Reinforcement Learning Approach

Reinforcement learning (RL) is a machine learning paradigm focused on training agents to make optimal decisions in dynamic environments by maximizing cumulative rewards. Current research emphasizes applying RL to diverse problems, including optimizing complex processes (e.g., space mission planning, manufacturing, traffic control), often employing algorithms like Proximal Policy Optimization (PPO) and Deep Q-Networks (DQN) with neural network architectures. This approach offers significant potential for improving efficiency and adaptability in various fields, from robotics and resource management to financial modeling and healthcare.

Papers

February 21, 2022

Autonomous Warehouse Robot using Deep Q-Learning
Ismot Sadik Peyas, Zahid Hasan, Md. Rafat Rahman Tushar, Al Musabbir, Raisa Mehjabin Azni, Shahnewaz Siddique
Deep Reinforcement Learning Q Learning Robot Navigation Deep Q Learning Reinforcement Learning Approach Warehouse Robot Multi Agent Q Learning

February 5, 2022

Reinforcement learning for multi-item retrieval in the puzzle-based storage system
Jing He, Xinglu Liu, Qiyao Duan, Wai Kin Victor Chan, Mingyao Qi
Reinforcement Learning Deep Q Network Reinforcement Learning Approach Storage System

January 15, 2022

Deep Reinforcement Learning for Shared Autonomous Vehicles (SAV) Fleet Management
Sergio Sainz-Palacios
Reinforcement Learning Deep Reinforcement Learning Reinforcement Learning Approach Connected and Automated Vehicle Shared Autonomous Vehicle Fleet Management

January 11, 2022

Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation
Simon Reichhuber, Sven Tomforde
Reinforcement Learning Active Learning Classification Model Reinforcement Learning Approach Requirement Based Roadmap Self Adaptation Reactive Behavior Active Reinforcement Learning

November 27, 2021

Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion Detection
Hooman Alavizadeh, Julian Jang-Jaccard, Hootan Alavizadeh
Q Learning Intrusion Detection Network Intrusion Detection Deep Q Learning Reinforcement Learning Approach

Reinforcement Learning Approach

Papers

Autonomous Warehouse Robot using Deep Q-Learning

Reinforcement learning for multi-item retrieval in the puzzle-based storage system

Deep Reinforcement Learning for Shared Autonomous Vehicles (SAV) Fleet Management

Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation

Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion Detection