Abstract: Mean field theory provides an effective way of scaling multiagent reinforcement learning (RL) algorithms to environments with many agents that can be abstracted by a virtual mean agent. However, vanilla mean field RL techniques assume a single type for all agents, complete observability of the action distribution of all agents and centralized learning. In this talk, I will describe extensions of mean field multiagent RL to partially observable domains, agents of multiple types and agents that learn independently of each other in a decentralized fashion.