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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.

1498papers

Papers - Page 35

November 25, 2023

Agent as Cerebrum, Controller as Cerebellum: Implementing an Embodied LMM-based Agent on Drones
Haoran Zhao, Fengxing Pan, Huqiuyue Ping, Yaoming Zhou
Deep Reinforcement Learning Agent System Agent Smith Robotic System Embodied Vision Various Fast Moving Drone

November 24, 2023

November 23, 2023

Towards Explainable Strategy Templates using NLP Transformers
Pallavi Bagga, Kostas Stathis
Natural Language Explanation Transformer Based Natural Language Deep Reinforcement Learning Specific Heuristic Agent Negotiation Rule Based Explanation

November 20, 2023

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
Electron Microscopy Deep Reinforcement Learning Defect Inspection Feature Extractor Positive Reinforcement

November 16, 2023

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

November 14, 2023

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

November 13, 2023

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

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+1
Genetic Algorithm Deep Reinforcement Learning Single Point Mutation Reinforcement Learning RL Method Flow Shop Scheduling

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
Task Offloading Mobile Edge Computing Deep Reinforcement Learning Computation Offloading

November 2, 2023

November 1, 2023

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

October 31, 2023

Closed Drafting as a Case Study for First-Principle Interpretability, Memory, and Generalizability in Deep Reinforcement Learning
Ryan Rezai, Jason Wang
Deep Q Network Deep Reinforcement Learning Game Mechanic Video Game Memory Trace Stronger Generalizability