Understanding Reinforcement

Reinforcement learning (RL) focuses on training agents to make optimal decisions by interacting with an environment and receiving rewards or penalties. Current research emphasizes improving RL's efficiency and applicability, exploring techniques like integrating RL with other methods (e.g., diffusion models, digital twins) to address challenges such as slow convergence and safe exploration. These advancements are impacting diverse fields, from optimizing resource management in complex networks to enhancing the realism and efficiency of crowd simulations in computer graphics, demonstrating RL's broad utility in solving sequential decision-making problems. Furthermore, research is actively developing principled methods for combining RL with other learning paradigms, such as imitation learning, to leverage the strengths of both approaches.

Papers

July 18, 2024

Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review
Masatoshi Uehara, Yulai Zhao, Tommaso Biancalani, Sergey Levine
Diffusion Model Fine Tuning Narrative Review Tutorial Review Understanding Reinforcement

June 12, 2024

Toward Enhanced Reinforcement Learning-Based Resource Management via Digital Twin: Opportunities, Applications, and Challenges
Nan Cheng, Xiucheng Wang, Zan Li, Zhisheng Yin, Tom Luan, Xuemin Shen
Reinforcement Learning Deep Reinforcement Learning Technical Challenge Financial Application Digital Twin Multiple Unmanned Aerial Vehicle Resource Management Inefficient Exploration Understanding Reinforcement

July 6, 2023

TGRL: An Algorithm for Teacher Guided Reinforcement Learning
Idan Shenfeld, Zhang-Wei Hong, Aviv Tamar, Pulkit Agrawal
Reinforcement Learning Practical Algorithm Sequential Decision Making Problem Teacher Student Sim to Real Reinforcement Learning Understanding Reinforcement

February 9, 2023

RayNet: A Simulation Platform for Developing Reinforcement Learning-Driven Network Protocols
Luca Giacomoni, Basil Benny, George Parisis
Reinforcement Learning Simulation Platform Understanding Reinforcement Protocol Learning

September 19, 2022

Understanding reinforcement learned crowds
Ariel Kwiatkowski, Vicky Kalogeiton, Julien Pettré, Marie-Paule Cani
Reinforcement Learning Crowded Environment Synthetic Trajectory Reward Function Design Crowd Analysis Egocentric World Model Understanding Reinforcement

June 23, 2022

CGAR: Critic Guided Action Redistribution in Reinforcement Leaning
Tairan Huang, Xu Li, Hao Li, Mingming Sun, Ping Li
Positive Reinforcement Policy Actor Critic Policy Distribution Intrinsic Exploration Understanding Reinforcement

April 25, 2022

Reinforcement Teaching
Alex Lewandowski, Calarina Muslimani, Dale Schuurmans, Matthew E. Taylor, Jun Luo
Meta Learning Reinforcement Learning Research Meta Learning Framework Understanding Reinforcement