Optimization & Control

Papers

• Distributional Reinforcement Learning With Quantile Regression (2017)
  Will Dabney, Mark Rowland, Marc G. Bellemare, et al.
  19.20
• Statistical Inference Of The Value Function For Reinforcement Learning In Infinite Horizon Settings (2020)
  C. Shi, S. Zhang, W. Lu, et al.
  13.14
• Adaptive Trust Region Policy Optimization: Global Convergence And Faster Rates For Regularized Mdps (2019)
  Lior Shani, Yonathan Efroni, Shie Mannor
  12.10
• Convergence Proof For Actor-critic Methods Applied To PPO And RUDDER (2020)
  Markus Holzleitner, Lukas Gruber, José Arjona-Medina, et al.
  11.67
• Robust Reinforcement Learning: A Case Study In Linear Quadratic Regulation (2020)
  Bo Pang, Zhong-Ping Jiang
  11.19
• Action Candidate Driven Clipped Double Q-learning For Discrete And Continuous Action Tasks (2022)
  Haobo Jiang, Jin Xie, Jian Yang
  10.61
• Finite-sample Analysis Of Nonlinear Stochastic Approximation With Applications In Reinforcement Learning (2019)
  Zaiwei Chen, Sheng Zhang, Thinh T. Doan, et al.
  10.35
• Revisiting State Augmentation Methods For Reinforcement Learning With Stochastic Delays (2021)
  Somjit Nath, Mayank Baranwal, Harshad Khadilkar
  10.35
• Efficiently Breaking The Curse Of Horizon In Off-policy Evaluation With Double Reinforcement Learning (2019)
  Nathan Kallus, Masatoshi Uehara
  10.21
• Achieving Zero Constraint Violation For Constrained Reinforcement Learning Via Primal-dual Approach (2021)
  Qinbo Bai, Amrit Singh Bedi, Mridul Agarwal, et al.
  9.59
• Learning And Information In Stochastic Networks And Queues (2021)
  Neil Walton, Kuang Xu
  9.03
• A reinforcement learning agent for maintenance of deteriorating systems with increasingly imperfect repairs (2025)
  Alberto Pliego Marug\'an et al.
  8.69
• Rethinking The Discount Factor In Reinforcement Learning: A Decision Theoretic Approach (2019)
  Silviu Pitis
  8.60
• Parameterized Mdps And Reinforcement Learning Problems -- A Maximum Entropy Principle Based Framework (2020)
  Amber Srivastava, Srinivasa M Salapaka
  8.60
• Inexact Iterative Numerical Linear Algebra For Neural Network-based Spectral Estimation And Rare-event Prediction (2023)
  John Strahan, Spencer C. Guo, Chatipat Lorpaiboon, et al.
  8.35
• Minimax Optimal Q Learning With Nearest Neighbors (2023)
  Puning Zhao, Lifeng Lai
  8.09
• Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model (2020)
  Gen Li et al.
  7.83
• Logarithmic Regret For Episodic Continuous-time Linear-quadratic Reinforcement Learning Over A Finite-time Horizon (2020)
  Matteo Basei, Xin Guo, Anran Hu, et al.
  7.81
• Computably Continuous Reinforcement-learning Objectives Are Pac-learnable (2023)
  Cambridge Yang, Michael Littman, Michael Carbin
  7.81
• Approximating Euclidean By Imprecise Markov Decision Processes (2020)
  Manfred Jaeger, Giorgio Bacci, Giovanni Bacci, et al.
  7.50
• Renewal Monte Carlo: Renewal Theory Based Reinforcement Learning (2018)
  Jayakumar Subramanian, Aditya Mahajan
  7.50
• Deep Embedded Multiplicative DMD for Algebra-Preserving Koopman Learning (2026)
  Kelan Gray et al.
  7.38
• Extensions of Robbins-Siegmund Theorem with Applications in Reinforcement Learning (2025)
  Xinyu Liu et al.
  7.35
• An Online Prediction Algorithm For Reinforcement Learning With Linear Function Approximation Using Cross Entropy Method (2018)
  Ajin George Joseph, Shalabh Bhatnagar
  7.16
• Entropic Regularization Of Markov Decision Processes (2019)
  Boris Belousov, Jan Peters
  6.77
• Nonparametric Bellman Mappings For Reinforcement Learning: Application To Robust Adaptive Filtering (2024)
  Yuki Akiyama, Minh Vu, Konstantinos Slavakis
  6.34
• $O(1/k)$ Finite-Time Bound for Non-Linear Two-Time-Scale Stochastic Approximation (2025)
  Siddharth Chandak
  6.28
• ParetoPilot: Zero-Surrogate Offline Multi-Objective Optimization via Infer-Perturb-Guide Diffusion (2026)
  Ruiqing Sun et al.
  6.23
• Uncertainty-Aware End-to-End Co-Design of Neural Network Processors: From Training and Mapping to Fabrication (2026)
  Yuyang Du et al.
  6.23
• Pseudospectral Bounds for Transient Amplification in Coupled Gradient Descent (2026)
  Ahanaf Hasan Ariq
  5.89
• PE-MHL: Physics-Encoded Modular Hybrid Layers for Scalable Learning of Complex Systems (2026)
  Ismail Hassaballa et al.
  5.89
• A Geometric Characterization of the Stationary Plateau for Two-Layer Neural Networks (2026)
  Tian Ding et al.
  5.89
• When Both Layers Learn: Training Dynamics of Representing Linear Models via ReLU Networks (2026)
  Berk Tinaz et al.
  5.89
• Activation Steering of Video Generation Models via Reduced-Order Linear Optimal Control (2026)
  Jihoon Hong et al.
  5.89
• Performance Dynamics And Termination Errors In Reinforcement Learning: A Unifying Perspective (2019)
  Nikki Lijing Kuang, Clement H. C. Leung
  5.84
• On Generalized Bellman Equations And Temporal-difference Learning (2017)
  Huizhen Yu, A. Rupam Mahmood, Richard S. Sutton
  5.84
• Semantic Constraint Synthesis for Adaptive Trajectory Optimization via Large Language Models (2026)
  Eleanor Brosius et al.
  5.49
• Reinforcement Learning With Non-cumulative Objective (2023)
  Wei Cui, Wei Yu
  5.24
• Differential Temporal Difference Learning (2018)
  Adithya M. Devraj, Ioannis Kontoyiannis, Sean P. Meyn
  5.24
• Logarithmic Regret Bounds For Continuous-time Average-reward Markov Decision Processes (2022)
  Xuefeng Gao, Xun Yu Zhou
  5.24
• Assumed Density Filtering Q-learning (2017)
  Heejin Jeong, Clark Zhang, George J. Pappas, et al.
  5.24
• Stochastic Reinforcement Learning (2019)
  Nikki Lijing Kuang, Clement H. C. Leung, Vienne W. K. Sung
  5.24
• Zeroth-order Actor-critic: An Evolutionary Framework For Sequential Decision Problems (2022)
  Yuheng Lei, Yao Lyu, Guojian Zhan, et al.
  5.24
• Online Apprenticeship Learning (2021)
  Lior Shani, Tom Zahavy, Shie Mannor
  5.24
• Nonlocal Mean Field Schr\"{o}dinger Bridge with Learned Interactions (2026)
  Daisuke Inoue et al.
  5.01
• When Freshness Is Not Enough: Distribution-Aware Age of Information for Networked LQR Control (2026)
  Abdullah Y. Etcibasi et al.
  5.01
• Near-Optimal Decentralized Stochastic Convex Optimization over Networks (2026)
  Nitai Kluger et al.
  5.01
• RA-DCA: A Randomized Active-Set DCA for Directional Stationarity in Max-Structured DC Programs (2026)
  Yi-Shuai Niu
  4.95
• Riemannian Archetypal Analysis: Interpretable non-linear data analysis on deformed star distributions (2026)
  Willem Diepeveen et al.
  4.95
• Error estimates for tamed Euler and Randomized Euler schemes for SDEs with locally Lipschitz drift with applications to non-logconcave sampling and optimization (2026)
  Iosif Lytras et al.
  4.95