Heterogeneous multi-robot reinforcement learning

Abstract

We study cooperative multi-robot tasks where the team is composed of agents with structurally different observations, action spaces, and reward functions. We introduce HetGPPO, a parameter-sharing paradigm enabling heterogeneous behaviours in multi-agent reinforcement learning with graph neural networks, achieving superior performance over role-blind baselines.

Paper