Develops infinite-horizon stationary robust mean-field games incorporating distributional uncertainty, proves equilibrium existence via fixed-point on contractive Bellman operator, gives convergent algorithm, and derives finite-population approximation bounds under contractive regime.
arXiv:2106.03442 [cs.LG] https://arxiv.org/abs/2106.03442
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A modified harmonic mean operator correctly computes reward rates in non-stationary SMDPs for average-reward reinforcement learning.
citing papers explorer
-
Stationary Robust Mean-Field Games under Model Mismatches
Develops infinite-horizon stationary robust mean-field games incorporating distributional uncertainty, proves equilibrium existence via fixed-point on contractive Bellman operator, gives convergent algorithm, and derives finite-population approximation bounds under contractive regime.
-
A Harmonic Mean Formulation of Average Reward Reinforcement Learning in SMDPs
A modified harmonic mean operator correctly computes reward rates in non-stationary SMDPs for average-reward reinforcement learning.