Deep Q-Learning

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Deep Q-learning uses neural networks to estimate how valuable each action is in a state, then improves those estimates from rewards and later outcomes. Common approaches include replay buffers and target networks for stable training, Double and Dueling DQN to reduce value-estimation errors, and distributional or ensemble methods to model uncertainty. The methods are tested on benchmarks such as Atari and control simulators, and applied to settings including robotics, traffic scheduling, and network management. A central tension is that bootstrapping and correlated experience can make learning unstable, while methods that improve robustness, safety, or adaptation may trade off against sample efficiency and peak performance.

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45 papers7 papers in the last four weeks, up 133% on the four weeks before. 0.0% of all new papers.

Bayesian Neural Networks via Dirichlet Process Priors on the Data-Generating Distribution for Randomized Value Functions in Deep Reinforcement Learning
Oct 20, 2026Sumit VashishthaBayesian RL Uncertainty Estimation in RL
Independent Researcher
Deep Reinforcement Learning for HPC Job Scheduling: A Statistical Evaluation
Oct 14, 2026Justin M. Cheney, Clement N. NyirendaReinforcement Learning High-Performance Computing
Computer Science Department, University of the Western Cape, Robert Sobukwe Rd, Bellville, Cape Town, 7535, South Africa · eResearch Office, University of the Western Cape, Bellville, Cape Town, South Africa
Selective Growing Q-Networks (SGQN): An Analysis of Adaptive Action Grids in Decoupled Q-Learning
Oct 14, 2026Parham Nooranbakht, Seyed Mahdi Basiri Azad, Joschka BödeckerReinforcement Learning Q-Learning
Faculty of Engineering, Department of Computer Science Albert-Ludwigs-Universität Freiburg, Germany
When Does Opponent Modeling Pay Off? Spatial Inference Stabilises Reinforcement Learning under Partial Observability
Oct 13, 2026Marcel Petran, Stefan EdelkampOpponent Modeling Multi-Agent Reinforcement Learning
Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic
Common-Mode Errors Limit Low-Timestep Deep Spiking Q-Networks
Oct 6, 2026Zijie Xu, Bingrui Guo, Yiding Sun +3Deep Q-Learning Temporal-Difference Learning
Peking University Beijing, 100871, China
Ramp Metering Control via Hybrid State Deep Reinforcement Learning in Partially Observable Connected Vehicle Environments
Oct 5, 2026Youcef Mehamlia, Nadir Farhi, Meriem BoualiRL Control Deep Q-Learning
Cosys-Grettia, Univ Gustave Eiffel, F-77454 Marne-la-Vallee, France. · Laboratoire LITAN, ESTIN, Amizour, Algeria.
Mixture-of-Experts for Cryptocurrency Order Execution: Training Stability, Tail Risk, and Failure Modes
Oct 2, 2026Alexander Ardaiz, Varun Budati, Ali HabibniaReinforcement Learning Algorithmic Trading
Virginia Tech, Blacksburg, Virginia, USA · Dataism Laboratory for Quantitative Finance, Blacksburg, Virginia, USA
Adaptive Chemotherapy Control under Tumor Heterogeneity via Reinforcement Learning
Sep 10, 2026Bereket Sitotaw Kidane, Md Samiul Haque Motayed, Shuo WangRL Control Reinforcement Learning
Department of Mechanical and Aerospace Engineering, University of Texas at Arlington, Arlington, TX 76019, USA
Generative Replay Mitigates Sample Starvation in Quantum Architecture Search
Sep 10, 2026Akash Kundu, Amit Kumar Jaiswal, Sebastian Feld +1Quantum Architecture Search Generative Replay
Delft University of Technology, Delft, The Netherlands. · Quantum Computing Division, QuTech, Delft, The Netherlands. · Indian Institute of Technology (BHU), Varanasi, India. +1
Mini-Batch Risk-Averse Deep Q-Learning: A Robot Navigation Case Study
Sep 7, 2026Aayush Patel, Andrzej RuszczyńskiDeep Q-Learning Robot Navigation
Department of Management Science and Information Systems, Rutgers University
Understanding and Stabilizing Deep Q-Learning via Controlled Bootstrapping and Regulated Value Dynamics
Aug 17, 2026Bozhou Chen, Yongyi Wang, Hanyu Liu +2RL Stability Reinforcement Learning
School of Computer Science, Peking University, Beijing, China
Revisiting Overestimation Bias Problem of Q-learning: Settling Large Discrete Action Space via Action Intersection
Aug 13, 2026Pu Li, Tao Tan, Hong Xie +2Reinforcement Learning Deep Q-Learning
Chongqing Institute of Green and Intelligent Technology, Chongqing, China · University of Science and Technology of China, Hefei, Anhui, China
Machine Learning-Based Cyber Defense for Cloud Infrastructure: An Adaptive Deep Q-Network Architecture for Intelligent Intrusion Detection and Automated Threat Mitigation
Aug 12, 2026Md Yassir Mottalib, Md Yousuf, Eklachur Rahman Bhuiyan +5Autonomous Cyber Defense Deep Q-Learning
Master of Science in Information System Technology, Wilmington University, USA · Master's of Science in Data Analytics, College of Sciences and Technology, University of Houston- Downtown, USA · Master of Science in Information Technology, Washington University of Science and Technology, USA +4
Dueling Deep Q-Learning for Intrusion Detection
Aug 11, 2026Logan Luna, Matthew P. Berkowitz, Laxima Niure Kandel +1Deep Q-Learning Network Intrusion Detection
Georgia Institute of Technology · Embry-Riddle Aeronautical University
Aftab: A Progressive Design Study of Visual Encoders and Value Estimation for Replay-Free Parallelized Q-Learning
Aug 7, 2026Taha Shieenavaz, Shabnam Zareshahraki, Loris NanniDeep Q-Learning Q-Learning
Department of Information Engineering, University of Padua, Italy
Revisiting TD Target Aggregation under Uncertainty in Q-Learning
Aug 4, 2026Lipeng Zu, Xiaonan ZhangDeep Q-Learning Value Function Estimation
Computer Science Department, Florida State University, Tallahassee, Florida, USA
Endpoint Replay: Compressing the Recency Buffer in Deep Reinforcement Learning
Jul 27, 2026Parham Mohammad Panahi, Armin Ashrafi, Haoyu Du +3Deep Q-Learning Experience Replay
Department of Computing Science, University of Alberta, Canada · Alberta Machine Intelligence Institute (Amii) · Canada CIFAR AI Chair
Optimal Reward Shaping: Autonomous Car Parking Case Study
Jul 26, 2026Emre Özkaya, Nicolas R. GaugerDeep Q-Learning Autonomous Parking
Chair for Scientific Computing, RPTU University of Kaiserslautern-Landau Paul-Ehrlich-Strasse 34, 67663 Kaiserslautern
When Every Simulation Counts: Value-Based Reinforcement Learning for Accelerated Photonics Inverse Design
Jul 26, 2026Longying Wen, Feiyang Wu, Jinglin Yu +3Deep Q-Learning Sample-Efficient RL
School of Science and Engineering The Chinese University of Hong Kong, Shenzhen Shenzhen, China · Nick Holonyak Micro and Nanotechnology Laboratory University of Illinois Urbana-Champaign Urbana, Illinois, USA
Learning-Driven Adaptive Audit Scheduling: A Sequential Decision Approach to Off-Chain Data Integrity
Jul 19, 2026Changting Lin, Fan Li, Weihang Yu +4Markov Decision Processes Deep Q-Learning
Hangzhou Yunphant Network Technology Co., Ltd., Hangzhou, China · Zhejiang University, Hangzhou, China · Central University of Finance and Economics, Beijing, China +2
CLaC@FinMMEval 2026 Task 3: Sentiment-Augmented Deep Reinforcement Learning for Active Trading -- An Alpha-Reward Approach
Jul 17, 2026Andrei Neagu, Eeham Khan, Leila KosseimReinforcement Learning Deep Q-Learning
Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada
Accelerating Q-learning through Efficient Value-Sharing across Actions
Jun 29, 2026Prabhat Nagarajan, Brett Daley, Martha White +1Deep Q-Learning Value Function Estimation
Department of Computing Science, University of Alberta, Edmonton, AB, Canada. · Alberta Machine Intelligence Institute · Meta (independent work, not on Meta’s behalf). +1
Deep Q-Learning on Hölder Spaces
Jun 15, 2026Qian QiRL Control Deep Q-Learning
Peking University
Space-sampled Value Decay: Forgetting Mechanisms for Non-stationary Deep Reinforcement Learning
Jun 10, 2026Felix Störck, Fabian Hinder, Barbara HammerNon-Stationary RL Reinforcement Learning
CITEC, Faculty of Technology, Bielefeld University, Germany.
Discovering Interpretable Multi-Parameter Control Policies for Evolutionary Algorithms Using Deep Reinforcement Learning
Jun 8, 2026Tai Nguyen, Phong Le, Carola Doerr +1Evolutionary Optimization Deep Q-Learning
University of St Andrews, St Andrews, United Kingdom · Sorbonne Universit´e, CNRS, LIP6, Paris, France
COP-Q: Safety-First Reinforcement Learning for Robot Control via Cholesky-Ordered Projection
Jun 3, 2026Guopeng Li, Moritz A. Zanger, Matthijs T. J. Spaan +1Robotic Control Multi-Objective Reinforcement Learning
Department of Cognitive Robotics, Delft University of Technology · School of Transportation, Southeast University, China · Department of Intelligent Systems, Delft University of Technology, the Netherlands
Task-Induced Representational Invariances Depend on Learning Objective in Deep RL
Jun 1, 2026Manu Srinath Halvagal, Sebastian Lee, SueYeon ChungMarkov Decision Processes Deep Q-Learning
Department of Physics, Harvard University · 2Kempner Institute, Harvard University · Center for Computational Neuroscience, Flatiron Institute
QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning
Jun 1, 2026Nazmus Shakib Shadin, Xinyue Zhang, Jingyi Wang +1Reinforcement Learning Deep Q-Learning
Department of Computer Science, Kennesaw State University, Marietta, GA, 30060 USA · Department of Computer Science, San Francisco State University, San Francisco, CA, 94132 USA · Department of Electrical and Computer Engineering, University of Houston, Houston, TX, 77204 USA
SDM-Q: Cost-Aware Staged Decision-Making for Multi-Omics Classification with Deep Q-Learning
May 29, 2026Nan Mu, Yangfan Xiao, Ling Wang +3Cost-Sensitive Learning Deep Q-Learning
College of Computer Science, Sichuan Normal University, Chengdu, Sichuan 610101, China. · Department of Mathematics, College of Science and Mathematics, Kennesaw State University, Kennesaw, GA 30144, USA. · Department of Computer Science, College of Computing and Software Engineering, Kennesaw State University, Marietta, GA 30060, USA.
Don't Forget the Critic: Value-Based Data Rehearsal for Multi-Cyclic Continual Reinforcement Learning
May 21, 2026Benjamin Poole, Andrew Quinn, Li Yang +1Reinforcement Learning Deep Q-Learning
Department of Computer Science University of North Carolina at Charlotte

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Bayesian Neural Networks via Dirichlet Process Priors on the Data-Generating Distribution for Randomized Value Functions in Deep Reinforcement Learning

Sumit Vashishtha

Independent Researcher

Bayesian RL Uncertainty Estimation in RL Bayesian Neural Networks Deep Q-Learning

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Bayesian neural networks (BNNs) offer a principled framework for quantifying epistemic uncertainty. However, standard weight-space priors yield complex, high-dimensional neural network posteriors that are computationally prohibitive to sample from, and popular approximate inference schemes often fail to capture epistemic uncertainty effectively---a failure mode particularly acute in reinforcement learning (RL) \citep{osband2018randomized}. To resolve this challenge, we introduce a new class of BNNs that quantify uncertainty by placing Bayesian nonparametric Dirichlet Process (DP) priors directly on the underlying data-generating distribution rather than on network parameters. Building on this foundation, we develop DP-DQN, a reinforcement learning algorithm that leverages DP-BNNs to maintain posterior distributions over value functions and perform efficient Thompson sampling. By grounding randomized value functions in Bayesian nonparametrics, DP-DQN unifies data-driven posterior reweighting (via the Bayesian Bootstrap) with generative prior hypotheses injected directly through the DP base measure, enabling decisive ``deep exploration'' with a single neural network without the overhead of multi-network ensembles.