RL Agent

Reinforcement learning (RL) agents are computational entities designed to learn optimal decision-making strategies through trial and error within a defined environment. Current research emphasizes improving data efficiency, particularly through techniques like curriculum learning, action masking, and the integration of quantum computing methods. These advancements are crucial for deploying RL agents in complex real-world scenarios, such as cybersecurity, robotics, and healthcare, where data scarcity or safety concerns are paramount. Furthermore, significant effort is dedicated to enhancing the explainability and robustness of RL agents, addressing challenges like negative transfer and the impact of reward misspecification.

Papers

October 9, 2024

Identifying and Addressing Delusions for Target-Directed Decision-Making
Mingde Zhao, Tristan Sylvain, Doina Precup, Yoshua Bengio
Better Generalization RL Agent False Premise Hallucination Agent State Goal Directed Decision Hindsight Goal Relabeling

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

September 13, 2024

Applying Action Masking and Curriculum Learning Techniques to Improve Data Efficiency and Overall Performance in Operational Technology Cyber Security using Reinforcement Learning
Alec Wilson, William Holmes, Ryan Menzies, Kez Smithson Whitehead
Reinforcement Learning Reinforcement Learning Environment RL Agent Data Efficiency Action Masking

August 30, 2024

Using Quantum Solved Deep Boltzmann Machines to Increase the Data Efficiency of RL Agents
Daniel Kent, Clement O'Rourke, Jake Southall, Kirsty Duncan, Adrian Bedford
Quantum Machine Learning Data Efficient Boltzmann Machine RL Agent Data Efficiency Quantum Boltzmann Machine Deep Boltzmann

July 16, 2024

Graceful task adaptation with a bi-hemispheric RL agent
Grant Nicholas, Levin Kuhlmann, Gideon Kowadlo
Task Adaptation Novel Task RL Agent

June 3, 2024

Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy
Riqiang Gao, Florin C. Ghesu, Simon Arberet, Shahab Basiri, Esa Kuusela, Martin Kraus, Dorin Comaniciu, Ali Kamen
Deep Reinforcement Learning Multi Agent Reinforcement Learning Radiation Oncology Treatment Planning Plant Leaf RL Agent Sequence Optimization

May 6, 2024

Robot Air Hockey: A Manipulation Testbed for Robot Learning with Reinforcement Learning
Caleb Chuck, Carl Qi, Michael J. Munje, Shuozhe Li, Max Rudolph, Chang Shi, Siddhant Agarwal, Harshit Sikchi, Abhinav Peri, Sarthak Dayal, Evan Kuo, Kavan Mehta, Anthony Wang, Peter Stone, Amy Zhang, Scott Niekum
Reinforcement Learning Robot Learning RL Agent Interactive Reinforcement Learning Manipulation Domain Air Hockey

March 8, 2024

Reset & Distill: A Recipe for Overcoming Negative Transfer in Continual Reinforcement Learning
Hongjoon Ahn, Jinu Hyeon, Youngmin Oh, Bosun Hwang, Taesup Moon
Complete Recipe Continual Reinforcement Learning RL Agent Negative Transfer Global Reset Feature Plasticity Loss Task Engagement

February 8, 2024

Differentially Private Deep Model-Based Reinforcement Learning
Alexandre Rio, Merwan Barlier, Igor Colin, Albert Thomas
Offline Reinforcement Learning Differential Privacy Guarantee Private Model RL Agent Model Based Policy Private Reinforcement Learning

December 5, 2023

Lights out: training RL agents robust to temporary blindness
N. Ordonez, M. Tromp, P. M. Julbe, W. Böhmer
Loss Function Optimal Policy DQN Agent Hidden Representation RL Agent Blind User

July 18, 2023

IxDRL: A Novel Explainable Deep Reinforcement Learning Toolkit based on Analyses of Interestingness
Pedro Sequeira, Melinda Gervasio
Reinforcement Learning General Analysis User Interest Sequential Decision Making Task RL Agent Explainable Deep Reinforcement Learning

May 26, 2023

IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Reality
Bingjie Tang, Michael A. Lin, Iretiayo Akinola, Ankur Handa, Gaurav S. Sukhatme, Fabio Ramos, Dieter Fox, Yashraj Narang
Reinforcement Learning Robotics Domain Real World Simulation Study Assembly Task Robotic Assembly RL Agent

May 8, 2023

DEFENDER: DTW-Based Episode Filtering Using Demonstrations for Enhancing RL Safety
André Correia, Luís Alexandre
Noisy Demonstration Task Agnostic RL Agent Multiple Defender Unsupervised Episode

April 25, 2023

Centralized control for multi-agent RL in a complex Real-Time-Strategy game
Roger Creus Castanyer
Multi Agent Reinforcement Learning Learning Agent RL Agent Centralized Control Real Time Strategy Multi Agent RL Single Agent RL

April 8, 2023

Evolving Reinforcement Learning Environment to Minimize Learner's Achievable Reward: An Application on Hardening Active Directory Systems
Diksha Goel, Aneta Neumann, Frank Neumann, Hung Nguyen, Mingyu Guo
Reinforcement Learning Application Proficiency Attack Graph RL Agent Defense Strategy B Learner Evolutionary Diversity Security Hardening

April 3, 2023

A Multiagent CyberBattleSim for RL Cyber Operation Agents
Thomas Kunz, Christian Fisher, James La Novara-Gsell, Christopher Nguyen, Li Li
Reinforcement Learning RL Agent Defense Strategy Sophisticated Agent Autonomous Reinforcement Learning

November 11, 2022

Global and Local Analysis of Interestingness for Competency-Aware Deep Reinforcement Learning
Pedro Sequeira, Jesse Hostetler, Melinda Gervasio
Reinforcement Learning World Event Agent Behavior User Interest Sequential Decision Making Task RL Agent

August 30, 2022

Distributed Ensembles of Reinforcement Learning Agents for Electricity Control
Pierrick Pochelu, Serge G. Petiton, Bruno Conche
Deep Reinforcement Learning Diverse Ensemble Reinforcement Learning Agent Human Like RL RL Agent Novel Ensemble

August 17, 2022

A Framework for Understanding and Visualizing Strategies of RL Agents
Pedro Sequeira, Daniel Elenius, Jesse Hostetler, Melinda Gervasio
New Framework Human Understanding Reinforcement Learning Agent Agent Behavior Sequential Decision Making Task RL Agent Strategy Discovery Visualization Method Agent Strategy

March 28, 2022

REPTILE: A Proactive Real-Time Deep Reinforcement Learning Self-adaptive Framework
Flavio Corradini, Miichele Loreti, Marco Piangerelli, Giacomo Rocchetti
Markov Decision Process Time Varying Deep Reinforcement RL Agent