Establishes exponential convergence in Wasserstein distance for the mean-field limit and finite-particle approximation of a consensus-based method solving nonconvex bi-level optimization problems.
Graph reinforcement learning for network control via bi-level optimization
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
A hierarchical RL-OC method uses inverse optimization to derive structured lower-level policies from demonstrations, claiming superior efficiency and quality over end-to-end RL and existing hierarchical baselines on two control tasks.
citing papers explorer
-
Convergence of Consensus-Based Particle Methods for Nonconvex Bi-Level Optimization
Establishes exponential convergence in Wasserstein distance for the mean-field limit and finite-particle approximation of a consensus-based method solving nonconvex bi-level optimization problems.
-
Hierarchical Decision Making with Structured Policies: A Principled Design via Inverse Optimization
A hierarchical RL-OC method uses inverse optimization to derive structured lower-level policies from demonstrations, claiming superior efficiency and quality over end-to-end RL and existing hierarchical baselines on two control tasks.