Modular Reinforcement Learning for Robot Swarms

A new paper on arXiv proposes a modular reinforcement learning approach for cooperative robot swarms. Each robot in a swarm is computationally limited and interacts only with a small subset of peers, without knowing its impact on collective utility. Traditional distributed multi-agent reinforcement learning requires each robot to represent a combinatorial number of interaction states, straining memory. The alternative uses a decomposed representation where each state feature is handled by a separate learning procedure, with results aggregated. This reduces memory demands while enabling effective cooperation toward a common goal. The paper is available at arXiv:2605.04939.