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 4, 2024

Scenario-based Thermal Management Parametrization Through Deep Reinforcement Learning
Thomas Rudolf, Philip Muhl, Sören Hohmann, Lutz Eckstein
Deep Reinforcement Learning Thermal Management Auto Tuning Enhanced Control

August 3, 2024

Deep Reinforcement Learning for Dynamic Order Picking in Warehouse Operations
Sasan Mahmoudinazlou, Abhay Sobhanan, Hadi Charkhgard, Ali Eshragh, George Dunn
Deep Reinforcement Learning Warehouse Environment Robotic Picking Warehouse Layout Order Picking Picker Routing

July 31, 2024

July 29, 2024

Quantum Machine Learning Architecture Search via Deep Reinforcement Learning
Xin Dai, Tzu-Chieh Wei, Shinjae Yoo, Samuel Yen-Chi Chen
Deep Reinforcement Learning Quantum Machine Learning Quantum Computing Quantum Circuit Quantum Architecture Search

July 28, 2024

July 26, 2024

SHANGUS: Deep Reinforcement Learning Meets Heuristic Optimization for Speedy Frontier-Based Exploration of Autonomous Vehicles in Unknown Spaces
Seunghyeop Nam, Tuan Anh Nguyen, Eunmi Choi, Dugki Min
Deep Reinforcement Learning Autonomous Vehicle Active Exploration Exploration Efficiency Frontier Based Exploration Goal Conditioned Exploration Optimization Heuristic Unknown Space

July 25, 2024

July 23, 2024

Neural Network-Based Bandit: A Medium Access Control for the IIoT Alarm Scenario
Prasoon Raghuwanshi, Onel Luis Alcaraz López, Neelesh B. Mehta, Hirley Alves, Matti Latva-aho
Deep Reinforcement Learning Multi Armed Bandit Neural Topic Random Access Industrial Internet of Thing Bandit Identification Industrial Internet Medium Access Control

July 21, 2024

Mitigating Deep Reinforcement Learning Backdoors in the Neural Activation Space
Sanyam Vyas, Chris Hicks, Vasilios Mavroudis
Deep Reinforcement Learning Backdoor Attack Backdoor Trigger Activation Space Monotone Adversary

July 19, 2024

July 18, 2024

Deep Reinforcement Learning

Papers

Scenario-based Thermal Management Parametrization Through Deep Reinforcement Learning

Deep Reinforcement Learning for Dynamic Order Picking in Warehouse Operations

Adaptive Transit Signal Priority based on Deep Reinforcement Learning and Connected Vehicles in a Traffic Microsimulation Environment

Multi-agent reinforcement learning for the control of three-dimensional Rayleigh-B\'enard convection

Image-Based Deep Reinforcement Learning with Intrinsically Motivated Stimuli: On the Execution of Complex Robotic Tasks

Quantum Machine Learning Architecture Search via Deep Reinforcement Learning

The Interpretability of Codebooks in Model-Based Reinforcement Learning is Limited

NAVIX: Scaling MiniGrid Environments with JAX

SHANGUS: Deep Reinforcement Learning Meets Heuristic Optimization for Speedy Frontier-Based Exploration of Autonomous Vehicles in Unknown Spaces

Adaptive Terminal Sliding Mode Control Using Deep Reinforcement Learning for Zero-Force Control of Exoskeleton Robot Systems

Personalized and Context-aware Route Planning for Edge-assisted Vehicles

Neural Network-Based Bandit: A Medium Access Control for the IIoT Alarm Scenario

Mitigating Deep Reinforcement Learning Backdoors in the Neural Activation Space

Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agent

A Comparative Study of Deep Reinforcement Learning Models: DQN vs PPO vs A2C

Random Latent Exploration for Deep Reinforcement Learning

Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization

Deep Reinforcement Learning for Multi-Objective Optimization: Enhancing Wind Turbine Energy Generation while Mitigating Noise Emissions

Analyzing and Bridging the Gap between Maximizing Total Reward and Discounted Reward in Deep Reinforcement Learning

Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learning