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

December 1, 2022

Five Properties of Specific Curiosity You Didn't Know Curious Machines Should Have
Nadia M. Ady, Roshan Shariff, Johannes Günther, Patrick M. Pilarski
Reinforcement Learning Reinforcement Learning Agent Many Property Curiosity Inducing Situation Artificial Curiosity Creative Machine

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

October 21, 2022

Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents
Yael Septon, Tobias Huber, Elisabeth André, Ofra Amir
Reinforcement Learning Reinforcement Learning Agent Based Policy Global Explanation rEward Decomposition

October 7, 2022

Advice Conformance Verification by Reinforcement Learning agents for Human-in-the-Loop
Mudit Verma, Ayush Kharkwal, Subbarao Kambhampati
Reinforcement Learning Reinforcement Learning Agent Human in the Loop Multi Agent Sequential Decision Advice Quality Conformance Checking

September 20, 2022

Towards Task-Prioritized Policy Composition
Finn Rietz, Erik Schaffernicht, Todor Stoyanov, Johannes A. Stork
Reinforcement Learning Reinforcement Learning Agent

September 8, 2022

FORLORN: A Framework for Comparing Offline Methods and Reinforcement Learning for Optimization of RAN Parameters
Vegard Edvardsen, Gard Spreemann, Jeriek Van den Abeele
Reinforcement Learning Optimization Purpose Reinforcement Learning Agent Mobile Network Radio Access Network Offline Evaluation

September 5, 2022

Red Teaming with Mind Reading: White-Box Adversarial Policies Against RL Agents
Stephen Casper, Taylor Killian, Gabriel Kreiman, Dylan Hadfield-Menell
Adversarial Example Reinforcement Learning Agent Red Teaming Adversarial Agent Adversarial Policy White Box Adversarial

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 26, 2022

DETERRENT: Detecting Trojans using Reinforcement Learning
Vasudev Gohil, Satwik Patnaik, Hao Guo, Dileep Kalathil, Jeyavijayan, Rajendran
Reinforcement Learning Reinforcement Learning Agent Trojan Detection Hardware Trojan Code Simulation

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

August 12, 2022

RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents
Rafael Rodriguez-Sanchez, Benjamin A. Spiegel, Jennifer Wang, Roma Patel, Stefanie Tellex, George Konidaris
Markov Decision Process Policy Gradient Domain Specific Domain Knowledge Reinforcement Learning Agent Declarative Statement

August 8, 2022

Continual Reinforcement Learning with TELLA
Neil Fendley, Cash Costello, Eric Nguyen, Gino Perrotta, Corey Lowman
Continual Learning Reinforcement Learning Agent Lifelong Learning Continual Reinforcement Learning Lifelong Machine Learning Learning Curriculum

Reinforcement Learning Agent

Papers

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

Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents

Advice Conformance Verification by Reinforcement Learning agents for Human-in-the-Loop

Towards Task-Prioritized Policy Composition

FORLORN: A Framework for Comparing Offline Methods and Reinforcement Learning for Optimization of RAN Parameters

Red Teaming with Mind Reading: White-Box Adversarial Policies Against RL Agents

Distributed Ensembles of Reinforcement Learning Agents for Electricity Control

DETERRENT: Detecting Trojans using Reinforcement Learning

A Framework for Understanding and Visualizing Strategies of RL Agents

RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents

Continual Reinforcement Learning with TELLA