Copertina

Sample-Efficient Model-Free Policy Gradient Methods for Stochastic LQR via Robust Linear Regression

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Song, Bowen, Gros, Sebastien, Iannelli, Andrea
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Systems and Control
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Convergence Guarantees of Model-free Policy Gradient Methods for LQR with Stochastic Data
di: Song, Bowen, et al.
Pubblicazione: (2025)

A Stochastic Gradient Descent Approach to Design Policy Gradient Methods for LQR
di: Song, Bowen, et al.
Pubblicazione: (2026)

Robustness of Online Identification-based Policy Iteration to Noisy Data
di: Song, Bowen, et al.
Pubblicazione: (2025)

Convergence and Robustness of Value and Policy Iteration for the Linear Quadratic Regulator
di: Song, Bowen, et al.
Pubblicazione: (2024)

The Role of Identification in Data-driven Policy Iteration: A System Theoretic Study
di: Song, Bowen, et al.
Pubblicazione: (2024)

Towards Safe Reinforcement Learning Using NMPC and Policy Gradients: Part I - Stochastic case
di: Gros, Sebastien, et al.
Pubblicazione: (2019)

On Globally Optimal Stochastic Policy Gradient Methods for Domain Randomized LQR Synthesis
di: Nguyen-Le, Alex, et al.
Pubblicazione: (2026)

Towards Safe Reinforcement Learning Using NMPC and Policy Gradients: Part II - Deterministic Case
di: Gros, Sebastien, et al.
Pubblicazione: (2019)

Policy Gradient Bounds in Multitask LQR
di: Stamouli, Charis, et al.
Pubblicazione: (2025)

Sample Complexity Bounds for Linear System Identification from a Finite Set
di: Chatzikiriakos, Nicolas, et al.
Pubblicazione: (2024)

Policy Gradient Methods for the Cost-Constrained LQR: Strong Duality and Global Convergence
di: Zhao, Feiran, et al.
Pubblicazione: (2024)

Hidden Convexity in Active Learning: A Convexified Online Input Design for ARX Systems
di: Chatzikiriakos, Nicolas, et al.
Pubblicazione: (2025)

Policy Gradient for LQR with Domain Randomization
di: Fujinami, Tesshu, et al.
Pubblicazione: (2025)

Stability-Certified On-Policy Data-Driven LQR via Recursive Learning and Policy Gradient
di: Sforni, Lorenzo, et al.
Pubblicazione: (2024)

Policy Gradient Adaptive Control for the LQR: Indirect and Direct Approaches
di: Zhao, Feiran, et al.
Pubblicazione: (2025)

Safe Reinforcement Learning Using Robust MPC
di: Zanon, Mario, et al.
Pubblicazione: (2019)

Adaptive control mechanisms in gradient descent algorithms
di: Iannelli, Andrea
Pubblicazione: (2025)

Derivative-Free Data-Driven Control of Continuous-Time Linear Time-Invariant Systems
di: Bosso, Alessandro, et al.
Pubblicazione: (2024)

LQR for Systems with Probabilistic Parametric Uncertainties: A Gradient Method
di: Cui, Leilei, et al.
Pubblicazione: (2026)

Stochastic LQR Design With Disturbance Preview
di: Liu, Jietian, et al.
Pubblicazione: (2024)

Model-Free Output Feedback Stabilization via Policy Gradient Methods
di: Zhang, Ankang, et al.
Pubblicazione: (2026)

Beyond Bounded Noise: Stochastic Set-Membership Estimation for Nonlinear Systems
di: Brändle, Felix, et al.
Pubblicazione: (2026)

Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem
di: Chakrabarti, Kushal, et al.
Pubblicazione: (2021)

Revisiting LQR Control from the Perspective of Receding-Horizon Policy Gradient
di: Zhang, Xiangyuan, et al.
Pubblicazione: (2023)

The Distributionally Robust Infinite-Horizon LQR
di: Hajar, Joudi, et al.
Pubblicazione: (2024)

Mixed Regular and Impulsive Sampled-data LQR
di: Daafouz, Jamal, et al.
Pubblicazione: (2024)

High Effort, Low Gain: Fundamental Limits of Active Learning for Linear Dynamical Systems
di: Chatzikiriakos, Nicolas, et al.
Pubblicazione: (2025)

Accelerated ADMM: Automated Parameter Tuning and Improved Linear Convergence
di: Tavakoli, Meisam, et al.
Pubblicazione: (2025)

A hybrid systems framework for data-based adaptive control of linear time-varying systems
di: Iannelli, Andrea, et al.
Pubblicazione: (2024)

Convergence Analysis of Gradient Flow for Overparameterized LQR Formulations
di: de Oliveira, Arthur Castello B., et al.
Pubblicazione: (2024)

MR-ARL: Model Reference Adaptive Reinforcement Learning for Robustly Stable On-Policy Data-Driven LQR
di: Borghesi, Marco, et al.
Pubblicazione: (2024)

Online Convex Optimization and Integral Quadratic Constraints: An automated approach to regret analysis
di: Jakob, Fabian, et al.
Pubblicazione: (2025)

Convergence and stability of Q-learning in Hierarchical Reinforcement Learning
di: Manenti, Massimiliano, et al.
Pubblicazione: (2025)

Data-Driven Stabilization of Continuous-Time LTI Systems from Noisy Input-Output Data
di: Bosso, Alessandro, et al.
Pubblicazione: (2025)

Second-Order Policy Gradient Methods for the Linear Quadratic Regulator
di: Valaei, Amirreza, et al.
Pubblicazione: (2025)

Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration
di: Carnevale, Guido, et al.
Pubblicazione: (2024)

Bridging Continuous-time LQR and Reinforcement Learning via Gradient Flow of the Bellman Error
di: Gießler, Armin, et al.
Pubblicazione: (2025)

Analyzing the Impact of Computation in Adaptive Dynamic Programming for Stochastic LQR Problem
di: Cao, Wenhan, et al.
Pubblicazione: (2024)

Data-Enabled Policy Optimization for Direct Adaptive Learning of the LQR
di: Zhao, Feiran, et al.
Pubblicazione: (2024)

Cost-Matching Model Predictive Control for Efficient Reinforcement Learning in Humanoid Locomotion
di: Cai, Wenqi, et al.
Pubblicazione: (2026)