Network Distributed Multi-Agent Reinforcement Learning for Consensus Control of Quadcopters
This paper proposes a Network Distributed Multi-Agent Reinforcement Learning (ND-MARL) framework for quadcopter consensus control. Compared to conventional multi-agent MARL formulations that rely on centralized planning or fully decentralized execution, ND-MARL incorporates the swarm communication graph into the decision process. Under a 2-Neighbor communication topology, each agent observes information of only two neighbors and outputs an action through a distributed policy. A high-level distributed consensus planner is trained using Multi-Agent Soft Actor-Critic (MASAC) and embedded in a hierarchical stack to generate reference target positions tracked by a low-level quadcopter controll...
| Authors | Youssef Mahran, Zeyad Gamal, Aamir Ahmad et al. |
|---|---|
| Primary category | cs.RO |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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