Network Distributed Multi-Agent Reinforcement Learning for Consensus Control of Quadcopters

Researchers propose Network Distributed Multi-Agent Reinforcement Learning (ND-MARL), a framework that enables quadcopter swarms to achieve consensus control using only local 2-neighbor communication. The approach demonstrates zero-shot scalability, with policies trained on 3 agents successfully deployed to swarms of up to 250 agents without retraining, marking a significant advancement in distributed autonomous systems.

This research addresses a fundamental challenge in multi-agent systems: enabling coordination without centralized control or exhaustive information sharing. Traditional MARL approaches either concentrate decision-making in a central planner, creating bottlenecks and single points of failure, or demand fully decentralized execution with limited scalability. ND-MARL bridges this gap by embedding communication topology directly into the learning process, allowing agents to make informed decisions based only on immediate neighbors.

The zero-shot scalability finding represents a breakthrough in reinforcement learning applicability. Policies trained on three-agent systems generalize to 250-agent swarms while maintaining stable convergence—a property rarely achieved in multi-agent learning. This scalability emerges naturally from the 2-neighbor communication constraint, which prevents information bottlenecks as swarm size increases. The trade-off of increased steady-state spread at larger scales reflects realistic physics rather than algorithmic failure.

For autonomous systems development, this work validates distributed learning approaches for real-world deployment. Quadcopter consensus has immediate applications in search-and-rescue, environmental monitoring, and surveillance operations where centralized communication infrastructure may be unavailable or unreliable. The hierarchical integration with low-level controllers demonstrates practical feasibility beyond simulation.

The framework's relevance extends beyond robotics. Communication-aware distributed learning maps directly to decentralized network systems, blockchain consensus mechanisms, and edge computing architectures. As autonomous swarms become commercially viable, scalable coordination methods become critical infrastructure. Future work should examine robustness to communication failures, heterogeneous agent capabilities, and dynamic topology changes—practical constraints that determine real-world adoption.

• →ND-MARL achieves zero-shot scalability from 3-agent training to 250-agent deployments without retraining or model adaptation.
• →The 2-neighbor communication topology creates a sparse but sufficient information flow that prevents coordination bottlenecks as swarm size increases.
• →Hierarchical integration of distributed planner with low-level controllers enables smooth consensus trajectories in practical quadcopter systems.
• →Learned policies generalize across scales due to algorithm design that embeds communication constraints into the learning process rather than treating them as external limitations.
• →Framework addresses critical gaps in multi-agent reinforcement learning by balancing distributed execution with coordinated decision-making.