Deep Q-Learning with Space Invaders

An Introduction to Q-Learning Part 1

A Novel XAI-Enhanced Quantum Adversarial Networks for Velocity Dispersion Modeling in MaNGA Galaxies

arXiv:2510. 24598v2 Announce Type: replace Abstract: Current quantum machine learning approaches often face challenges balancing predictive accuracy, robustness, and interpretability.

An Introduction to Q-Learning Part 2/2

Enhancing Q-Value Updates in Deep Q-Learning via Successor-State Prediction

arXiv:2511. 03836v2 Announce Type: replace Abstract: Deep Q-Networks (DQNs) estimate future returns by learning from transitions sampled from a replay buffer.

Direct Advantage Estimation for Scalable and Sample-efficient Deep Reinforcement Learning

arXiv:2606. 20411v1 Announce Type: new Abstract: Direct Advantage Estimation (DAE) has been shown to improve the sample efficiency of deep reinforcement learning algorithms.

Discovering Interpretable Multi-Parameter Control Policies for Evolutionary Algorithms Using Deep Reinforcement Learning

arXiv:2606. 10129v1 Announce Type: new Abstract: While deep Reinforcement Learning (deep-RL) has been increasingly applied to parameter control in evolutionary algorithms, rigorous theoretical analysis of parameter control remains largely restricted to single-parameter settings, owing to the difficulty of deriving effective, interpretable multi-parameter policies amenable to formal study.

UCB exploration via Q-ensembles

Comparing Classical and Quantum Machine Learning for Regression in High Energy Physics Collision Data

The paper systematically compares four classical machine learning models—SVM, ANN, CNN, and LSTM—with their quantum equivalents—QSVM, QNN, QCNN, and QLSTM—on simulated proton‑proton collision data from CERN Open Data. Classical models, especially CNN and LSTM, slightly outperform the quantum models under current hardware and dataset constraints, but quantum models achieve comparable accuracy with far fewer trainable parameters; for example, the QCNN matches a deep classical CNN using only four qubits and a depth‑three circuit. The study also shows that the regression task is non‑trivial for shallow polynomial fits, underscoring the relevance of the architectural comparison. whyItMatters":"The work provides a realistic benchmark of classical versus quantum machine learning performance on high‑energy physics data, highlighting parameter‑efficiency advantages of quantum models for near‑term devices."

Understanding and Stabilizing Deep Q-Learning via Controlled Bootstrapping and Regulated Value Dynamics

arXiv:2608. 16182v1 Announce Type: cross Abstract: Deep Q-learning (DQL) has achieved remarkable empirical success in reinforcement learning, yet its training process remains notoriously unstable.

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.

Aftab: A Comprehensive Benchmark of CNN Encoders and Advanced Value Functions in Parallelized Q-Networks

arXiv:2608. 07335v1 Announce Type: cross Abstract: Recent advancements in deep reinforcement learning have increasingly favored simplified, highly parallelized paradigms.

Accelerating Q-learning through Efficient Value-Sharing across Actions

arXiv:2606. 29806v1 Announce Type: cross Abstract: Action-values are foundational to many control algorithms such as Q-learning.