A proof of imitation of Wasserstein inverse reinforcement learning for multi-objective optimization
Machine Learning
2023-05-19 v2 Artificial Intelligence
Machine Learning
Abstract
We prove Wasserstein inverse reinforcement learning enables the learner's reward values to imitate the expert's reward values in a finite iteration for multi-objective optimizations. Moreover, we prove Wasserstein inverse reinforcement learning enables the learner's optimal solutions to imitate the expert's optimal solutions for multi-objective optimizations with lexicographic order.
Keywords
Cite
@article{arxiv.2305.10089,
title = {A proof of imitation of Wasserstein inverse reinforcement learning for multi-objective optimization},
author = {Akira Kitaoka and Riki Eto},
journal= {arXiv preprint arXiv:2305.10089},
year = {2023}
}
Comments
9 pages. This text is continuation from arXiv:2305.06137