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

July 25, 2022

Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks
Laura Stops, Roel Leenhouts, Qinghe Gao, Artur M. Schweidtmann
Neural Network Graph Drawing Reinforcement Learning Algorithm Reinforcement Learning Agent Hierarchical Reinforcement Learning Actor Critic Analysis by Synthesis Chemical Engineering Chemical Process Flowsheets

July 19, 2022

Bayesian Generational Population-Based Training
Xingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen, Binxin Ru, Michael A. Osborne
Reinforcement Learning Bayesian Optimization Reinforcement Learning Algorithm Generation Model Population Based Training

June 8, 2022

June 1, 2022

RLSS: A Deep Reinforcement Learning Algorithm for Sequential Scene Generation
Azimkhon Ostonov, Peter Wonka, Dominik L. Michels
Action Space Proximal Policy Optimization Reinforcement Learning Algorithm Generative Approach Scene Generation

May 31, 2022

k-Means Maximum Entropy Exploration
Alexander Nedergaard, Matthew Cook
Reinforcement Learning Algorithm Entropy Exploration Artificial Curiosity

May 30, 2022

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

Reinforcement Learning Algorithm

Papers

Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks

Bayesian Generational Population-Based Training

Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines

Sim2real for Reinforcement Learning Driven Next Generation Networks

RLSS: A Deep Reinforcement Learning Algorithm for Sequential Scene Generation

k-Means Maximum Entropy Exploration

SEREN: Knowing When to Explore and When to Exploit

Stock Trading Optimization through Model-based Reinforcement Learning with Resistance Support Relative Strength

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