Stochastic Optimal Control

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12 papers in the last four weeks, up 33% on the four weeks before. 0.1% of all new papers.

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Trajectory-Regularized Stochastic Optimal Control via KL Divergence
Jul 24, 2026Mintae Kim, Koushil SreenathStochastic Optimal Control
Hybrid Robotics, UC Berkeley, CA 94720, USA
Generalised Bellman recurrence and three dualities in sequential decision-making
Jul 20, 2026Fernando E. Rosas, David Hyland, Daniel PolaniMarkov Decision Processes Sequential Decision Making
Department of Informatics, University of Sussex · Department of Computer Science, University of Oxford · Department of Computer Science, University of Hertfordshire
Relative Entropy-Bounded Ambiguous Chance Constraints for Robust Planning in Nonlinear Systems
Jul 18, 2026Trevor N. Wolf, Jay W. McMahonChance-Constrained Optimization Stochastic Optimal Control
Smead Department of Aerospace Engineering Sciences, University of Colorado Boulder, Boulder, CO 80303, USA
LQG solution for POMDP without estimating states: A minimum variance approach
Jul 13, 2026Ranjan Sarkar, Prabhat K. MishraStochastic Optimal Control Feedback Control
Department of Artificial Intelligence at Indian Institute of Technology Kharagpur, West Bengal, India
Deep Learning for Dynamic Programming with Recursive Utility
Jul 5, 2026Xianhua Peng, Wu GuoStochastic Optimal Control Dynamic Programming
HSBC Business School, Peking University, University Town, Nanshan District, Shenzhen, 518055, China.
Finite-Sample Closed-Loop Stability of Model Predictive Path Integral Control for Linear Time-Invariant Systems
Jul 4, 2026Hyung-Jin Yoon, Hunmin KimDynamical Systems Stochastic Linear Dynamical Systems
Department of Mechanical and Nuclear Engineering, Tennessee Technological University, Cookeville, TN, USA
Stochastic Optimal Control Sampling for Diffusion Inverse Problems
Jun 27, 2026Jie Zhang, Youmei Qiu, Hanling Tian +3Stochastic Optimal Control Diffusion-Based Inverse Problems
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China · Shanghai Key Laboratory of Flexible Medical Robotics, Tongren Hospital, Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
Mean-Field PhiBE: Continuous-Time Mean-Field Reinforcement Learning from Discrete-Time Data
Jun 25, 2026Erhan Bayraktar, Martin Hernandez, Qinxin Yan +1Reinforcement Learning Stochastic Optimal Control
Department of Mathematics, University of Michigan, Ann Arbor, MI, USA. · Department of Statistics and Data Science, University of California, Los Angeles, CA, USA. · Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, USA.
QMaxCal: Path-Space Regularization for Open Quantum Control via Girsanov's Theorem
Jun 18, 2026Merijn Moody, Zier Mensch, Miranda C. N. Cheng +2Stochastic Optimal Control
Dutch Institute for Emergent Phenomena, University of Amsterdam, Netherlands · Institute of Physics, University of Amsterdam, Netherlands · Korteweg-de Vries Institute for Mathematics, University of Amsterdam, Netherlands +4
DiRecT: Safe Diffusion-Based Planning via Receding-Horizon Denoising
Jun 13, 2026Paolo Giaretta, Zeyang Li, Navid AzizanConstrained Motion Planning Diffusion-Based Planning
MIT
Deterministic Policy Gradient for Learning Equilibrium in Time-Inconsistent Control Problems
Jun 10, 2026Xin Guo, Yijie Huang, Xiang YuPolicy Gradient Stochastic Optimal Control
Department of Industrial Engineering and Operations Research, University of California, Berkeley, USA. · Department of Applied Mathematics, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
Autopilot-Preserving Residual Q-Learning with HJB-Inspired Finite-Action Risk Filtering for Fixed-Wing UAV Command Supervision
May 31, 2026Mehmet Iscan, Batuhan TemizAerial Robotics Stochastic Optimal Control
PythaLab, Yildiz Technical University, Istanbul, Turkey · Autopilot Systems Chief Engineer, Turkish Aerospace (TUSAŞ), Ankara, Turkey
A Per-Component Diagnostic Protocol for Neural HJB-PIDE Solvers under Control-Dependent Lévy Jumps
May 31, 2026R. DrissiNeural PDE Solvers Stochastic Optimal Control
Prospect-Theory Behavior from Bellman Optimality in MDPs with Catastrophic States
May 31, 2026Yujiao ChenMarkov Decision Processes Stochastic Optimal Control
Massachusetts Institute of Technology · Cambridge, MA 02139, USA
Sample Complexity of Policy Gradient for Log-Growth Control
May 26, 2026Qiuhua Pan, Yukai Shen, Liwei Zhang +2Stochastic Linear Dynamical Systems Stochastic Optimal Control
State Key Laboratory of Submarine Geoscience, School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai 200240, China · Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China · Shanghai Key Laboratory of Perception and Control in Industrial Network Systems, Shanghai 200240, China +1
Lifted Schrödinger Bridges for Gaussian Mixture Endpoints: Projection Gaps and Path-Space Obstructions
May 24, 2026Siddhartha Ganguly, George Rapakoulias, Panagiotis TsiotrasStochastic Optimal Control Schrödinger Bridges
S. Ganguly, G. Rapakoulias, and P. Tsiotras are with Daniel Guggenheim School of Aerospace, Georgia Institute of Technology, ½ Atlanta, USA.
Generative Modeling by Value-Driven Transport
May 21, 2026Pablo Moreno-Muñoz, Adrian Müller, Gergely NeuValue Function Estimation Stochastic Optimal Control
Universitat Pompeu Fabra · Barcelona, Spain · ETH Zürich +2
Zero-shot adaptation to order book dynamics
May 20, 2026Arip AsadulaevSequential Decision Making Stochastic Optimal Control
Beyond the Bellman Recursion: A Pontryagin-Guided Framework for Non-Exponential Discounting
May 20, 2026Hojin Ko, Jeonggyu HuhReinforcement Learning Stochastic Optimal Control
Department of Mathematics, Sungkyunkwan University, Suwon, Republic of Korea
Language Generation as Optimal Control: Closed-Loop Diffusion in Latent Control Space
May 14, 2026ZiYi Dong, Yuliang Huang, Weijian Deng +3Language Model-Based Control Stochastic Optimal Control
Sun Yat-sen University, Guangzhou, Guangdong, China · Australian National University, Canberra, ACT, Australia · Tsinghua University, Beijing, China
Efficient Adjoint Matching for Fine-tuning Diffusion Models
May 12, 2026Jeongwoo Shin, Dongsoo Shin, Yuchen Zhu +5Diffusion Model Alignment Stochastic Optimal Control
Seoul National University · Georgia Institute of Technology · Sungkyunkwan University
Gradient-Free Noise Optimization for Reward Alignment in Generative Models
May 12, 2026Jeongsol Kim, Hongeun Kim, Jian Wang +1Diffusion Model Alignment Stochastic Optimal Control
KAIST AI1 · Snap Inc.2
Mutual Information Optimal Density Control of Linear Systems and Generalized Schrödinger Bridges with Reference Refinement
May 10, 2026Shoju Enami, Kenji KashimaStochastic Optimal Control Schrödinger Bridges
Graduate School of Informatics, Kyoto University, Kyoto, Japan
Monotone Neural Policy Iteration for High-Dimensional First-Order Hamilton--Jacobi--Bellman Equations
May 8, 2026Minseok Kim, Yeongjong Kim, Namkyeong Cho +1Neural PDE Solvers Stochastic Optimal Control
Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea. · Department of Mathematics, Pohang University of Science and Technology, Pohang, Republic of Korea. · Department of Mathematical Finance and Big Data, Gachon University, Seongnam, Republic of Korea +1
Globally Solving Unbalanced Optimal Transport and Density Control for Gaussian Distributions
May 5, 2026Haruto Nakashima, Siddhartha Ganguly, Kenji KashimaSemidefinite Programming Stochastic Optimal Control
The Applied Mathematics and Physics Department, Graduate School of Informatics, Kyoto University, Kyoto, Japan · Daniel Guggenheim School of Aerospace, Georgia Institute of Technology, Atlanta, USA
Adaptive Estimation and Optimal Control in Offline Contextual MDPs without Stationarity
May 5, 2026Riddhiman Bhattacharyya, Sayak Chakrabarty, Imon BanerjeeNon-Stationary RL Markov Decision Processes
Department of Statistics, University of California, Santa Cruz Santa Cruz, California, USA · Department of Computer Science Northwestern University Evanston, IL, USA · Department of Industrial Engineering and Management Sciences Northwestern University Evanston, IL, USA
Feedback Motion Planning for Stochastic Nonlinear Systems with Signal Temporal Logic Specifications
May 4, 2026Liqian Ma, Zishun Liu, Glen Chou +1Constrained Motion Planning Trajectory Optimization
Georgia Institute of Technology, Atlanta, GA 30332
Analytic Bridge Diffusions for Controlled Path Generation
May 3, 2026Michael ChertkovStochastic Optimal Control Schrödinger Bridges
Program in Applied Mathematics & Department of Mathematics University of Arizona, AZ 85721, USA
A unified perspective on fine-tuning and sampling with diffusion and flow models
Apr 30, 2026Carles Domingo-Enrich, Yuanqi Du, Michael S. AlbergoStochastic Optimal Control Diffusion Sampling
Microsoft Research New England · Harvard University, Kempner Institute
Continuous-time q-learning for mean-field control with common noise, part-II: q-learning algorithms
Apr 30, 2026Zhenjie Ren, Xiaoli Wei, Xiang Yu +1Reinforcement Learning Stochastic Optimal Control
LaMME, Universit´e ´Evry Paris-Saclay, ´Evry, France · Department of Applied Mathematics, The Hong Kong Polytechnic University, Kowloon, Hong Kong · Department of Industrial Engineering and Operations Research, Columbia University, New York, USA

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Trajectory-Regularized Stochastic Optimal Control via KL Divergence

Mintae Kim, Koushil Sreenath

Hybrid Robotics, UC Berkeley, CA 94720, USA

Stochastic Optimal Control

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We introduce trajectory-regularized stochastic optimal control (TRSOC), which augments standard stochastic optimal control (SOC) with a Kullback--Leibler (KL) divergence between controlled and reference trajectory distributions. Using Girsanov's theorem, the trajectory KL reduces to a quadratic drift mismatch penalty, yielding a modified running cost that preserves the dynamic programming (DP) structure. We derive the corresponding Hamilton--Jacobi--Bellman (HJB) equation and characterize the optimal policy. In the linear-quadratic (LQ) setting, the formulation admits a closed-form solution with an augmented control cost. Experiments show that the regularization parameter induces a trade-off between performance-driven and reference-preserving behavior, including cases with reference dynamics learned from offline data.