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

April 2, 2023

Experimentation Platforms Meet Reinforcement Learning: Bayesian Sequential Decision-Making for Continuous Monitoring
Runzhe Wan, Yu Liu, James McQueen, Doug Hains, Rui Song
Reinforcement Learning Reinforcement Learning Agent Hypothesis Testing Meta Analysis Optimal Decision Rule

March 2, 2023

Domain Adaptation of Reinforcement Learning Agents based on Network Service Proximity
Kaushik Dey, Satheesh K. Perepu, Pallab Dasgupta, Abir Das
Reinforcement Learning Domain Adaptation Reinforcement Learning Agent Proximity Search Communication Network

February 24, 2023

GANterfactual-RL: Understanding Reinforcement Learning Agents' Strategies through Visual Counterfactual Explanations
Tobias Huber, Maximilian Demmler, Silvan Mertes, Matthew L. Olson, Elisabeth André
Reinforcement Learning Counterfactual Explanation Artificial Intelligence Model General Strategy Reinforcement Learning Agent Adversarial Method Reinforcement Learning Model Visual Counterfactual Explanation

February 13, 2023

Universal Agent Mixtures and the Geometry of Intelligence
Samuel Allen Alexander, David Quarel, Len Du, Marcus Hutter
Multi Agent Reinforcement Learning Geometric Analysis Reinforcement Learning Agent Cognitive Intelligence

January 30, 2023

Transferring Multiple Policies to Hotstart Reinforcement Learning in an Air Compressor Management Problem
Hélène Plisnier, Denis Steckelmacher, Jeroen Willems, Bruno Depraetere, Ann Nowé
Reinforcement Learning Reinforcement Learning Agent Reward Shaping Diverse Policy Industrial Machine Policy Alignment Compressor Based Machine

January 28, 2023

Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling
Kolby Nottingham, Prithviraj Ammanabrolu, Alane Suhr, Yejin Choi, Hannaneh Hajishirzi, Sameer Singh, Roy Fox
Reinforcement Learning Decision Making Environment Exploration World Model Reinforcement Learning Agent Electric SHEEP Agent DREAM

January 24, 2023

Story Shaping: Teaching Agents Human-like Behavior with Stories
Xiangyu Peng, Christopher Cui, Wei Zhou, Renee Jia, Mark Riedl
Reinforcement Learning Agent BEHAVIOR Explanation Intrinsic Reward Written Story Reward Design Pedagogical Agent Narrative Designer Tacit Knowledge

December 15, 2022

Emergent Behaviors in Multi-Agent Target Acquisition
Piyush K. Sharma, Erin Zaroukian, Derrik E. Asher, Bryson Howell
Reinforcement Learning Multi Agent Multi Agent System Reinforcement Learning Agent Emergent Behavior

December 6, 2022

Reinforcement Learning for UAV control with Policy and Reward Shaping
Cristian Millán-Arias, Ruben Contreras, Francisco Cruz, Bruno Fernandes
Reinforcement Learning Unmanned Aerial Vehicle Autonomous Agent Reinforcement Learning Agent Reward Shaping Health Policy Recommendation Drone Control Interactive Reinforcement Learning

December 1, 2022

November 30, 2022

Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules
Loris Di Natale, Bratislav Svetozarevic, Philipp Heer, Colin N. Jones
Reinforcement Learning Agent Actor Critic Efficient Reinforcement Learning Active Exploration Simple RULE State Action Space Artificial Intelligence Agent

November 23, 2022

Reinforcement Learning Agent Design and Optimization with Bandwidth Allocation Model
Rafael F. Reale, Joberto S. B. Martins
Reinforcement Learning Optimization Purpose Reinforcement Learning Agent Agent Capability Bandwidth Allocation

November 21, 2022

Examining Policy Entropy of Reinforcement Learning Agents for Personalization Tasks
Anton Dereventsov, Andrew Starnes, Clayton G. Webster
Reinforcement Learning Agent Reinforcement Learning System Personalization Task Policy Entropy

November 19, 2022

ReInform: Selecting paths with reinforcement learning for contextualized link prediction
Marina Speranskaya, Sameh Methias, Benjamin Roth
Reinforcement Learning Human Prediction Link Prediction Reinforcement Learning Agent Path Finding

November 6, 2022

ProtoX: Explaining a Reinforcement Learning Agent via Prototyping
Ronilo J. Ragodos, Tong Wang, Qihang Lin, Xun Zhou
Deep Reinforcement Learning Behavior Cloning Reinforcement Learning Agent Fast Prototyping

November 2, 2022

Over-communicate no more: Situated RL agents learn concise communication protocols
Aleksandra Kalinowska, Elnaz Davoodi, Florian Strub, Kory W Mathewson, Ivana Kajic, Michael Bowling, Todd D Murphey, Patrick M Pilarski
Path Breaking Emergence Reinforcement Learning Agent Artificial Agent Emergent Communication Situated Reasoning Communication Modality Communication Protocol Information Overload

October 29, 2022

DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous Driving
Resul Dagdanov, Feyza Eksen, Halil Durmus, Ferhat Yurdakul, Nazim Kemal Ure
Reinforcement Learning Data Detection Imitation Learning Reinforcement Learning Agent Reliable Autonomous Driving Future Failure Autonomous Driving Benchmark

October 24, 2022

MEET: A Monte Carlo Exploration-Exploitation Trade-off for Buffer Sampling
Julius Ott, Lorenzo Servadei, Jose Arjona-Medina, Enrico Rinaldi, Gianfranco Mauro, Daniela Sánchez Lopera, Michael Stephan, Thomas Stadelmayer, Avik Santra, Robert Wille
Reinforcement Learning Reinforcement Learning Agent Data Sampling Exploration Exploitation Trade Bentonite Buffer Q Value

October 23, 2022

Climate Change Policy Exploration using Reinforcement Learning
Theodore Wolf
Reinforcement Learning Deep Reinforcement Learning Reinforcement Learning Agent Earth System Model Climate Policy

Reinforcement Learning Agent

Papers

Experimentation Platforms Meet Reinforcement Learning: Bayesian Sequential Decision-Making for Continuous Monitoring

Domain Adaptation of Reinforcement Learning Agents based on Network Service Proximity

GANterfactual-RL: Understanding Reinforcement Learning Agents' Strategies through Visual Counterfactual Explanations

Universal Agent Mixtures and the Geometry of Intelligence

Transferring Multiple Policies to Hotstart Reinforcement Learning in an Air Compressor Management Problem

Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling

Story Shaping: Teaching Agents Human-like Behavior with Stories

Emergent Behaviors in Multi-Agent Target Acquisition

Reinforcement Learning for UAV control with Policy and Reward Shaping

Modeling Mobile Health Users as Reinforcement Learning Agents

Five Properties of Specific Curiosity You Didn't Know Curious Machines Should Have

Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules

Reinforcement Learning Agent Design and Optimization with Bandwidth Allocation Model

Examining Policy Entropy of Reinforcement Learning Agents for Personalization Tasks

ReInform: Selecting paths with reinforcement learning for contextualized link prediction

ProtoX: Explaining a Reinforcement Learning Agent via Prototyping

Over-communicate no more: Situated RL agents learn concise communication protocols

DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous Driving

MEET: A Monte Carlo Exploration-Exploitation Trade-off for Buffer Sampling

Climate Change Policy Exploration using Reinforcement Learning