Deep Reinforcement Experience Replay Sparse Reward Environment

October 25, 2024

Enhancing Battery Storage Energy Arbitrage with Deep Reinforcement Learning and Time-Series Forecasting
Manuel Sage, Joshua Campbell, Yaoyao Fiona Zhao
Deep Learning LeArning Abstract Deep Reinforcement Learning Time Series Forecasting Deep Q Network Deep Reinforcement Energy Storage Electricity Price Energy Arbitrage

October 20, 2024

AssemblyComplete: 3D Combinatorial Construction with Deep Reinforcement Learning
Alan Chen, Changliu Liu
LeArning Abstract Robotics Domain Assembly Task Deep Reinforcement 3D Graph

October 17, 2024

Adversarial Inception for Bounded Backdoor Poisoning in Deep Reinforcement Learning
Ethan Rathbun, Christopher Amato, Alina Oprea
Reinforcement Learning Backdoor Attack Adversarial Learning Deep Reinforcement Adversarial Behavior Backdoor Poisoning Attack

October 15, 2024

Advanced Persistent Threats (APT) Attribution Using Deep Reinforcement Learning
Animesh Singh Basnet, Mohamed Chahine Ghanem, Dipo Dunsin, Wiktor Sowinski-Mydlarz
LeArning Abstract Deep Reinforcement Learning Deep Reinforcement Source Attribution Advanced Persistent Threat Malware Representation Behavioural Analysis Threat Attribution

October 14, 2024

Enhancing Robustness in Deep Reinforcement Learning: A Lyapunov Exponent Approach
Rory Young, Nicolas Pugeault
Deep Reinforcement Learning Constructive Approach Deep Reinforcement Deep RL Better Robustness Continuous Control Task Lyapunov Exponent

October 13, 2024

SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning
Hojoon Lee, Dongyoon Hwang, Donghu Kim, Hyunseung Kim, Jun Jet Tai, Kaushik Subramanian, Peter R. Wurman, Jaegul Choo, Peter Stone, Takuma Seno
LeArning Abstract Many Parameter Deep Reinforcement Deep RL Normalization Layer Simplicity Bias

September 23, 2024

Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments
Keqin Li, Jiajing Chen, Denzhi Yu, Tao Dajun, Xinyu Qiu, Lian Jieting, Sun Baiwei, Zhang Shengyuan, Zhenyu Wan, Ran Ji, Bo Hong, Fanghao Ni
Reinforcement Learning Obstacle Avoidance Robot Motion Deep Reinforcement Warehouse Environment Based Obstacle Avoidance Warehouse Robot

September 18, 2024

Improving Soft-Capture Phase Success in Space Debris Removal Missions: Leveraging Deep Reinforcement Learning and Tactile Feedback
Bahador Beigomi, Zheng H. Zhu
Reinforcement Learning Deep Reinforcement Learning Deep Reinforcement Tactile Feedback Capturing Maneuver Robot Operator Space Debris Removal

September 11, 2024

Autonomous loading of ore piles with Load-Haul-Dump machines using Deep Reinforcement Learning
Rodrigo Salas, Francisco Leiva, Javier Ruiz-del-Solar
Deep Reinforcement Learning Deep Reinforcement Dynamic Loading Dynamic Maneuver

July 5, 2024

MetaLoco: Universal Quadrupedal Locomotion with Meta-Reinforcement Learning and Motion Imitation
Fatemeh Zargarbashi, Fabrizio Di Giuro, Jin Cheng, Dongho Kang, Bhavya Sukhija, Stelian Coros
Non Humanoid Robot Meta Reinforcement Learning Deep Reinforcement Quadrupedal Locomotion

July 1, 2024

Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinforcement Learning-Based Planning Framework
Xibo Li, Shruti Patel, Christof Büskens
Deep Reinforcement Learning Autonomous Driving Learning Based Collision Free Deep Reinforcement Path Planner Local Trajectory

June 28, 2024

Edge-DIRECT: A Deep Reinforcement Learning-based Method for Solving Heterogeneous Electric Vehicle Routing Problem with Time Window Constraints
Arash Mozhdehi, Mahdi Mohammadizadeh, Xin Wang
Extreme Edge Deep Reinforcement Temporal Constraint Learning Based Method Electric Vehicle Routing Problem

June 18, 2024

A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo
Miguel Vasco, Takuma Seno, Kenta Kawamoto, Kaushik Subramanian, Peter R. Wurman, Peter Stone
Autonomous Vehicle Autonomous Racing Deep Reinforcement Egocentric Camera Vision Based Reinforcement Learning

June 17, 2024

Online Context Learning for Socially-compliant Navigation
Iaroslav Okunevich, Alexandre Lombard, Tomas Krajnik, Yassine Ruichek, Zhi Yan
Context Learning Deep Reinforcement Social Navigation Bot Generated Text Robot Goal

June 7, 2024

Optimization of geological carbon storage operations with multimodal latent dynamic model and deep reinforcement learning
Zhongzheng Wang, Yuntian Chen, Guodong Chen, Dongxiao Zhang
Deep Reinforcement Learning Optimization Purpose Deep Reinforcement Numerical Simulation Geological Carbon Storage Multimodal Latent

May 21, 2024

Deep Reinforcement Learning for Time-Critical Wilderness Search And Rescue Using Drones
Jan-Hendrik Ewers, David Anderson, Douglas Thomson
Deep Reinforcement Learning Various Fast Moving Drone Deep Reinforcement Efficient Search Wilderness Search

Deep Reinforcement

Papers

Tangled Program Graphs as an alternative to DRL-based control algorithms for UAVs

Towards Active Flow Control Strategies Through Deep Reinforcement Learning

Enhancing Adaptive Mixed-Criticality Scheduling with Deep Reinforcement Learning

Efficient Diversity-based Experience Replay for Deep Reinforcement Learning

Enhancing Battery Storage Energy Arbitrage with Deep Reinforcement Learning and Time-Series Forecasting

AssemblyComplete: 3D Combinatorial Construction with Deep Reinforcement Learning

Adversarial Inception for Bounded Backdoor Poisoning in Deep Reinforcement Learning

Advanced Persistent Threats (APT) Attribution Using Deep Reinforcement Learning

Enhancing Robustness in Deep Reinforcement Learning: A Lyapunov Exponent Approach

SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning

Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments

Improving Soft-Capture Phase Success in Space Debris Removal Missions: Leveraging Deep Reinforcement Learning and Tactile Feedback

Autonomous loading of ore piles with Load-Haul-Dump machines using Deep Reinforcement Learning

MetaLoco: Universal Quadrupedal Locomotion with Meta-Reinforcement Learning and Motion Imitation

Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinforcement Learning-Based Planning Framework

Edge-DIRECT: A Deep Reinforcement Learning-based Method for Solving Heterogeneous Electric Vehicle Routing Problem with Time Window Constraints

A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo

Online Context Learning for Socially-compliant Navigation

Optimization of geological carbon storage operations with multimodal latent dynamic model and deep reinforcement learning

Deep Reinforcement Learning for Time-Critical Wilderness Search And Rescue Using Drones