2 papers
cs.LG2023
A proof of imitation of Wasserstein inverse reinforcement learning for multi-objective optimization
Akira Kitaoka, Riki Eto
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 optimization…
cs.LG2023
A proof of convergence of inverse reinforcement learning for multi-objective optimization
Akira Kitaoka, Riki Eto
We show the convergence of Wasserstein inverse reinforcement learning for multi-objective optimizations with the projective subgradient method by formulating an inverse problem of…