A proof of convergence of inverse reinforcement learning for multi-objective optimization
Machine Learning
2023-05-19 v3 Artificial Intelligence
Machine Learning
Abstract
We show the convergence of Wasserstein inverse reinforcement learning for multi-objective optimizations with the projective subgradient method by formulating an inverse problem of the multi-objective optimization problem. In addition, we prove convergence of inverse reinforcement learning (maximum entropy inverse reinforcement learning, guided cost learning) with gradient descent and the projective subgradient method.
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Cite
@article{arxiv.2305.06137,
title = {A proof of convergence of inverse reinforcement learning for multi-objective optimization},
author = {Akira Kitaoka and Riki Eto},
journal= {arXiv preprint arXiv:2305.06137},
year = {2023}
}
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10 pages