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

May 27, 2022

May 26, 2022

Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency
Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang
Reinforcement Learning Representation Learning Markov Decision Process Reinforcement Learning Algorithm Policy OpTimization Sample Efficiency Partially Observed Markov Decision Process Partially Observed

May 20, 2022

LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation
David Ireland, Giovanni Montana
Camera Lens Reinforcement Learning Algorithm Robust Graph Combinatorial Optimisation Problem Non Euclidean Graph Large Scale Combinatorial

May 16, 2022

Qualitative Differences Between Evolutionary Strategies and Reinforcement Learning Methods for Control of Autonomous Agents
Nicola Milano, Stefano Nolfi
Reinforcement Learning External Control Autonomous Agent Reinforcement Learning Algorithm Qualitative Difference Evolution Strategy

May 13, 2022

Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarking
Hanna Krasowski, Jakob Thumm, Marlon Müller, Lukas Schäfer, Xiao Wang, Matthias Althoff
Reinforcement Learning Timely Survey Action Space Reinforcement Learning Algorithm Safe Reinforcement Learning Conceptual Analysis

May 12, 2022

Provably Safe Deep Reinforcement Learning for Robotic Manipulation in Human Environments
Jakob Thumm, Matthias Althoff
Deep Reinforcement Learning Motion Planning Robotic Manipulation Reinforcement Learning Algorithm Dynamic Obstacle Complex Environment Manipulator Control

May 11, 2022

A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning
Archit Sharma, Rehaan Ahmad, Chelsea Finn
Reinforcement Learning Reinforcement Learning Algorithm Based Policy Episodic Reinforcement Learning Distribution Matching Sparse Reward Continuous Control

May 6, 2022

How to Spend Your Robot Time: Bridging Kickstarting and Offline Reinforcement Learning for Vision-based Robotic Manipulation
Alex X. Lee, Coline Devin, Jost Tobias Springenberg, Yuxiang Zhou, Thomas Lampe, Abbas Abdolmaleki, Konstantinos Bousmalis
Reinforcement Learning Offline Reinforcement Learning Reinforcement Learning Algorithm Vision Based Sub Optimal Policy Action Distribution Manipulation Benchmark Robot Time

April 22, 2022

Finite-Time Analysis of Temporal Difference Learning: Discrete-Time Linear System Perspective
Donghwan Lee, Do Wan Kim
Reinforcement Learning Reinforcement Learning Algorithm Discrete Time Temporal Difference Learning Finite Time

April 20, 2022

A Reinforcement Learning-based Volt-VAR Control Dataset and Testing Environment
Yuanqi Gao, Nanpeng Yu
Reinforcement Learning Reinforcement Learning Algorithm Test Environment Based Volt Var Control

April 19, 2022

COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation
Jongmin Lee, Cosmin Paduraru, Daniel J. Mankowitz, Nicolas Heess, Doina Precup, Kee-Eung Kim, Arthur Guez
Reinforcement Learning Optimal Policy Reinforcement Learning Algorithm Policy Evaluation DIstribution Estimation Balancing Efficiency

April 11, 2022

JORLDY: a fully customizable open source framework for reinforcement learning
Kyushik Min, Hyunho Lee, Kwansu Shin, Taehak Lee, Hojoon Lee, Jinwon Choi, Sungho Son
Reinforcement Learning Reinforcement Learning Algorithm Open Source Framework Reinforcement Learning Framework Open Source Reinforcement Learning

April 5, 2022

Learning to Bid Long-Term: Multi-Agent Reinforcement Learning with Long-Term and Sparse Reward in Repeated Auction Games
Jing Tan, Ramin Khalili, Holger Karl
LeArning Abstract Multi Agent Reinforcement Learning Long Term Reinforcement Learning Algorithm Sparse Reward Reward Signal Short Term Sequential Game

March 30, 2022

Learning to Socially Navigate in Pedestrian-rich Environments with Interaction Capacity
Quecheng Qiu, Shunyi Yao, Jing Wang, Jun Ma, Guangda Chen, Jianmin Ji
Reinforcement Learning LeArning Abstract Deep Reinforcement Learning Autonomous Robot Reinforcement Learning Algorithm Social Navigation Rich Interaction Pedestrian Environment

March 29, 2022

On Reinforcement Learning, Effect Handlers, and the State Monad
Ugo Dal Lago, Francesco Gavazzo, Alexis Ghyselen
Reinforcement Learning Reinforcement Learning Algorithm Functional Programming Decision Theory Lambda Calculus Monad Transformer

March 26, 2022

Aggressive Quadrotor Flight Using Curiosity-Driven Reinforcement Learning
Qiyu Sun, Jinbao Fang, Wei Xing Zheng, Yang Tang
Reinforcement Learning Algorithm Curiosity Driven Reinforcement Learning Quadrotor Flight

March 23, 2022

An Optical Control Environment for Benchmarking Reinforcement Learning Algorithms
Abulikemu Abuduweili, Changliu Liu
Reinforcement Learning Deep Reinforcement Learning Reinforcement Learning Algorithm Policy Reinforcement Learning Reinforcement Learning Benchmark Continuous Environment

March 11, 2022

Reinforcement Learning Algorithm

Papers

Provably Sample-Efficient RL with Side Information about Latent Dynamics

Learning to Solve Combinatorial Graph Partitioning Problems via Efficient Exploration

Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency

LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation

Qualitative Differences Between Evolutionary Strategies and Reinforcement Learning Methods for Control of Autonomous Agents

Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarking

Provably Safe Deep Reinforcement Learning for Robotic Manipulation in Human Environments

A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning

How to Spend Your Robot Time: Bridging Kickstarting and Offline Reinforcement Learning for Vision-based Robotic Manipulation

Finite-Time Analysis of Temporal Difference Learning: Discrete-Time Linear System Perspective

A Reinforcement Learning-based Volt-VAR Control Dataset and Testing Environment

COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation

JORLDY: a fully customizable open source framework for reinforcement learning

Learning to Bid Long-Term: Multi-Agent Reinforcement Learning with Long-Term and Sparse Reward in Repeated Auction Games

Learning to Socially Navigate in Pedestrian-rich Environments with Interaction Capacity

On Reinforcement Learning, Effect Handlers, and the State Monad

Aggressive Quadrotor Flight Using Curiosity-Driven Reinforcement Learning

An Optical Control Environment for Benchmarking Reinforcement Learning Algorithms

A Machine Learning Approach for Prosumer Management in Intraday Electricity Markets

Deep Binary Reinforcement Learning for Scalable Verification