Mean Field Theory

Mean field theory is decision making under uncertainty approach which responds to the agregate actions of many agents rather than agents individually.

Our Papers on Mean Field Theory

  1. Mean Field MARL
    Decentralized Mean Field Games
    Sriram Ganapathi Subramanian, Matthew Taylor, Mark Crowley, and Pascal Poupart.
    In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-2022). Virtual. Feb, 2022.
  2. PO-MFRL
    Partially Observable Mean Field Reinforcement Learning
    Sriram Ganapathi Subramanian, Matthew Taylor, Mark Crowley, and Pascal Poupart.
    In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS). International Foundation for Autonomous Agents and Multiagent Systems, London, United Kingdom. May, 2021.