Review for NeurIPS paper: Calibration of Shared Equilibria in General Sum Partially Observable Markov Games

Summary and Contributions: The paper presents the concept of shared equilibrium in certain kinds of multi agent stochastic games with a restricted form of partial observability. The formalism includes the notion of supertypes (different distributions of agents) and types (where each agents is given a true type each episode). The agent's type influences the rewards available as does the joint state of the system and joint action over all agents. One key constraint is that all agents of the same type follow the same policy from an egocentric perspective (where they themselves are the focal agent and all other agents are interchangeable). They define a policy gradient approach for individual agents, also present a higher order learning rule that shifts the distribution over supertypes at a slower timescale.