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

November 19, 2023

Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets
Kun Yang, Cong Shen, Jing Yang, Shu-ping Yeh, Jerry Sydir
Reinforcement Learning Offline Reinforcement Learning Q Learning Offline Reinforcement Learning Algorithm Optimal Reinforcement Learning Wireless Network Optimization Dataset Mixture

October 28, 2023

Weakly Coupled Deep Q-Networks
Ibrahim El Shar, Daniel R. Jiang
Deep Reinforcement Learning Q Learning Deep Q Network DQN Agent Double DQN

October 27, 2023

October 24, 2023

On the Convergence and Sample Complexity Analysis of Deep Q-Networks with $\epsilon$-Greedy Exploration
Shuai Zhang, Hongkang Li, Meng Wang, Miao Liu, Pin-Yu Chen, Songtao Lu, Sijia Liu, Keerthiram Murugesan, Subhajit Chaudhury
Deep Reinforcement Learning Sample Complexity Early Stage Convergence Q Learning Deep Q Network Greedy Exploration

October 10, 2023

October 3, 2023

Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation
Guojun Xiong, Jian Li
Multi Armed Bandit Q Learning Finite Time Restless Multi Armed Bandit Index Policy Dynamic Restless

October 2, 2023

Using Reinforcement Learning to Optimize Responses in Care Processes: A Case Study on Aggression Incidents
Bart J. Verhoef, Xixi Lu
Reinforcement Learning Case Study Markov Decision Process Q Learning Optimal Policy Communal Violence Healthcare Workflow High Performing Policy Response Evaluation

September 18, 2023

Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions
Yevgen Chebotar, Quan Vuong, Alex Irpan, Karol Hausman, Fei Xia, Yao Lu, Aviral Kumar, Tianhe Yu, Alexander Herzog, Karl Pertsch, Keerthana Gopalakrishnan, Julian Ibarz, Ofir Nachum, Sumedh Sontakke, Grecia Salazar, Huong T Tran, Jodilyn Peralta, Clayton Tan, Deeksha Manjunath, Jaspiar Singht, Brianna Zitkovich, Tomas Jackson, Kanishka Rao, Chelsea Finn, Sergey Levine
Reinforcement Learning Offline Reinforcement Learning Q Learning Q Function Task Specific Policy Q Transformer

September 13, 2023

Dynamic control of self-assembly of quasicrystalline structures through reinforcement learning
Uyen Tu Lieu, Natsuhiko Yoshinaga
Reinforcement Learning Q Learning Dynamic Control Large Scale Structure Self Assembly

September 12, 2023

September 11, 2023

Career Path Recommendations for Long-term Income Maximization: A Reinforcement Learning Approach
Spyros Avlonitis, Dor Lavi, Masoud Mansoury, David Graus
Q Learning Optimal Policy Career Counseling

September 10, 2023

Convex Q Learning in a Stochastic Environment: Extended Version
Fan Lu, Sean Meyn
Markov Decision Process Q Learning Convex Optimization Extended Version Stochastic Environment Convex Q Learning

September 7, 2023

Navigation Through Endoluminal Channels Using Q-Learning
Oded Medina, Liora Kleinburd, Nir Shvalb
Q Learning Reinforcement Learning Algorithm Local Navigation

August 24, 2023

Actuator Trajectory Planning for UAVs with Overhead Manipulator using Reinforcement Learning
Hazim Alzorgan, Abolfazl Razi, Ata Jahangir Moshayedi
Reinforcement Learning Unmanned Aerial Vehicle Q Learning Trajectory Planning Various Fast Moving Drone Aerial Manipulator Unmanned Aerial

August 17, 2023

Reinforcement Learning for Battery Management in Dairy Farming
Nawazish Ali, Abdul Wahid, Rachael shaw, Karl Mason
Reinforcement Learning Q Learning Renewable Generation Dairy Farming Battery Management

August 10, 2023

A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control
Marshall Wang, John Willes, Thomas Jiralerspong, Matin Moezzi
Reinforcement Learning Consistent Comparison Q Learning Heating, Ventilation, and Air Conditioning

July 22, 2023

A Flexible Framework for Incorporating Patient Preferences Into Q-Learning
Joshua P. Zitovsky, Leslie Wilson, Michael R. Kosorok
Q Learning Preference Feedback Multiple Outcome Dynamic Treatment Regime Utility Learning

Q Learning

Papers

Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets

Weakly Coupled Deep Q-Networks

Model-free Posterior Sampling via Learning Rate Randomization

Lifting the Veil: Unlocking the Power of Depth in Q-learning

On the Convergence and Sample Complexity Analysis of Deep Q-Networks with $\epsilon$-Greedy Exploration

Boosting Continuous Control with Consistency Policy

Suppressing Overestimation in Q-Learning through Adversarial Behaviors

Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation

Using Reinforcement Learning to Optimize Responses in Care Processes: A Case Study on Aggression Incidents

Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions

Dynamic control of self-assembly of quasicrystalline structures through reinforcement learning

Reasoning with Latent Diffusion in Offline Reinforcement Learning

A Q-learning Approach for Adherence-Aware Recommendations

Career Path Recommendations for Long-term Income Maximization: A Reinforcement Learning Approach

Convex Q Learning in a Stochastic Environment: Extended Version

Navigation Through Endoluminal Channels Using Q-Learning

Actuator Trajectory Planning for UAVs with Overhead Manipulator using Reinforcement Learning

Reinforcement Learning for Battery Management in Dairy Farming

A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control

A Flexible Framework for Incorporating Patient Preferences Into Q-Learning