Reinforcement learning in a continuum of agents

We present a decision-making framework for modeling the collective behavior of large groups of cooperatively interacting agents based on a continuum description of the agents’ joint state. The continuum model is derived from an agent-based system of locally coupled stochastic differential equations, taking into account that each agent in the group is only partially informed about the global system state. The usefulness of the proposed framework is twofold: (i) for multi-agent scenarios, it provides a computational approach to handling large-scale distributed decision-making problems and learning decentralized control policies. (ii) For single-agent systems, it offers an alternative approximation scheme for evaluating expectations of state distributions. We demonstrate our framework on a variant of the Kuramoto model using a variety of distributed control tasks, such as positioning and aggregation. As part of our experiments, we compare the effectiveness of the controllers learned by the continuum model and agent-based systems of different sizes, and we analyze how the degree of observability in the system affects the learning process.