Deep Reinforcement Learning for Infinite Horizon Mean Field Problems in Continuous Spaces

arXiv:2309.10953v2 Announce Type: replace-cross
Abstract: We present the development and analysis of a reinforcement learning (RL) algorithm designed to solve continuous-space mean field game (MFG) and mean field control (MFC) problems in a unified manner. The proposed approach pairs the actor-critic (AC) paradigm with a representation of the mean field distribution via a parameterized score function, which can be efficiently updated in an online fashion, and uses Langevin dynamics to obtain samples from the resulting distribution. The AC agent and …

abstract actor actor-critic algorithm analysis and analysis arxiv continuous control cs.lg development game horizon math.oc mean paradigm reinforcement reinforcement learning representation solve space spaces type