Hugging Face Blog
May 20, 2022
An Introduction to Q-Learning Part 1
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OpenAI Blog
Apr 21, 2017
Equivalence between policy gradients and soft Q-learning
arXiv AI
Jun 18
Pareto Q-Learning with Reward Machines
arXiv:2606. 19134v1 Announce Type: cross Abstract: We present Pareto Q-Learning with Reward Machines (PQLRM), a multi-objective reinforcement learning algorithm for tasks whose reward structure is specified by a set of reward machines (RMs).
arXiv AI
Jul 21
From Classical to Quantum Reinforcement Learning and Its Applications in Quantum Control: A Beginner's Tutorial
arXiv:2601. 08662v3 Announce Type: replace Abstract: This tutorial is designed to make reinforcement learning (RL) more accessible to undergraduate students by offering clear, example-driven explanations.
arXiv AI
Jun 26
Heavy-Ball Q-Learning with Residual Weighting Correction
arXiv:2606. 27112v1 Announce Type: cross Abstract: This paper proposes a corrected heavy-ball Q-learning method for reinforcement learning (RL) and establishes its convergence.
arXiv AI
Jul 10
Spectral Analysis of Dueling Q-Learning
arXiv:2607. 08340v1 Announce Type: cross Abstract: Q-learning is a fundamental algorithm in reinforcement learning (RL) for solving discounted Markov decision processes (MDPs) when the transition kernel is unknown.
OpenAI Blog
Jun 5, 2017
UCB exploration via Q-ensembles
Hugging Face Blog
May 4, 2022
An Introduction to Deep Reinforcement Learning
Hugging Face Blog
Jun 7, 2022
Deep Q-Learning with Space Invaders
arXiv AI
Jun 16
Deep Q-Learning on H\"older Spaces
arXiv:2606. 16846v1 Announce Type: cross Abstract: We study the operator-theoretic core of Q-learning in continuous-time stochastic control with continuous states and actions.
arXiv Machine Learning
Jul 7
A Switching System Theory of Q-Learning with Linear Function Approximation
arXiv:2605. 11021v3 Announce Type: replace Abstract: Q-learning is a fundamental algorithmic primitive in reinforcement learning.
arXiv Machine Learning
Jul 2
Quantum vs. Classical Machine Learning: A Unified Empirical Comparison
arXiv:2607. 01197v1 Announce Type: new Abstract: Quantum computing has emerged as a promising computational paradigm for machine learning (ML), with the potential to offer computational advantages over classical approaches.