Parametric PDE Control with Deep Reinforcement Learning and Differentiable L0-Sparse Polynomial Policies

arXiv:2403.15267v1 Announce Type: new
Abstract: Optimal control of parametric partial differential equations (PDEs) is crucial in many applications in engineering and science. In recent years, the progress in scientific machine learning has opened up new frontiers for the control of parametric PDEs. In particular, deep reinforcement learning (DRL) has the potential to solve high-dimensional and complex control problems in a large variety of applications. Most DRL methods rely on deep neural network (DNN) control policies. However, for many dynamical systems, …

abstract applications arxiv control cs.lg differentiable differential engineering frontiers machine machine learning parametric policies polynomial progress reinforcement reinforcement learning science scientific type