Search Results for author:
Found 7 papers, 3 papers with code
Manifold-Guided Lyapunov Control with Diffusion Models
This paper presents a novel approach to generating stabilizing controllers for a large class of dynamical systems using diffusion models.
Physics-Informed Neural Network Policy Iteration: Algorithms, Convergence, and Verification
We provide a theoretical analysis of both algorithms in terms of convergence of neural approximations towards the true optimal solutions in a general setting.
Denoising Diffusion Restoration Tackles Forward and Inverse Problems for the Laplace Operator
Equivalently, we present an approach to restore the solution and the parameters in the Poisson equation by exploiting the eigenvalues and the eigenfunctions of the Laplacian operator.
Harmonic Control Lyapunov Barrier Functions for Constrained Optimal Control with Reach-Avoid Specifications
This paper introduces harmonic control Lyapunov barrier functions (harmonic CLBF) that aid in constrained control problems such as reach-avoid problems.
Actor-Critic Methods using Physics-Informed Neural Networks: Control of a 1D PDE Model for Fluid-Cooled Battery Packs
The Hamilton-Jacobi-Bellman (HJB) equation is a PDE that evaluates the optimality of the value function and determines an optimal controller.
Bridging Physics-Informed Neural Networks with Reinforcement Learning: Hamilton-Jacobi-Bellman Proximal Policy Optimization (HJBPPO)
The Proximal Policy Optimization (PPO)-Clipped algorithm is improvised with this implementation as it uses a value network to compute the objective function for its policy network.
A Comparison of Reward Functions in Q-Learning Applied to a Cart Position Problem
Growing advancements in reinforcement learning has led to advancements in control theory.
