A proof of convergence of inverse reinforcement learning for multi-objective optimization

Akira Kitaoka, Riki Eto

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.

External IDs:dblp:journals/corr/abs-2305-06137