RL Method

Reinforcement learning (RL) focuses on training agents to make optimal decisions in complex environments through trial and error. Current research emphasizes improving sample efficiency, particularly through techniques like splitting and aggregating policy gradients to better utilize parallel computing resources and addressing the challenges of knowledge transfer across different domains to reduce data needs. Prominent algorithms include policy gradient methods (e.g., PPO) and Q-learning, often combined with other techniques such as genetic algorithms or used in conjunction with large language models for tasks like human feedback reinforcement learning. These advancements are driving applications in diverse fields, including robotics, scheduling optimization, and even design processes.

Papers

July 29, 2024

SAPG: Split and Aggregate Policy Gradients
Jayesh Singla, Ananye Agarwal, Deepak Pathak
Policy Gradient Policy Reinforcement Learning Split and Fit RL Method GPU Based

April 26, 2024

Knowledge Transfer for Cross-Domain Reinforcement Learning: A Systematic Review
Sergio A. Serrano, Jose Martinez-Carranza, L. Enrique Sucar
Reinforcement Learning Knowledge Transfer Systematic Review Transfer Learning Approach Domain Knowledge Transfer RL Method Cross Domain Reinforcement Learning

November 10, 2023

Genetic Algorithm enhanced by Deep Reinforcement Learning in parent selection mechanism and mutation : Minimizing makespan in permutation flow shop scheduling problems
Maissa Irmouli, Nourelhouda Benazzoug, Alaa Dania Adimi, Fatma Zohra Rezkellah, Imane Hamzaoui, Thanina Hamitouche, Malika Bessedik, Fatima Si Tayeb
Reinforcement Learning Deep Reinforcement Learning Genetic Algorithm Single Point Mutation Flow Shop Scheduling RL Method

October 9, 2023

May 17, 2023

Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions
Karine Karine, Predrag Klasnja, Susan A. Murphy, Benjamin M. Marlin
Reinforcement Learning Global Impact Policy Gradient Partial Observability Adaptive Intervention Intervention Policy RL Method

March 3, 2023

Decision Transformer under Random Frame Dropping
Kaizhe Hu, Ray Chen Zheng, Yang Gao, Huazhe Xu
Deep Reinforcement Learning Decision Transformer Offline RL RL Method

December 1, 2022

Launchpad: Learning to Schedule Using Offline and Online RL Methods
Vanamala Venkataswamy, Jake Grigsby, Andrew Grimshaw, Yanjun Qi
Offline Reinforcement Learning Data Driven Policy OpTimization Task Scheduling Action Free Offline RL Method

November 6, 2022

Design Process is a Reinforcement Learning Problem
Reza kakooee, Benjamin Dillunberger
Reinforcement Learning Reinforcement Learning Algorithm Reinforcement Learning Problem Design Process RL Method

May 11, 2022

RLOP: RL Methods in Option Pricing from a Mathematical Perspective
Ziheng Chen
Option Pricing Agent Tuning Hedging Strategy Mathematical Understanding RL Method