Enhancing the Performance of DeepReach on High-Dimensional Systems through Optimizing Activation Functions
Abstract
With the continuous advancement in autonomous systems, it becomes crucial to provide robust safety guarantees for safety-critical systems. Hamilton-Jacobi Reachability Analysis is a formal verification method that guarantees performance and safety for dynamical systems and is widely applicable to various tasks and challenges. Traditionally, reachability problems are solved by using grid-based methods, whose computational and memory cost scales exponentially with the dimensionality of the system. To overcome this challenge, DeepReach, a deep learning-based approach that approximately solves high-dimensional reachability problems, is proposed and has shown lots of promise. In this paper, we aim to improve the performance of DeepReach on high-dimensional systems by exploring different choices of activation functions. We first run experiments on a 3D system as a proof of concept. Then we demonstrate the effectiveness of our approach on a 9D multi-vehicle collision problem.
Cite
@article{arxiv.2312.17583,
title = {Enhancing the Performance of DeepReach on High-Dimensional Systems through Optimizing Activation Functions},
author = {Qian Wang and Tianhao Wu},
journal= {arXiv preprint arXiv:2312.17583},
year = {2024}
}