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

February 5, 2024

Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning
Shengyi Huang, Quentin Gallouédec, Florian Felten, Antonin Raffin, Rousslan Fernand Julien Dossa, Yanxiao Zhao, Ryan Sullivan, Viktor Makoviychuk, Denys Makoviichuk, Mohamad H. Danesh, Cyril Roumégous, Jiayi Weng, Chufan Chen, Md Masudur Rahman, João G. M. Araújo, Guorui Quan, Daniel Tan, Timo Klein, Rujikorn Charakorn, Mark Towers, Yann Berthelot, Kinal Mehta, Dipam Chakraborty, Arjun KG, Valentin Charraut, Chang Ye, Zichen Liu, Lucas N. Alegre, Alexander Nikulin, Xiao Hu, Tianlin Liu, Jongwook Choi, Brent Yi
Reinforcement Learning Reinforcement Learning Algorithm Optical Experiment Reinforcement Learning Benchmark

February 1, 2024

Leveraging Approximate Model-based Shielding for Probabilistic Safety Guarantees in Continuous Environments
Alexander W. Goodall, Francesco Belardinelli
Policy Gradient Reinforcement Learning Algorithm Safe Reinforcement Learning Safety Guarantee Continuous Environment Model Based Shielding

January 21, 2024

VQC-Based Reinforcement Learning with Data Re-uploading: Performance and Trainability
Rodrigo Coelho, André Sequeira, Luís Paulo Santos
Reinforcement Learning System Performance Reinforcement Learning Algorithm Variational Quantum Circuit Deep Q Learning Deep Q

January 20, 2024

Detecting Hidden Triggers: Mapping Non-Markov Reward Functions to Markov
Gregory Hyde, Eugene Santos Jr
Reinforcement Learning Algorithm Markov Model Non Markovian Reward Imperceptible Trigger Markovian Reward

December 31, 2023

Tight Finite Time Bounds of Two-Time-Scale Linear Stochastic Approximation with Markovian Noise
Shaan Ul Haque, Sajad Khodadadian, Siva Theja Maguluri
Reinforcement Learning Algorithm Convergence Rate Stochastic Approximation Finite Time

December 14, 2023

Arithmetics-Based Decomposition of Numeral Words -- Arithmetic Conditions give the Unpacking Strategy
Isidor Konrad Maier, Matthias Wolff
Practical Algorithm Reinforcement Learning Algorithm Microbial Decomposition Strategy Discovery Mathematical Understanding Numeral System

December 11, 2023

November 27, 2023

Replay across Experiments: A Natural Extension of Off-Policy RL
Dhruva Tirumala, Thomas Lampe, Jose Enrique Chen, Tuomas Haarnoja, Sandy Huang, Guy Lever, Ben Moran, Tim Hertweck, Leonard Hasenclever, Martin Riedmiller, Nicolas Heess, Markus Wulfmeier
Reinforcement Learning Algorithm Optical Experiment Policy Reinforcement Learning RePLAy Loss Natural Extension Data Replay

November 21, 2023

Decentralised Q-Learning for Multi-Agent Markov Decision Processes with a Satisfiability Criterion
Keshav P. Keval, Vivek S. Borkar
Multi Agent Reinforcement Learning Algorithm Decentralized FL Satisfiability Problem Gossip Algorithm Pre Specified Agent Specific Approachability Problem

November 16, 2023

Runtime Verification of Learning Properties for Reinforcement Learning Algorithms
Tommaso Mannucci, Julio de Oliveira Filho
Reinforcement Learning Practical Algorithm Reinforcement Learning Algorithm Runtime Verification

November 14, 2023

Deep Reinforcement Learning for 2D Physics-Based Object Manipulation in Clutter
Luca Renna
Deep Reinforcement Learning Markov Decision Process Policy Gradient 2 Dimensional Reinforcement Learning Algorithm Dense Clutter Modern Reinforcement Learning

November 12, 2023

An advantage based policy transfer algorithm for reinforcement learning with metrics of transferability
Md Ferdous Alam, Parinaz Naghizadeh, David Hoelzle
Reinforcement Learning Reinforcement Learning Algorithm Task Transferability Metric Library Advantage Feedback Transfer Reinforcement Learning Policy Transfer

November 6, 2023

Reinforcement Learning for Safety Testing: Lessons from A Mobile Robot Case Study
Tom P. Huck, Martin Kaiser, Constantin Cronrath, Bengt Lennartson, Torsten Kröger, Tamim Asfour
Reinforcement Learning Collision Avoidance Reinforcement Learning Algorithm Critical Lesson Safety Critical Safety Validation

November 3, 2023

November 2, 2023

Diffusion Models for Reinforcement Learning: A Survey
Zhengbang Zhu, Hanye Zhao, Haoran He, Yichao Zhong, Shenyu Zhang, Haoquan Guo, Tingting Chen, Weinan Zhang
Reinforcement Learning Diffusion Model Timely Survey Reinforcement Learning Algorithm Reinforcement Learning Task

October 22, 2023

Learning to bag with a simulation-free reinforcement learning framework for robots
Francisco Munguia-Galeano, Jihong Zhu, Juan David Hernández, Ze Ji
LeArning Abstract Non Humanoid Robot Reinforcement Learning Algorithm Stable Bagging Evolutionary Bagging

October 13, 2023

PAGE: Equilibrate Personalization and Generalization in Federated Learning
Qian Chen, Zilong Wang, Jiaqi Hu, Haonan Yan, Jianying Zhou, Xiaodong Lin
Strong Generalization Reinforcement Learning Algorithm Global Model Local Model Web Page

October 7, 2023

Improving Offline-to-Online Reinforcement Learning with Q Conditioned State Entropy Exploration
Ziqi Zhang, Xiao Xiong, Zifeng Zhuang, Jinxin Liu, Donglin Wang
Reinforcement Learning Offline Reinforcement Learning Reinforcement Learning Algorithm Online Fine Tuning Entropy Exploration

Reinforcement Learning Algorithm

Papers

Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning

Leveraging Approximate Model-based Shielding for Probabilistic Safety Guarantees in Continuous Environments

VQC-Based Reinforcement Learning with Data Re-uploading: Performance and Trainability

Detecting Hidden Triggers: Mapping Non-Markov Reward Functions to Markov

Tight Finite Time Bounds of Two-Time-Scale Linear Stochastic Approximation with Markovian Noise

Arithmetics-Based Decomposition of Numeral Words -- Arithmetic Conditions give the Unpacking Strategy

Learning Polynomial Representations of Physical Objects with Application to Certifying Correct Packing Configurations

Spreeze: High-Throughput Parallel Reinforcement Learning Framework

Replay across Experiments: A Natural Extension of Off-Policy RL

Decentralised Q-Learning for Multi-Agent Markov Decision Processes with a Satisfiability Criterion

Runtime Verification of Learning Properties for Reinforcement Learning Algorithms

Deep Reinforcement Learning for 2D Physics-Based Object Manipulation in Clutter

An advantage based policy transfer algorithm for reinforcement learning with metrics of transferability

Reinforcement Learning for Safety Testing: Lessons from A Mobile Robot Case Study

Towards model-free RL algorithms that scale well with unstructured data

Hierarchical Reinforcement Learning for Power Network Topology Control

Diffusion Models for Reinforcement Learning: A Survey

Learning to bag with a simulation-free reinforcement learning framework for robots

PAGE: Equilibrate Personalization and Generalization in Federated Learning

Improving Offline-to-Online Reinforcement Learning with Q Conditioned State Entropy Exploration