  Mean field theory provides an effective way of scaling multiagent
reinforcement learning algorithms to environments with many agents that can be
abstracted by a virtual mean agent. In this paper, we extend mean field
multiagent algorithms to multiple types. The types enable the relaxation of a
core assumption in mean field reinforcement learning, which is that all agents
in the environment are playing almost similar strategies and have the same
goal. We conduct experiments on three different testbeds for the field of many
agent reinforcement learning, based on the standard MAgents framework. We
consider two different kinds of mean field environments: a) Games where agents
belong to predefined types that are known a priori and b) Games where the type
of each agent is unknown and therefore must be learned based on observations.
We introduce new algorithms for each type of game and demonstrate their
superior performance over state of the art algorithms that assume that all
agents belong to the same type and other baseline algorithms in the MAgent
framework.
