Search Results for author:
Found 18 papers, 2 papers with code
Finite-Time Convergence in Continuous-Time Optimization
In this paper, we investigate a Lyapunov-like differential inequality that allows us to establish finite-time stability of a continuous-time state-space dynamical system represented via a multivariate ordinary differential equation or differential inclusion.
Policy Optimization for PDE Control with a Warm Start
The PO step fine-tunes the model-based controller to compensate for the modeling error from dimensionality reduction.
Smooth and Sparse Latent Dynamics in Operator Learning with Jerk Regularization
Spatiotemporal modeling is critical for understanding complex systems across various scientific and engineering disciplines, but governing equations are often not fully known or computationally intractable due to inherent system complexity.
Dual parametric and state estimation for partial differential equations
Designing estimation algorithms for systems governed by partial differential equations (PDEs) such as fluid flows is challenging due to the high-dimensional and oftentimes nonlinear nature of the dynamics, as well as their dependence on unobserved physical parameters.
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning Algorithms
This project serves the learning for dynamics & control (L4DC) community, aiming to explore key questions: the convergence of RL algorithms in learning control policies; the stability and robustness issues of learning-based controllers; and the scalability of RL algorithms to high- and potentially infinite-dimensional systems.
Global Convergence of Receding-Horizon Policy Search in Learning Estimator Designs
We introduce the receding-horizon policy gradient (RHPG) algorithm, the first PG algorithm with provable global convergence in learning the optimal linear estimator designs, i. e., the Kalman filter (KF).
Reinforcement learning-based estimation for partial differential equations
In systems governed by nonlinear partial differential equations such as fluid flows, the design of state estimators such as Kalman filters relies on a reduced-order model (ROM) that projects the original high-dimensional dynamics onto a computationally tractable low-dimensional space.
Domain Knowledge-Infused Deep Learning for Automated Analog/Radio-Frequency Circuit Parameter Optimization
The design automation of analog circuits is a longstanding challenge.
Domain Knowledge-Based Automated Analog Circuit Design with Deep Reinforcement Learning
The design automation of analog circuits is a longstanding challenge in the integrated circuit field.
Reinforcement Learning State Estimation for High-Dimensional Nonlinear Systems
In high-dimensional nonlinear systems such as fluid flows, the design of state estimators such as Kalman filters relies on a reduced-order model (ROM) of the dynamics.
Lyapunov Robust Constrained-MDPs: Soft-Constrained Robustly Stable Policy Optimization under Model Uncertainty
Safety and robustness are two desired properties for any reinforcement learning algorithm.
First-Order Optimization Algorithms via Discretization of Finite-Time Convergent Flows
In this paper, we investigate in the context of deep neural networks, the performance of several discretization algorithms for two first-order finite-time optimization flows.
Finite-time Newton seeking control
The averaged Newton seeking system is shown to achieve finite-time stability of the unknown optimum of the static map.
Optimization and Control
Robust Constrained-MDPs: Soft-Constrained Robust Policy Optimization under Model Uncertainty
In this paper, we focus on the problem of robustifying reinforcement learning (RL) algorithms with respect to model uncertainties.
First-Order Optimization Inspired from Finite-Time Convergent Flows
In this paper, we investigate the performance of two first-order optimization algorithms, obtained from forward Euler discretization of finite-time optimization flows.
Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization
Data generated from dynamical systems with unknown dynamics enable the learning of state observers that are: robust to modeling error, computationally tractable to design, and capable of operating with guaranteed performance.
Local Policy Optimization for Trajectory-Centric Reinforcement Learning
Motivated by these problems, we try to formulate the problem of trajectory optimization and local policy synthesis as a single optimization problem.
Model-based Reinforcement Learning
reinforcement-learning
+1
Finite-Time Convergence of Continuous-Time Optimization Algorithms via Differential Inclusions
In this paper, we propose two discontinuous dynamical systems in continuous time with guaranteed prescribed finite-time local convergence to strict local minima of a given cost function.
