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

August 7, 2024

AI-Driven approach for sustainable extraction of earth's subsurface renewable energy while minimizing seismic activity
Diego Gutierrez-Oribio, Alexandros Stathas, Ioannis Stefanou
Reinforcement Learning Reinforcement Learning Algorithm Process Extraction Renewable Energy AI Based Seismic Activity Geothermal Resource Subsurface Anatomy

July 23, 2024

Automatic Environment Shaping is the Next Frontier in RL
Younghyo Park, Gabriel B. Margolis, Pulkit Agrawal
Reinforcement Learning Reinforcement Learning Algorithm Policy OpTimization Robotic Task New Frontier Unsupervised Environment Design

July 5, 2024

Gradient-based Regularization for Action Smoothness in Robotic Control with Reinforcement Learning
I Lee, Hoang-Giang Cao, Cong-Tinh Dao, Yu-Cheng Chen, I-Chen Wu
Reinforcement Learning Deep Reinforcement Learning Reinforcement Learning Algorithm Robot Control Smooth Animation Action Correction

June 29, 2024

PUZZLES: A Benchmark for Neural Algorithmic Reasoning
Benjamin Estermann, Luca A. Lanzendörfer, Yannick Niedermayr, Roger Wattenhofer
Reinforcement Learning New Benchmark Reinforcement Learning Algorithm Logical Reasoning Puzzle Solving Neural Algorithmic Reasoning Algorithmic Reasoning Logical Puzzle

June 26, 2024

Reinforcement Learning with Intrinsically Motivated Feedback Graph for Lost-sales Inventory Control
Zifan Liu, Xinran Li, Shibo Chen, Gen Li, Jiashuo Jiang, Jun Zhang
Reinforcement Learning Reinforcement Learning Algorithm Intrinsic Reward Inventory Management Feedback Graph

June 21, 2024

An Idiosyncrasy of Time-discretization in Reinforcement Learning
Kris De Asis, Richard S. Sutton
Reinforcement Learning Reinforcement Learning Algorithm Discrete Time Time Discretization Atypical Speech

June 20, 2024

Beyond Optimism: Exploration With Partially Observable Rewards
Simone Parisi, Alireza Kazemipour, Michael Bowling
Reinforcement Learning Environment Exploration Reinforcement Learning Algorithm Exploration Method Reward Structure

June 19, 2024

June 18, 2024

A Systematization of the Wagner Framework: Graph Theory Conjectures and Reinforcement Learning
Flora Angileri, Giulia Lombardi, Andrea Fois, Renato Faraone, Carlo Metta, Michele Salvi, Luigi Amedeo Bianchi, Marco Fantozzi, Silvia Giulia Galfrè, Daniele Pavesi, Maurizio Parton, Francesco Morandin
Reinforcement Learning Reinforcement Learning Algorithm Unifying Framework Graph Problem Single Player Graph Building Game

June 10, 2024

Is Value Functions Estimation with Classification Plug-and-play for Offline Reinforcement Learning?
Denis Tarasov, Kirill Brilliantov, Dmitrii Kharlapenko
Deep Neural Network Deep Reinforcement Learning Offline Reinforcement Learning Reinforcement Learning Algorithm Value Function Cross Entropy Plug and Play

June 9, 2024

ICU-Sepsis: A Benchmark MDP Built from Real Medical Data
Kartik Choudhary, Dhawal Gupta, Philip S. Thomas
Reinforcement Learning Reinforcement Learning Algorithm Medical Data

June 7, 2024

Reinforcement Learning and Regret Bounds for Admission Control
Lucas Weber, Ana Bušić, Jiamin Zhu
Reinforcement Learning Markov Decision Process Reinforcement Learning Algorithm Regret Bound Low Regret Customer Side Queue Admission Control

June 3, 2024

Deep reinforcement learning for weakly coupled MDP's with continuous actions
Francisco Robledo, Urtzi Ayesta, Konstantin Avrachenkov
Deep Reinforcement Learning Action Space Reinforcement Learning Algorithm Lagrangian Relaxation Efficient Policy Learning Continuous Action

June 2, 2024

Learning-Based Verification of Stochastic Dynamical Systems with Neural Network Policies
Thom Badings, Wietze Koops, Sebastian Junges, Nils Jansen
Reinforcement Learning Algorithm State Space Stochastic Dynamical System Neural Network Policy Reach Avoid

May 28, 2024

Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination
Zhiyao Luo, Yangchen Pan, Peter Watkinson, Tingting Zhu
Reinforcement Learning Markov Decision Process Offline Reinforcement Learning Reinforcement Learning Algorithm Dynamic Treatment Regime

May 20, 2024

Highway Graph to Accelerate Reinforcement Learning
Zidu Yin, Zhen Zhang, Dong Gong, Stefano V. Albrecht, Javen Q. Shi
Reinforcement Learning Reinforcement Learning Algorithm Policy Reinforcement Learning Model Based Planning Road Network Graph

May 16, 2024

Combining RL and IL using a dynamic, performance-based modulation over learning signals and its application to local planning
Francisco Leiva, Javier Ruiz-del-Solar
Reinforcement Learning Imitation Learning Mobile Robot Reinforcement Learning Algorithm Behavior Cloning Abnormal Signal Modulation Scheme Local Planning

May 13, 2024

Neural Network Compression for Reinforcement Learning Tasks
Dmitry A. Ivanov, Denis A. Larionov, Oleg V. Maslennikov, Vladimir V. Voevodin
Reinforcement Learning Reinforcement Learning Algorithm Sparsity Increase Efficient Inference Neural Network Compression Neural Network Inference Reinforcement Learning Task

May 7, 2024

SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory Systems
Kailash Gogineni, Sai Santosh Dayapule, Juan Gómez-Luna, Karthikeya Gogineni, Peng Wei, Tian Lan, Mohammad Sadrosadati, Onur Mutlu, Guru Venkataramani
Reinforcement Learning System Description Reinforcement Learning Algorithm Efficient Reinforcement Learning Processing in Memory SWIFT DynGFN Tabular Q Learning