Heterogeneous multi-robot reinforcement learning

Abstract

We study cooperative multi-robot tasks where the team is composed of agents with structurally different sensors, actuators, and compute budgets. We introduce a centralised-training, decentralised-execution scheme that explicitly conditions on agent type, and show that it scales to teams of twelve heterogeneous robots with strictly better performance than role-blind baselines.

Paper