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

August 31, 2022

Partial Counterfactual Identification for Infinite Horizon Partially Observable Markov Decision Process
Aditya Kelvianto Sidharta
Q Learning Observable Markov Decision Process Infinite Horizon Causal Query Counterfactual Query

August 26, 2022

Prospect Theory-inspired Automated P2P Energy Trading with Q-learning-based Dynamic Pricing
Ashutosh Timilsina, Simone Silvestri
Q Learning Energy Market Peer to Peer Energy Trading Energy Trading

August 2, 2022

Digital Twin-Assisted Efficient Reinforcement Learning for Edge Task Scheduling
Xiucheng Wang, Longfei Ma, Haocheng Li, Zhisheng Yin, Tom. Luan, Nan Cheng
Reinforcement Learning Digital Twin Q Learning Task Scheduling Asynchronous Q Learning Edge Server Scheduling

August 1, 2022

Mitigating Off-Policy Bias in Actor-Critic Methods with One-Step Q-learning: A Novel Correction Approach
Baturay Saglam, Dogan C. Cicek, Furkan B. Mutlu, Suleyman S. Kozat
Q Learning Policy OpTimization Actor Critic Model Free Deep Reinforcement Learning Policy Policy Gradient

July 25, 2022

July 22, 2022

Graph-Structured Policy Learning for Multi-Goal Manipulation Tasks
David Klee, Ondrej Biza, Robert Platt
Q Learning Robotic Manipulation Manipulation Task State Representation Multi Goal Reinforcement Learning

July 2, 2022

q-Learning in Continuous Time
Yanwei Jia, Xun Yu Zhou
Reinforcement Learning Policy Gradient Q Learning Continuous Time

June 30, 2022

On the Learning and Learnability of Quasimetrics
Tongzhou Wang, Phillip Isola
Reinforcement Learning LeArning Abstract Q Learning Quasimetric Learning

June 23, 2022

June 21, 2022

Federated Stochastic Approximation under Markov Noise and Heterogeneity: Applications in Reinforcement Learning
Sajad Khodadadian, Pranay Sharma, Gauri Joshi, Siva Theja Maguluri
Q Learning Federated Reinforcement Learning Linear Speedup Slow Convergence Markovian Sampling Federated Automatic Differentiation

June 20, 2022

MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer
Jeewon Jeon, Woojun Kim, Whiyoung Jung, Youngchul Sung
Multi Agent Reinforcement Learning Q Learning Sparse Reward Multiple Agent Intrinsic Reward Replay Buffer Sub Goal

June 12, 2022

June 7, 2022

Concentration bounds for SSP Q-learning for average cost MDPs
Shaan Ul Haque, Vivek Borkar
Q Learning Shortest Path Value Iteration Selective Focus Q$ Learning Average Reward Markov Decision Process

May 30, 2022

Designing Rewards for Fast Learning
Henry Sowerby, Zhiyuan Zhou, Michael L. Littman
Reinforcement Learning Q Learning Reward Function Fast Learning Reward Design

May 27, 2022

Deep Reinforcement Learning for Distributed and Uncoordinated Cognitive Radios Resource Allocation
Ankita Tondwalkar, Andres Kwasinski
Deep Reinforcement Learning Q Learning Resource Allocation Single Agent Reinforcement Learning Clean Distribution Cognitive Radio Learning Based Resource Allocation

May 26, 2022

Q Learning

Papers

Partial Counterfactual Identification for Infinite Horizon Partially Observable Markov Decision Process

Prospect Theory-inspired Automated P2P Energy Trading with Q-learning-based Dynamic Pricing

Digital Twin-Assisted Efficient Reinforcement Learning for Edge Task Scheduling

Mitigating Off-Policy Bias in Actor-Critic Methods with One-Step Q-learning: A Novel Correction Approach

Finite-Time Analysis of Asynchronous Q-learning under Diminishing Step-Size from Control-Theoretic View

Personalised Robot Behaviour Modelling for Robot-Assisted Therapy in the Context of Autism Spectrum Disorder

Graph-Structured Policy Learning for Multi-Goal Manipulation Tasks

q-Learning in Continuous Time

On the Learning and Learnability of Quasimetrics

Reinforcement Learning under Partial Observability Guided by Learned Environment Models

Recursive Reinforcement Learning

Federated Stochastic Approximation under Markov Noise and Heterogeneity: Applications in Reinforcement Learning

MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer

RL-GA: A Reinforcement Learning-Based Genetic Algorithm for Electromagnetic Detection Satellite Scheduling Problem

Reinforcement Learning for Vision-based Object Manipulation with Non-parametric Policy and Action Primitives

Concentration bounds for SSP Q-learning for average cost MDPs

Designing Rewards for Fast Learning

Deep Reinforcement Learning for Distributed and Uncoordinated Cognitive Radios Resource Allocation

Does DQN Learn?

Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment