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

August 5, 2022

Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter
Aleksandar Stanić, Yujin Tang, David Ha, Jürgen Schmidhuber
LeArning Abstract Reinforcement Learning Agent Object Centric Transformer Feed Forward Layer

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 15, 2022

Outcome-Guided Counterfactuals for Reinforcement Learning Agents from a Jointly Trained Generative Latent Space
Eric Yeh, Pedro Sequeira, Jesse Hostetler, Melinda Gervasio
Reinforcement Learning Latent Space Variational Autoencoder Reinforcement Learning Agent Counterfactual Example Consistent Counterfactuals Latent Space Interpolation

July 8, 2022

Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agents
Nathaniel Hamilton, Kyle Dunlap, Taylor T Johnson, Kerianne L Hobbs
Reinforcement Learning Training Data System Performance Safe Reinforcement Learning Reinforcement Learning Agent Policy Reinforcement Learning Safe Exploration Ablation Study

July 5, 2022

Decentralized scheduling through an adaptive, trading-based multi-agent system
Michael Kölle, Lennart Rietdorf, Kyrill Schmid
Multi Agent Reinforcement Learning Multi Agent System Reinforcement Learning Agent Parameter Sharing Agent Architecture

June 27, 2022

EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System
Haoran Su, Yaofeng D. Zhong, Joseph Y. J. Chow, Biswadip Dey, Li Jin
Reinforcement Learning Agent Traffic Signal Control Decentralized Reinforcement Learning

June 24, 2022

Value Function Decomposition for Iterative Design of Reinforcement Learning Agents
James MacGlashan, Evan Archer, Alisa Devlic, Takuma Seno, Craig Sherstan, Peter R. Wurman, Peter Stone
Reinforcement Learning Reinforcement Learning Agent Actor Critic Algorithm Agent Capability Decision Making Agent Value Decomposition Iterative Design Value Function Decomposition

June 15, 2022

A Search-Based Testing Approach for Deep Reinforcement Learning Agents
Amirhossein Zolfagharian, Manel Abdellatif, Lionel Briand, Mojtaba Bagherzadeh, Ramesh S
Deep Reinforcement Learning Reinforcement Learning Agent Search Based Deep Q

May 27, 2022

Personalized Algorithmic Recourse with Preference Elicitation
Giovanni De Toni, Paolo Viappiani, Stefano Teso, Bruno Lepri, Andrea Passerini
Monte Carlo Tree Search Reinforcement Learning Agent Human in the Loop Algorithmic Recourse Preference Elicitation

May 20, 2022

Prototyping three key properties of specific curiosity in computational reinforcement learning
Nadia M. Ady, Roshan Shariff, Johannes Günther, Patrick M. Pilarski
Reinforcement Learning Agent Decision Making Agent Curiosity Inducing Situation Fast Prototyping

May 12, 2022

Asking for Knowledge: Training RL Agents to Query External Knowledge Using Language
Iou-Jen Liu, Xingdi Yuan, Marc-Alexandre Côté, Pierre-Yves Oudeyer, Alexander G. Schwing
Knowledge Based Reinforcement Learning Agent External Knowledge AI Challenge Episodic Bonus

May 10, 2022

VesNet-RL: Simulation-based Reinforcement Learning for Real-World US Probe Navigation
Yuan Bi, Zhongliang Jiang, Yuan Gao, Thomas Wendler, Angelos Karlas, Nassir Navab
Reinforcement Learning Deep Reinforcement Learning Reinforcement Learning Agent Robotic Ultrasound Vessel Detection Carotid Artery

May 7, 2022

Search-Based Testing of Reinforcement Learning
Martin Tappler, Filip Cano Córdoba, Bernhard K. Aichernig, Bettina Könighofer
Reinforcement Learning Deep Reinforcement Learning Reinforcement Learning Agent Reinforcement Learning Task Deep Reinforcement Learning Agent Search Based Robust Test

May 6, 2022

Dynamically writing coupled memories using a reinforcement learning agent, meeting physical bounds
Théo Jules, Laura Michel, Adèle Douin, Frédéric Lechenault
Memory Trace Reinforcement Learning Agent General Bound Personal Memory Memory Capacity Adiabatic Quantum

April 9, 2022

Hardware Trojan Insertion Using Reinforcement Learning
Amin Sarihi, Ahmad Patooghy, Peter Jamieson, Abdel-Hameed A. Badawy
Reinforcement Learning Reinforcement Learning Agent Hardware Trojan Benchmark Circuit

April 4, 2022

Reinforcement Learning Agents in Colonel Blotto
Joseph Christian G. Noel
Reinforcement Learning Reinforcement Learning Agent Agent Based Model Artificial Agent Optimal Strategy

March 17, 2022

The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents
Patrick M. Pilarski, Andrew Butcher, Elnaz Davoodi, Michael Bradley Johanson, Dylan J. A. Brenneis, Adam S. R. Parker, Leslie Acker, Matthew M. Botvinick, Joseph Modayil, Adam White
Agent Smith Timely Communication Sparse Reward Reinforcement Learning Agent Prior Coordination Communication Learning

February 23, 2022

Drawing Inductor Layout with a Reinforcement Learning Agent: Method and Application for VCO Inductors
Cameron Haigh, Zichen Zhang, Negar Hassanpour, Khurram Javed, Yingying Fu, Shayan Shahramian, Shawn Zhang, Jun Luo
Reinforcement Learning Application Proficiency Practical Method Reinforcement Learning Agent

January 24, 2022

The Paradox of Choice: Using Attention in Hierarchical Reinforcement Learning
Andrei Nica, Khimya Khetarpal, Doina Precup
Human Attention Affordance Learning Reinforcement Learning Agent Hierarchical Reinforcement Learning Long Horizon Task Value Laden Choice Decision Making Agent Fermi Paradox

January 11, 2022

Automated Reinforcement Learning (AutoRL): A Survey and Open Problems
Jack Parker-Holder, Raghu Rajan, Xingyou Song, André Biedenkapp, Yingjie Miao, Theresa Eimer, Baohe Zhang, Vu Nguyen, Roberto Calandra, Aleksandra Faust, Frank Hutter, Marius Lindauer
Reinforcement Learning Timely Survey Reinforcement Learning Agent Open Problem

Reinforcement Learning Agent

Papers

Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter

Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks

Outcome-Guided Counterfactuals for Reinforcement Learning Agents from a Jointly Trained Generative Latent Space

Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agents

Decentralized scheduling through an adaptive, trading-based multi-agent system

EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System

Value Function Decomposition for Iterative Design of Reinforcement Learning Agents

A Search-Based Testing Approach for Deep Reinforcement Learning Agents

Personalized Algorithmic Recourse with Preference Elicitation

Prototyping three key properties of specific curiosity in computational reinforcement learning

Asking for Knowledge: Training RL Agents to Query External Knowledge Using Language

VesNet-RL: Simulation-based Reinforcement Learning for Real-World US Probe Navigation

Search-Based Testing of Reinforcement Learning

Dynamically writing coupled memories using a reinforcement learning agent, meeting physical bounds

Hardware Trojan Insertion Using Reinforcement Learning

Reinforcement Learning Agents in Colonel Blotto

The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents

Drawing Inductor Layout with a Reinforcement Learning Agent: Method and Application for VCO Inductors

The Paradox of Choice: Using Attention in Hierarchical Reinforcement Learning

Automated Reinforcement Learning (AutoRL): A Survey and Open Problems