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In multi-agent systems with large number of agents, typically thecontribution of each agent to the value of other agents is minimal (e.g.,aggregation systems such as Uber, Deliveroo). In this paper, we consider suchmulti-agent systems where each agent is self-interested and takes a sequence ofdecisions and represent them as a Stochastic Non-atomic Congestion Game (SNCG).We derive key properties for equilibrium solutions in SNCG model withnon-atomic and also nearly non-atomic agents. With those key equilibriumproperties, we provide a novel Multi-Agent Reinforcement Learning (MARL)mechanism that minimizes variance across values of agents in the same state. Todemonstrate the utility of this new mechanism, we provide detailed results on areal-world taxi dataset and also a generic simulator for aggregation systems.We show that our approach reduces the variance in revenues earned by taxidrivers, while still providing higher joint revenues than leading approaches.

Learning Individual Policies in Large Multi-agent Systems through Local Variance Minimization