Reinforcement Learning Solution

Reinforcement learning (RL) is increasingly used to solve complex decision-making problems across diverse domains, aiming to optimize agent behavior through trial-and-error learning. Current research focuses on extending RL's capabilities to multi-agent scenarios, improving robustness and generalization, and applying advanced architectures like deep Q-networks and actor-critic methods to specific applications such as traffic control and autonomous driving. These advancements hold significant promise for optimizing resource allocation (e.g., energy management in vehicles), improving efficiency in complex systems (e.g., multi-UAV coordination), and creating more intelligent and adaptable systems in various fields.

Papers

December 19, 2023

Leading the Pack: N-player Opponent Shaping
Alexandra Souly, Timon Willi, Akbir Khan, Robert Kirk, Chris Lu, Edward Grefenstette, Tim Rocktäschel
Two Player Opponent Shaping Reinforcement Learning Solution Feature Shaping

November 23, 2023

A DRL solution to help reduce the cost in waiting time of securing a traffic light for cyclists
Lucas Magnana, Hervé Rivano, Nicolas Chiabaut
Time Matter Hidden CoST Traffic Light DRL Algorithm Reinforcement Learning Solution

July 24, 2023

Multi-UAV Speed Control with Collision Avoidance and Handover-aware Cell Association: DRL with Action Branching
Zijiang Yan, Wael Jaafar, Bassant Selim, Hina Tabassum
Collision Avoidance Various Fast Moving Drone Action Feature Robot Handover UAV Network Double Deep Q Network Ga DRL Reinforcement Learning Solution Latent Space Communication

November 8, 2022

Progress and summary of reinforcement learning on energy management of MPS-EV
Jincheng Hu, Yang Lin, Liang Chu, Zhuoran Hou, Jihan Li, Jingjing Jiang, Yuanjian Zhang
Reinforcement Learning Much Progress Electric Vehicle Energy Management Reinforcement Learning Solution

July 16, 2022

Robust AI Driving Strategy for Autonomous Vehicles
Subramanya Nageshrao, Yousaf Rahman, Vladimir Ivanovic, Mrdjan Jankovic, Eric Tseng, Michael Hafner, Dimitar Filev
Reinforcement Learning Autonomous Vehicle Automated Driving Strategy Logic Intelligent Vehicle Robust AI Reinforcement Learning Solution

May 12, 2022

Feature and Instance Joint Selection: A Reinforcement Learning Perspective
Wei Fan, Kunpeng Liu, Hao Liu, Hengshu Zhu, Hui Xiong, Yanjie Fu
Efficient Exploration Content Based Feature Reinforcement Learning Approach Instance Selection Joint Selection Reinforcement Learning Solution

March 14, 2022

L2Explorer: A Lifelong Reinforcement Learning Assessment Environment
Erik C. Johnson, Eric Q. Nguyen, Blake Schreurs, Chigozie S. Ewulum, Chace Ashcraft, Neil M. Fendley, Megan M. Baker, Alexander New, Gautam K. Vallabha
Reinforcement Learning Lifelong Learning Continual Reinforcement Learning Continual Learning Algorithm Lifelong Reinforcement Learning Reinforcement Learning Solution

January 15, 2022

Profitable Strategy Design by Using Deep Reinforcement Learning for Trades on Cryptocurrency Markets
Mohsen Asgari, Seyed Hossein Khasteh
Deep Reinforcement Learning Proximal Policy Optimization Generative Adversarial Imitation Learning Post Trade Allocation Cryptocurrency Market Strategy Synthesis Reinforcement Learning Solution