Deep Reinforcement Learning Approach

Deep reinforcement learning (DRL) employs artificial intelligence to enable agents to learn optimal strategies through trial and error within complex environments. Current research focuses on applying DRL algorithms, such as Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Actor-Critic methods (including A3C and DDPG), to diverse problems ranging from resource management (energy grids, battery systems) and traffic optimization to financial trading and autonomous systems (robotics, vehicle navigation). The significance of DRL lies in its ability to solve complex decision-making problems beyond the capabilities of traditional methods, leading to improved efficiency, safety, and resource allocation across various sectors.

Papers

September 12, 2024

Towards Opinion Shaping: A Deep Reinforcement Learning Approach in Bot-User Interactions
Farbod Siahkali, Saba Samadi, Hamed Kebriaei
Interaction Strategy Deep Reinforcement Learning Approach Deep Deterministic Policy Gradient Human Chatbot Opinion Formation Preference Fine Tuning

July 1, 2024

A Deep Reinforcement Learning Approach to Battery Management in Dairy Farming via Proximal Policy Optimization
Nawazish Ali, Rachael Shaw, Karl Mason
Q Learning Proximal Policy Optimization Deep Reinforcement Learning Approach Dairy Farming Battery Management

May 30, 2024

A Deep Reinforcement Learning Approach for Trading Optimization in the Forex Market with Multi-Agent Asynchronous Distribution
Davoud Sarani, Dr. Parviz Rashidi-Khazaee
Deep Reinforcement Learning Approach Trading Strategy Algorithmic Trading Multi Currency

May 29, 2024

Exploring the impact of traffic signal control and connected and automated vehicles on intersections safety: A deep reinforcement learning approach
Amir Hossein Karbasi, Hao Yang, Saiedeh Razavi
Global Impact Vehicle Plan Traffic Signal Control Deep Reinforcement Learning Approach Adaptive Traffic Signal Control Intersection Safety

May 24, 2024

Transmission Interface Power Flow Adjustment: A Deep Reinforcement Learning Approach based on Multi-task Attribution Map
Shunyu Liu, Wei Luo, Yanzhen Zhou, Kaixuan Chen, Quan Zhang, Huating Xu, Qinglai Guo, Mingli Song
Data Driven Deep Reinforcement Learning Approach Attribution Map Multi Task Optimization

April 12, 2024

Kinematics Modeling of Peroxy Free Radicals: A Deep Reinforcement Learning Approach
Subhadarsi Nayak, Hrithwik Shalu, Joseph Stember
Deep Reinforcement Learning Approach Kinematics Modeling Finite Rate Chemistry

April 4, 2024

A Deep Reinforcement Learning Approach for Security-Aware Service Acquisition in IoT
Marco Arazzi, Serena Nicolazzo, Antonino Nocera
Internet of Thing Deep Reinforcement Learning Approach Internet of Thing Environment

March 16, 2024

Scheduling Drone and Mobile Charger via Hybrid-Action Deep Reinforcement Learning
Jizhe Dou, Haotian Zhang, Guodong Sun
Unmanned Aerial Vehicle Deep Reinforcement Learning Approach Wireless Power Transfer Hybrid Action Reinforcement Learning

March 5, 2024

A Zero-Shot Reinforcement Learning Strategy for Autonomous Guidewire Navigation
Valentina Scarponi, Michel Duprez, Florent Nageotte, Stéphane Cotin
Zero Shot Reinforcement Learning Algorithm Deep Reinforcement Learning Approach Catheter Path Guidewire Segmentation Endovascular Navigation

February 15, 2024

Enhancing Courier Scheduling in Crowdsourced Last-Mile Delivery through Dynamic Shift Extensions: A Deep Reinforcement Learning Approach
Zead Saleh, Ahmad Al Hanbali, Ahmad Baubaid
Sequential Decision Deep Q Network Deep Reinforcement Learning Approach Last Mile Synthetic Shift

February 7, 2024

A Deep Reinforcement Learning Approach for Adaptive Traffic Routing in Next-gen Networks
Akshita Abrol, Purnima Murali Mohan, Tram Truong-Huu
Deep Reinforcement Learning Deep Q Learning Deep Reinforcement Learning Approach Software Defined Traffic Dynamic Next Generation Network

February 4, 2024

Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach
Brian Etter, James Lee Hu, Mohammedreza Ebrahimi, Weifeng Li, Xin Li, Hsinchun Chen
Malware Detection Obfuscation Technique Deep Reinforcement Learning Approach Adversarial Malware Based Malware Proactive Cyber Defense

January 22, 2024

Fast and Scalable Network Slicing by Integrating Deep Learning with Lagrangian Methods
Tianlun Hu, Qi Liao, Qiang Liu, Antonio Massaro, Georg Carle
Deep Learning High Dimensional Deep Reinforcement Learning Approach Network Slicing Lagrangian Method

December 16, 2023

Improving Environment Robustness of Deep Reinforcement Learning Approaches for Autonomous Racing Using Bayesian Optimization-based Curriculum Learning
Rohan Banerjee, Prishita Ray, Mark Campbell
Reinforcement Learning Deep Reinforcement Learning Curriculum Learning Bayesian Perspective Autonomous Racing Deep Reinforcement Learning Approach Irreducible Curriculum

December 11, 2023

Mobile Edge Computing and AI Enabled Web3 Metaverse over 6G Wireless Communications: A Deep Reinforcement Learning Approach
Wenhan Yu, Terence Jie Chua, Jun Zhao
Artificial Intelligence Ubiquitous Semantic Metaverse Deep Reinforcement Learning Approach Mobile Edge Computing Computation Offloading Quality of Service G Wireless Multi User Immersive Interaction

December 1, 2023

Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach
Xingqiu He, Chaoqun You, Tony Q. S. Quek
Reinforcement Learning Markov Decision Process Deep Reinforcement Learning Approach Mobile Edge Computing Age of Information

October 10, 2023

Deep reinforcement learning uncovers processes for separating azeotropic mixtures without prior knowledge
Quirin Göttl, Jonathan Pirnay, Jakob Burger, Dominik G. Grimm
Reinforcement Learning Mixture Component Prior Knowledge Deep Reinforcement Learning Approach Analysis by Synthesis Chemical Process Flowsheets

October 3, 2023

A Deep Reinforcement Learning Approach for Interactive Search with Sentence-level Feedback
Jianghong Zhou, Joyce C. Ho, Chen Lin, Eugene Agichtein
Deep Q Learning Deep Reinforcement Learning Approach

September 9, 2023

Finding Influencers in Complex Networks: An Effective Deep Reinforcement Learning Approach
Changan Liu, Changjun Fan, Zhongzhi Zhang
Deep Reinforcement Learning Complex Network Deep Reinforcement Learning Approach Influence Maximization Social Network Analysis

June 25, 2023

Safety-Critical Scenario Generation Via Reinforcement Learning Based Editing
Haolan Liu, Liangjun Zhang, Siva Kumar Sastry Hari, Jishen Zhao
Generative Model Autonomous Vehicle Deep Reinforcement Learning Approach Safety Critical Scenario Critical Scenario Generation Abstract Plausibility

Deep Reinforcement Learning Approach

Papers

Towards Opinion Shaping: A Deep Reinforcement Learning Approach in Bot-User Interactions

A Deep Reinforcement Learning Approach to Battery Management in Dairy Farming via Proximal Policy Optimization

A Deep Reinforcement Learning Approach for Trading Optimization in the Forex Market with Multi-Agent Asynchronous Distribution

Exploring the impact of traffic signal control and connected and automated vehicles on intersections safety: A deep reinforcement learning approach

Transmission Interface Power Flow Adjustment: A Deep Reinforcement Learning Approach based on Multi-task Attribution Map

Kinematics Modeling of Peroxy Free Radicals: A Deep Reinforcement Learning Approach

A Deep Reinforcement Learning Approach for Security-Aware Service Acquisition in IoT

Scheduling Drone and Mobile Charger via Hybrid-Action Deep Reinforcement Learning

A Zero-Shot Reinforcement Learning Strategy for Autonomous Guidewire Navigation

Enhancing Courier Scheduling in Crowdsourced Last-Mile Delivery through Dynamic Shift Extensions: A Deep Reinforcement Learning Approach

A Deep Reinforcement Learning Approach for Adaptive Traffic Routing in Next-gen Networks

Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach

Fast and Scalable Network Slicing by Integrating Deep Learning with Lagrangian Methods

Improving Environment Robustness of Deep Reinforcement Learning Approaches for Autonomous Racing Using Bayesian Optimization-based Curriculum Learning

Mobile Edge Computing and AI Enabled Web3 Metaverse over 6G Wireless Communications: A Deep Reinforcement Learning Approach

Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach

Deep reinforcement learning uncovers processes for separating azeotropic mixtures without prior knowledge

A Deep Reinforcement Learning Approach for Interactive Search with Sentence-level Feedback

Finding Influencers in Complex Networks: An Effective Deep Reinforcement Learning Approach

Safety-Critical Scenario Generation Via Reinforcement Learning Based Editing