Reinforcement Learning Agent

Reinforcement learning (RL) agents are computational systems designed to learn optimal decision-making strategies through trial and error, aiming to maximize cumulative rewards within a defined environment. Current research emphasizes improving RL agent efficiency and robustness, focusing on areas like scalable architecture search, offline training with real-world and simulated data, and incorporating safety mechanisms and ethical considerations into agent design. These advancements are significant for diverse applications, including building energy optimization, personalized recommendations, and autonomous systems, driving progress in both theoretical understanding and practical deployment of RL.

Papers

November 18, 2024

Syllabus: Portable Curricula for Reinforcement Learning Agents
Ryan Sullivan, Ryan Pégoud, Ameen Ur Rahmen, Xinchen Yang, Junyun Huang, Aayush Verma, Nistha Mitra, John P. Dickerson
Reinforcement Learning Curriculum Learning Reinforcement Learning Agent Course Specific Context Multi Agent RL Reinforcement Learning Library Neural MMO

November 17, 2024

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents
Benjamin M. Peter, Mert Korkali
Reinforcement Learning Proximal Policy Optimization Reinforcement Learning Agent Power Network Hybrid Policy

November 13, 2024

October 30, 2024

October 29, 2024

October 28, 2024

A Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applications
Sriniketh Vangaru, Daniel Rosen, Dylan Green, Raphael Rodriguez, Maxwell Wiecek, Amos Johnson, Alyse M. Jones, William C. Headley
Reinforcement Learning Financial Application Multi Agent Reinforcement Learning Reinforcement Learning Agent Cognitive Radio

October 15, 2024

Safety Filtering While Training: Improving the Performance and Sample Efficiency of Reinforcement Learning Agents
Federico Pizarro Bejarano, Lukas Brunke, Angela P. Schoellig
Reinforcement Learning Training Data High Efficiency System Performance Reinforcement Learning Agent Safety Filter Standard Reinforcement Learning

October 8, 2024

RL, but don't do anything I wouldn't do
Michael K. Cohen, Marcus Hutter, Yoshua Bengio, Stuart Russell
Reinforcement Learning Reinforcement Learning Agent Human Like RL RL Agent

October 2, 2024

September 22, 2024

COSBO: Conservative Offline Simulation-Based Policy Optimization
Eshagh Kargar, Ville Kyrki
Offline Reinforcement Learning Policy OpTimization Reinforcement Learning Agent Long Term Cooperation Offline Policy Learning Reality Gap

September 6, 2024

Stacked Universal Successor Feature Approximators for Safety in Reinforcement Learning
Ian Cannon, Washington Garcia, Thomas Gresavage, Joseph Saurine, Ian Leong, Jared Culbertson
Reinforcement Learning Human SAFETY Reinforcement Learning Agent Continuous Control Safety Critical Control Reinforcement Learning Environment Successor Representation

September 5, 2024

CHIRPs: Change-Induced Regret Proxy metrics for Lifelong Reinforcement Learning
John Birkbeck, Adam Sobey, Federico Cerutti, Katherine Heseltine Hurley Flynn, Timothy J. Norman
Reinforcement Learning Agent Agent Performance Lifelong Reinforcement Learning Bell and Whistle Static Regret

August 28, 2024

An Extremely Data-efficient and Generative LLM-based Reinforcement Learning Agent for Recommenders
Shuang Feng, Grace Feng
Reinforcement Learning Generative Question Reward Model Reinforcement Learning Agent Data Efficient

August 19, 2024

Algorithmic Contract Design with Reinforcement Learning Agents
David Molina Concha, Kyeonghyeon Park, Hyun-Rok Lee, Taesik Lee, Chi-Guhn Lee
Reinforcement Learning Agent Principal Agent Contract Design

August 14, 2024

SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion Planning
Jianye Xu, Pan Hu, Bassam Alrifaee
Strong Generalization Multi Agent Reinforcement Learning Motion Planning Reinforcement Learning Agent

August 12, 2024

Online Optimization of Curriculum Learning Schedules using Evolutionary Optimization
Mohit Jiwatode, Leon Schlecht, Alexander Dockhorn
Evolutionary Algorithm Curriculum Learning Reinforcement Learning Agent Online Optimization Evolutionary Optimization

Reinforcement Learning Agent

Papers

Syllabus: Portable Curricula for Reinforcement Learning Agents

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent

RLInspect: An Interactive Visual Approach to Assess Reinforcement Learning Algorithm

Mechanistic Interpretability of Reinforcement Learning Agents

Stepping Out of the Shadows: Reinforcement Learning in Shadow Mode

Predicting Future Actions of Reinforcement Learning Agents

Human-Readable Programs as Actors of Reinforcement Learning Agents Using Critic-Moderated Evolution

A Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applications

Safety Filtering While Training: Improving the Performance and Sample Efficiency of Reinforcement Learning Agents

RL, but don't do anything I wouldn't do

Scalable Reinforcement Learning-based Neural Architecture Search

Real-World Data and Calibrated Simulation Suite for Offline Training of Reinforcement Learning Agents to Optimize Energy and Emission in Buildings for Environmental Sustainability

COSBO: Conservative Offline Simulation-Based Policy Optimization

Stacked Universal Successor Feature Approximators for Safety in Reinforcement Learning

CHIRPs: Change-Induced Regret Proxy metrics for Lifelong Reinforcement Learning

An Extremely Data-efficient and Generative LLM-based Reinforcement Learning Agent for Recommenders

Algorithmic Contract Design with Reinforcement Learning Agents

SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion Planning

Online Optimization of Curriculum Learning Schedules using Evolutionary Optimization