New RL algorithms achieve optimal policies with polynomial sample complexity for mean-field problems.
problem Statistical efficiency of Mean-Field Reinforcement Learning with general function approximation.
method Introduce MF-MBED to characterize problem complexity, propose algorithms based on maximal likelihood estimation.
result Rich mean-field RL problems have low MF-MBED, leading to polynomial sample complexity.