Nonuniqueness and Convergence to Equivalent Solutions in Observer-based Inverse Reinforcement Learning
Abstract
A key challenge in solving the deterministic inverse reinforcement learning (IRL) problem online and in real-time is the existence of multiple solutions. Nonuniqueness necessitates the study of the notion of equivalent solutions, i.e., solutions that result in a different cost functional but same feedback matrix, and convergence to such solutions. While offline algorithms that result in convergence to equivalent solutions have been developed in the literature, online, real-time techniques that address nonuniqueness are not available. In this paper, a regularized history stack observer that converges to approximately equivalent solutions of the IRL problem is developed. Novel data-richness conditions are developed to facilitate the analysis and simulation results are provided to demonstrate the effectiveness of the developed technique.
Keywords
Cite
@article{arxiv.2210.16299,
title = {Nonuniqueness and Convergence to Equivalent Solutions in Observer-based Inverse Reinforcement Learning},
author = {Jared Town and Zachary Morrison and Rushikesh Kamalapurkar},
journal= {arXiv preprint arXiv:2210.16299},
year = {2024}
}