Reinforcement Learning Application

Reinforcement learning (RL) is increasingly applied to solve complex real-world problems by training agents to make optimal decisions through trial and error. Current research focuses on improving RL's robustness and safety, particularly through techniques like uncertainty estimation and adversarial training, and scaling RL algorithms to handle massive datasets and distributed computing environments. These advancements are driving impactful applications across diverse fields, including finance, traffic control, and energy systems optimization, where RL offers more efficient and adaptable solutions than traditional methods.

Papers

August 20, 2024

The Evolution of Reinforcement Learning in Quantitative Finance
Nikolaos Pippas, Cagatay Turkay, Elliot A. Ludvig
Reinforcement Learning Multi Agent Multi Agent System Specie Evolution Quantitative Finance Reinforcement Learning Application

July 8, 2024

iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvement
Aoyu Pang, Maonan Wang, Man-On Pun, Chung Shue Chen, Xi Xiong
Large Language Model Reinforcement Learning Intelligent Transportation System Traffic Signal Control Reinforcement Learning Application

June 28, 2024

Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systems
Marine Cauz, Adrien Bolland, Nicolas Wyrsch, Christophe Ballif
Reinforcement Learning Novel Reinforcement Learning Energy System Renewable Energy Source Co Optimization Efficient Design Reinforcement Learning Application Control Co

March 27, 2024

Safe and Robust Reinforcement Learning: Principles and Practice
Taku Yamagata, Raul Santos-Rodriguez
Reinforcement Learning Practice Mode General Principle Robust Reinforcement Learning Reinforcement Learning Application

June 29, 2023

SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores
Zhiyu Mei, Wei Fu, Jiaxuan Gao, Guangju Wang, Huanchen Zhang, Yi Wu
Reinforcement Learning Reinforcement Learning Algorithm Reinforcement Learning Benchmark Complex ECC Core Reinforcement Learning Application

June 8, 2022

Sim2real for Reinforcement Learning Driven Next Generation Networks
Peizheng Li, Jonathan Thomas, Xiaoyang Wang, Hakan Erdol, Abdelrahim Ahmad, Rui Inacio, Shipra Kapoor, Arjun Parekh, Angela Doufexi, Arman Shojaeifard, Robert Piechocki
Reinforcement Learning Reinforcement Learning Algorithm Multi Objective Optimization Problem Next Generation Network Reinforcement Learning Application