Online Reinforcement Learning

Online reinforcement learning (RL) focuses on training agents to make optimal decisions in dynamic environments through continuous interaction and feedback. Current research emphasizes improving sample efficiency, particularly through offline data pre-training and techniques like prioritized experience replay and ensemble methods, as well as exploring novel model architectures such as Kolmogorov-Arnold Networks. These advancements aim to address challenges like reward sparsity, distribution shifts between offline and online data, and the need for safe and reliable learning in high-stakes applications such as robotics and healthcare, ultimately leading to more robust and efficient RL agents.

Papers

October 11, 2023

Sample-Driven Federated Learning for Energy-Efficient and Real-Time IoT Sensing
Minh Ngoc Luu, Minh-Duong Nguyen, Ebrahim Bedeer, Van Duc Nguyen, Dinh Thai Hoang, Diep N. Nguyen, Quoc-Viet Pham
Online Reinforcement Learning Internet of Thing Network Internet of Thing Environment Global Optimum

September 25, 2023

Designing and evaluating an online reinforcement learning agent for physical exercise recommendations in N-of-1 trials
Dominik Meier, Ipek Ensari, Stefan Konigorski
Product Design Agent Smith Online Reinforcement Learning Physical Activity Randomized Experiment Adaptive Intervention Medical Intervention Personalized Intervention

September 13, 2023

A Real-World Quadrupedal Locomotion Benchmark for Offline Reinforcement Learning
Hongyin Zhang, Shuyu Yang, Donglin Wang
Offline Reinforcement Learning Model Free Online Reinforcement Learning

August 15, 2023

Dyadic Reinforcement Learning
Shuangning Li, Lluis Salvat Niell, Sung Won Choi, Inbal Nahum-Shani, Guy Shani, Susan Murphy
Online Reinforcement Learning Mobile Health Dyadic Regression Model

July 25, 2023

Settling the Sample Complexity of Online Reinforcement Learning
Zihan Zhang, Yuxin Chen, Jason D. Lee, Simon S. Du
Sample Complexity Online Reinforcement Learning Minimax Regret Low Regret

July 9, 2023

A User Study on Explainable Online Reinforcement Learning for Adaptive Systems
Andreas Metzger, Jan Laufer, Felix Feit, Klaus Pohl
Deep Reinforcement Learning Reinforcement Learning Algorithm Online Reinforcement Learning User Study Explainable Reinforcement Learning Adaptive System

June 6, 2023

Boosting Offline Reinforcement Learning with Action Preference Query
Qisen Yang, Shenzhi Wang, Matthieu Gaetan Lin, Shiji Song, Gao Huang
Offline Reinforcement Learning Training Free Online Reinforcement Learning Online Fine Tuning Offline Preference

May 29, 2023

May 19, 2023

Multimodal Web Navigation with Instruction-Finetuned Foundation Models
Hiroki Furuta, Kuang-Huei Lee, Ofir Nachum, Yutaka Matsuo, Aleksandra Faust, Shixiang Shane Gu, Izzeddin Gur
Instruction Following Online Reinforcement Learning Vision Language Foundation Model Multimodal Content Web Automation Offline Pre Training

April 25, 2023

What can online reinforcement learning with function approximation benefit from general coverage conditions?
Fanghui Liu, Luca Viano, Volkan Cevher
Reinforcement Learning Online Reinforcement Learning Efficient Reinforcement Learning Function Approximation Sample Efficient Higher Coverage Rate Linear MDPs

April 19, 2023

Long-Term Fairness with Unknown Dynamics
Tongxin Yin, Reilly Raab, Mingyan Liu, Yang Liu
Online Reinforcement Learning Unknown Dynamic Evolutionary Game Theory Long Term Fairness

April 11, 2023

Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling
Susobhan Ghosh, Raphael Kim, Prasidh Chhabria, Raaz Dwivedi, Predrag Klasnja, Peng Liao, Kelly Zhang, Susan Murphy
Reinforcement Learning Online Reinforcement Learning Digital Healthcare Health Behavior

March 31, 2023

Online Reinforcement Learning in Markov Decision Process Using Linear Programming
Vincent Leon, S. Rasoul Etesami
Markov Decision Process Online Reinforcement Learning Linear Programming Episodic Markov Decision Process Stochastic Reward Regret Rate

March 28, 2023

On-line reinforcement learning for optimization of real-life energy trading strategy
Łukasz Lepak, Paweł Wawrzyński
Optimization Purpose Online Reinforcement Learning Trading Strategy Day Ahead Energy Market Automated Cryptocurrency Trading Energy Arbitrage

March 20, 2023

Bridging Imitation and Online Reinforcement Learning: An Optimistic Tale
Botao Hao, Rahul Jain, Dengwang Tang, Zheng Wen
Imitation Learning Online Reinforcement Learning Based Imitation Generative Training Informative Prior

March 16, 2023

Online Reinforcement Learning in Periodic MDP
Ayush Aniket, Arpan Chattopadhyay
Reinforcement Learning Markov Decision Process Online Reinforcement Learning Calibrated Confidence Piecewise Deterministic Markov Process

February 7, 2023

Online Reinforcement Learning with Uncertain Episode Lengths
Debmalya Mandal, Goran Radanovic, Jiarui Gan, Adish Singla, Rupak Majumdar
Reinforcement Learning Algorithm Online Reinforcement Learning Episodic Reinforcement Learning Uncertain Episode Length

February 6, 2023

Online Reinforcement Learning

Papers

Sample-Driven Federated Learning for Energy-Efficient and Real-Time IoT Sensing

Designing and evaluating an online reinforcement learning agent for physical exercise recommendations in N-of-1 trials

A Real-World Quadrupedal Locomotion Benchmark for Offline Reinforcement Learning

Dyadic Reinforcement Learning

Settling the Sample Complexity of Online Reinforcement Learning

A User Study on Explainable Online Reinforcement Learning for Adaptive Systems

Boosting Offline Reinforcement Learning with Action Preference Query

Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration

Off-Policy RL Algorithms Can be Sample-Efficient for Continuous Control via Sample Multiple Reuse

Multimodal Web Navigation with Instruction-Finetuned Foundation Models

What can online reinforcement learning with function approximation benefit from general coverage conditions?

Long-Term Fairness with Unknown Dynamics

Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling

Online Reinforcement Learning in Markov Decision Process Using Linear Programming

On-line reinforcement learning for optimization of real-life energy trading strategy

Bridging Imitation and Online Reinforcement Learning: An Optimistic Tale

Online Reinforcement Learning in Periodic MDP

Online Reinforcement Learning with Uncertain Episode Lengths

Efficient Online Reinforcement Learning with Offline Data

Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning