Reinforcement Learning Algorithm

Reinforcement learning (RL) algorithms train agents to make optimal decisions by interacting with an environment and maximizing cumulative rewards. Current research emphasizes improving RL's efficiency and stability, focusing on areas like model-based methods incorporating techniques such as Monte Carlo Tree Search, the development of novel algorithms for specific applications (e.g., traffic control, robotics), and addressing challenges in high-dimensional or partially observable environments. The impact of RL spans diverse fields, from optimizing resource allocation in complex systems to developing more effective personalized interventions in healthcare and improving the efficiency of robotic control systems.

Papers

September 18, 2023

Privileged to Predicted: Towards Sensorimotor Reinforcement Learning for Urban Driving
Ege Onat Özsüer, Barış Akgün, Fatma Güney
Reinforcement Learning Autonomous Driving Reinforcement Learning Algorithm Privileged Information Urban Driving Multiple Prediction Privileged Feature Sensorimotor Policy

September 16, 2023

Stylized Table Tennis Robots Skill Learning with Incomplete Human Demonstrations
Xiang Zhu, Zixuan Chen, Jianyu Chen
Reinforcement Learning Reinforcement Learning Algorithm Expert Demonstration Table Tennis Complex Robotic Task

September 7, 2023

Navigation Through Endoluminal Channels Using Q-Learning
Oded Medina, Liora Kleinburd, Nir Shvalb
Q Learning Reinforcement Learning Algorithm Local Navigation

September 1, 2023

End-to-end Lidar-Driven Reinforcement Learning for Autonomous Racing
Meraj Mammadov
Reinforcement Learning Reinforcement Learning Algorithm Reinforcement Learning Agent Autonomous Racing Racing Scenario

August 28, 2023

Target-independent XLA optimization using Reinforcement Learning
Milan Ganai, Haichen Li, Theodore Enns, Yida Wang, Randy Huang
Reinforcement Learning Deep Reinforcement Learning Reinforcement Learning Algorithm Iterative Optimization LLM Based Machine Translation Single Population Optimization Task

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

July 7, 2023

Action-State Dependent Dynamic Model Selection
Francesco Cordoni, Alessio Sancetta
Optimal Policy Reinforcement Learning Algorithm Dynamic Programming Portfolio Selection

June 30, 2023

Design of Induction Machines using Reinforcement Learning
Yasmin SarcheshmehPour, Tommi Ryyppo, Victor Mukherjee, Alex Jung
Reinforcement Learning Product Design Reinforcement Learning Algorithm Induction Motor Electrical Machine

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 27, 2023

Rethinking Closed-loop Training for Autonomous Driving
Chris Zhang, Runsheng Guo, Wenyuan Zeng, Yuwen Xiong, Binbin Dai, Rui Hu, Mengye Ren, Raquel Urtasun
Autonomous Driving Reinforcement Learning Algorithm High Fidelity Closed Loop

June 23, 2023

Reinforcement Learning with Temporal-Logic-Based Causal Diagrams
Yash Paliwal, Rajarshi Roy, Jean-Raphaël Gaglione, Nasim Baharisangari, Daniel Neider, Xiaoming Duan, Ufuk Topcu, Zhe Xu
Reinforcement Learning Causal Graph Reinforcement Learning Algorithm Causal Knowledge Temporal Causal

June 21, 2023

AdCraft: An Advanced Reinforcement Learning Benchmark Environment for Search Engine Marketing Optimization
Maziar Gomrokchi, Owen Levin, Jeffrey Roach, Jonah White
Reinforcement Learning Deep Reinforcement Learning Reinforcement Learning Algorithm Reinforcement Learning Benchmark Convergence Behavior Sponsored Search

June 13, 2023

Using Collision Momentum in Deep Reinforcement Learning Based Adversarial Pedestrian Modeling
Dianwei Chen, Ekim Yurtsever, Keith Redmill, Umit Ozguner
Reinforcement Learning Algorithm Adversarial Reinforcement Learning Driving Algorithm Pedestrian Simulation Social Force Pedestrian Behavior Prediction Impact Mechanic

June 6, 2023

RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Control
Jonas Eschmann, Dario Albani, Giuseppe Loianno
Reinforcement Learning Deep Reinforcement Learning Reinforcement Learning Algorithm Continuous Control Tiny Machine Learning Reinforcement Learning Library

June 2, 2023

Hyperparameters in Reinforcement Learning and How To Tune Them
Theresa Eimer, Marius Lindauer, Roberta Raileanu
Reinforcement Learning Deep Reinforcement Learning Hyperparameter Optimization Reinforcement Learning Algorithm Related Hyperparameters Hyperparameter Landscape Hyperparameter OPtimization

May 31, 2023

Replicability in Reinforcement Learning
Amin Karbasi, Grigoris Velegkas, Lin F. Yang, Felix Zhou
Reinforcement Learning Reinforcement Learning Algorithm List Replicability Replicable Algorithm

May 24, 2023

Neural Lyapunov and Optimal Control
Daniel Layeghi, Steve Tonneau, Michael Mistry
Reinforcement Learning Proximal Policy Optimization Optimal Control Reinforcement Learning Algorithm Lyapunov Function Neural Lyapunov Optimal Value Function

May 2, 2023

An Adaptive Behaviour-Based Strategy for SARs interacting with Older Adults with MCI during a Serious Game Scenario
Eleonora Zedda, Marco Manca, Fabio Paterno, Carmen Santoro
Reinforcement Learning Adaptive Importance Reinforcement Learning Algorithm Sars Cov 2 Older Adult Socially Assistive Robot Serious Game Cognitive Training

April 20, 2023

Topological Guided Actor-Critic Modular Learning of Continuous Systems with Temporal Objectives
Lening Li, Zhentian Qian
Reinforcement Learning Algorithm Continuous Time Temporal Goal Policy Synthesis Actor Critic Framework Online Policy Customization Topological Guidance

April 18, 2023

An adaptive safety layer with hard constraints for safe reinforcement learning in multi-energy management systems
Glenn Ceusters, Muhammad Andy Putratama, Rüdiger Franke, Ann Nowé, Maarten Messagie
Reinforcement Learning Algorithm Safe Reinforcement Learning Hard Constraint Safety Layer Multi Energy