The neural Inverse Reinforcement Learning algorithm proposed by Ruijia Zhang, Siliang Zeng, Chenliang Li, Alfredo Garcia, and Mingyi Hong is the first to provide a non-asymptotic convergence guarantee that identifies a provably global optimum within neural network settings.

Authors

• Person: Samuel Tesfazgi, Leonhard Sprandl, Sandra Hirche Organization: AISTATS
  Track: Poster Session 3 - aistats 2026

Sources

• Track: Poster Session 3 - aistats 2026 virtual.aistats.org Samuel Tesfazgi, Leonhard Sprandl, Sandra Hirche · AISTATS via serper

Referenced by nodes (1)

• artificial neural networks concept