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

November 19, 2023

November 18, 2023

Benchmarking Feature Extractors for Reinforcement Learning-Based Semiconductor Defect Localization
Enrique Dehaerne, Bappaditya Dey, Sandip Halder, Stefan De Gendt
Deep Reinforcement Learning Positive Reinforcement Feature Extractor Electron Microscopy Defect Inspection

November 16, 2023

Short vs. Long-term Coordination of Drones: When Distributed Optimization Meets Deep Reinforcement Learning
Chuhao Qin, Evangelos Pournaras
Deep Reinforcement Learning Optimization Purpose Single Drone Various Fast Moving Drone Autonomous Drone Short Text

November 14, 2023

Deep Reinforcement Learning for 2D Physics-Based Object Manipulation in Clutter
Luca Renna
Deep Reinforcement Learning Markov Decision Process Policy Gradient 2 Dimensional Reinforcement Learning Algorithm Dense Clutter Modern Reinforcement Learning

November 13, 2023

TIAGo RL: Simulated Reinforcement Learning Environments with Tactile Data for Mobile Robots
Luca Lach, Francesco Ferro, Robert Haschke
Deep Reinforcement Learning Mobile Robot Tactile Data Tactile Based Open Source Reinforcement Learning

November 10, 2023

Genetic Algorithm enhanced by Deep Reinforcement Learning in parent selection mechanism and mutation : Minimizing makespan in permutation flow shop scheduling problems
Maissa Irmouli, Nourelhouda Benazzoug, Alaa Dania Adimi, Fatma Zohra Rezkellah, Imane Hamzaoui, Thanina Hamitouche, Malika Bessedik, Fatima Si Tayeb
Reinforcement Learning Deep Reinforcement Learning Genetic Algorithm Single Point Mutation Flow Shop Scheduling RL Method

November 7, 2023

November 4, 2023

QOCO: A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning for Mobile Edge Computing
Iman Rahmati, Hamed Shah-Mansouri, Ali Movaghar
Deep Reinforcement Learning Mobile Edge Computing Task Offloading Computation Offloading

November 2, 2023

November 1, 2023

Learning impartial policies for sequential counterfactual explanations using Deep Reinforcement Learning
E. Panagiotou, E. Ntoutsi
Reinforcement Learning Deep Reinforcement Learning Counterfactual Explanation Explainable Artificial Intelligence Probabilistic Model Simple Classifier State Space

October 31, 2023

October 29, 2023

Automaton Distillation: Neuro-Symbolic Transfer Learning for Deep Reinforcement Learning
Suraj Singireddy, Precious Nwaorgu, Andre Beckus, Aden McKinney, Chinwendu Enyioha, Sumit Kumar Jha, George K. Atia, Alvaro Velasquez
Reinforcement Learning Deep Reinforcement Learning Deep Q Network Instruction Distillation

October 28, 2023

October 27, 2023

Deep Reinforcement Learning for Weapons to Targets Assignment in a Hypersonic strike
Brian Gaudet, Kris Drozd, Roberto Furfaro
Deep Reinforcement Learning Autonomous Decision Making Hypersonic Flow Target Assignment