Q Learning

Q-learning is a reinforcement learning algorithm aiming to find optimal actions in an environment by learning a Q-function that estimates the expected cumulative reward for each state-action pair. Current research focuses on improving Q-learning's robustness, efficiency, and applicability to complex scenarios, including multi-agent systems, partially observable environments (POMDPs), and those with corrupted rewards, often employing deep learning architectures like deep Q-networks (DQNs) and modifications such as double Q-learning and prioritized experience replay. These advancements are significant for addressing challenges in various fields, such as robotics, autonomous systems, and network optimization, where efficient and reliable decision-making under uncertainty is crucial.

Papers

May 23, 2023

When should we prefer Decision Transformers for Offline Reinforcement Learning?
Prajjwal Bhargava, Rohan Chitnis, Alborz Geramifard, Shagun Sodhani, Amy Zhang
Imitation Learning Offline Reinforcement Learning Q Learning Decision Transformer Conservative Q Learning Robotics Benchmark

May 18, 2023

The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond
Jiin Woo, Gauri Joshi, Yuejie Chi
Reinforcement Learning Q Learning Linear Speedup Tabular Markov Decision Process Blessing Generation

May 17, 2023

Model-Free Robust Average-Reward Reinforcement Learning
Yue Wang, Alvaro Velasquez, George Atia, Ashley Prater-Bennette, Shaofeng Zou
Q Learning Model Free Value Iteration Robust Reward

May 14, 2023

Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning
Mina Razghandi, Hao Zhou, Melike Erol-Kantarci, Damla Turgut
Q Learning Smart Environment Synthetic Data Generator Adversarial AutoEncoder Home Energy Management

May 11, 2023

On Practical Robust Reinforcement Learning: Practical Uncertainty Set and Double-Agent Algorithm
Ukjo Hwang, Songnam Hong
Markov Decision Process Q Learning Robust Policy Robust Reinforcement Learning Uncertainty Set

May 2, 2023

Mixed-Integer Optimal Control via Reinforcement Learning: A Case Study on Hybrid Electric Vehicle Energy Management
Jinming Xu, Nasser Lashgarian Azad, Yuan Lin
Reinforcement Learning Case Study Q Learning Mixed Integer Tractor Trailer Vehicle Hybrid Action Reinforcement Learning

April 25, 2023

April 19, 2023

Integrated Ray-Tracing and Coverage Planning Control using Reinforcement Learning
Savvas Papaioannou, Panayiotis Kolios, Theocharis Theocharides, Christos G. Panayiotou, Marios M. Polycarpou
Reinforcement Learning Q Learning Ray Tracing

April 17, 2023

A study on a Q-Learning algorithm application to a manufacturing assembly problem
Miguel Neves, Miguel Vieira, Pedro Neto
Reinforcement Learning Practical Algorithm Study Feature Q Learning Reinforcement Learning Algorithm Assembly Task

April 13, 2023

Deep reinforcement learning applied to an assembly sequence planning problem with user preferences
Miguel Neves, Pedro Neto
Deep Reinforcement Learning Q Learning Deep Q Learning User Preference Assembly Planning Tabular Q Learning Reinforcement Learning Performance

March 31, 2023

Understanding Reinforcement Learning Algorithms: The Progress from Basic Q-learning to Proximal Policy Optimization
Mohamed-Amine Chadi, Hajar Mousannif
Reinforcement Learning Artificial Intelligence Q Learning Proximal Policy Optimization Reinforcement Learning Algorithm Much Progress State of the Art Algorithm

March 30, 2023

Q-Learning based system for path planning with unmanned aerial vehicles swarms in obstacle environments
Alejandro Puente-Castro, Daniel Rivero, Eurico Pedrosa, Artur Pereira, Nuno Lau, Enrique Fernandez-Blanco
Reinforcement Learning Unmanned Aerial Vehicle System Description Q Learning Path Planning UAV Swarm Obstacle Rich Environment Autonomous Control Path Planning Method

March 24, 2023

Robust Path Following on Rivers Using Bootstrapped Reinforcement Learning
Niklas Paulig, Ostap Okhrin
Deep Reinforcement Learning Q Learning Autonomous Surface Vessel River Sandbank High Precision Navigation PID Controller Bootstrapped Deep Q Learning

March 16, 2023

Self-Inspection Method of Unmanned Aerial Vehicles in Power Plants Using Deep Q-Network Reinforcement Learning
Haoran Guan
Reinforcement Learning Unmanned Aerial Vehicle Q Learning Deep Q Network Deep Q Self Checking

March 15, 2023

Smoothed Q-learning
David Barber
Reinforcement Learning Q Learning Double Q Learning

March 10, 2023

Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning
Xiucheng Wang, Nan Cheng, Longfei Ma, Ruijin Sun, Rong Chai, Ning Lu
Knowledge Distillation Q Learning Heterogeneous Federated Learning Model Distillation Training Framework

March 9, 2023

March 4, 2023

Wasserstein Actor-Critic: Directed Exploration via Optimism for Continuous-Actions Control
Amarildo Likmeta, Matteo Sacco, Alberto Maria Metelli, Marcello Restelli
Reinforcement Learning Q Learning Epistemic Uncertainty Distributional Reinforcement Learning Wasserstein Dictionary Directed Exploration Continuous Action

Q Learning

Papers

When should we prefer Decision Transformers for Offline Reinforcement Learning?

The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond

Model-Free Robust Average-Reward Reinforcement Learning

Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning

On Practical Robust Reinforcement Learning: Practical Uncertainty Set and Double-Agent Algorithm

Mixed-Integer Optimal Control via Reinforcement Learning: A Case Study on Hybrid Electric Vehicle Energy Management

Adaptive Services Function Chain Orchestration For Digital Health Twin Use Cases: Heuristic-boosted Q-Learning Approach

Q-learning with biased policy rules

Integrated Ray-Tracing and Coverage Planning Control using Reinforcement Learning

A study on a Q-Learning algorithm application to a manufacturing assembly problem

Deep reinforcement learning applied to an assembly sequence planning problem with user preferences

Understanding Reinforcement Learning Algorithms: The Progress from Basic Q-learning to Proximal Policy Optimization

Q-Learning based system for path planning with unmanned aerial vehicles swarms in obstacle environments

Robust Path Following on Rivers Using Bootstrapped Reinforcement Learning

Self-Inspection Method of Unmanned Aerial Vehicles in Power Plants Using Deep Q-Network Reinforcement Learning

Smoothed Q-learning

Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning

Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning

Learning Strategic Value and Cooperation in Multi-Player Stochastic Games through Side Payments

Wasserstein Actor-Critic: Directed Exploration via Optimism for Continuous-Actions Control