Reactive optimal motion planning for a class of holonomic planar agents using reinforcement learning with provable guarantees.

ABSTRACT: In control theory, reactive methods have been widely celebrated owing to their success in providing robust, provably convergent solutions to control problems. Even though such methods have long been formulated for motion planning, optimality has largely been left untreated through reactive means, with the community focusing on discrete/graph-based solutions. Although the latter exhibit certain advantages (completeness, complicated state-spaces), the recent rise in Reinforcement Learning (RL), provides novel ways to address the limitations of reactive methods. The goal of this paper is to treat the reactive optimal motion planning problem through an RL framework. A policy iteration RL scheme is formulated in a consistent manner with the control-theoretic results, thus utilizing the advantag

SUBMITTER: Rousseas P 

PROVIDER: S-EPMC10791867 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature