  We investigate the problem of decentralized multi-agent navigation tasks,
where multiple agents need to reach initially unassigned targets in a limited
time. Classical planning-based methods suffer from expensive computation
overhead at each step and offer limited expressiveness for complex cooperation
strategies. In contrast, reinforcement learning (RL) has recently become a
popular paradigm for addressing this issue. However, RL struggles with low data
efficiency and cooperation when directly exploring (nearly) optimal policies in
the large search space, especially with an increased agent number (e.g., 10+
agents) or in complex environments (e.g., 3D simulators). In this paper, we
propose Multi-Agent Scalable GNN-based P lanner (MASP), a goal-conditioned
hierarchical planner for navigation tasks with a substantial number of agents.
MASP adopts a hierarchical framework to divide a large search space into
multiple smaller spaces, thereby reducing the space complexity and accelerating
training convergence. We also leverage graph neural networks (GNN) to model the
interaction between agents and goals, improving goal achievement. Besides, to
enhance generalization capabilities in scenarios with unseen team sizes, we
divide agents into multiple groups, each with a previously trained number of
agents. The results demonstrate that MASP outperforms classical planning-based
competitors and RL baselines, achieving a nearly 100% success rate with minimal
training data in both multi-agent particle environments (MPE) with 50 agents
and a quadrotor 3-dimensional environment (OmniDrones) with 20 agents.
Furthermore, the learned policy showcases zero-shot generalization across
unseen team sizes.
