How can transformers improve drone coordination in DRL?
Graph Based Deep Reinforcement Learning Aided by Transformers for Multi-Agent Cooperation
April 14, 2025
https://arxiv.org/pdf/2504.08195This paper proposes a new framework for coordinating multiple autonomous drones to achieve tasks like disaster response or environmental monitoring. It uses graph neural networks (GNNs) and transformers to improve how drones communicate and make decisions, especially when they can only see a small part of the environment and have limited communication range. The system uses a double deep Q-network (DDQN) to optimize drone behavior, learning from experience and prioritizing important interactions.
Key points for LLM-based multi-agent systems:
- GNNs and Transformers for Coordination: Demonstrates how GNNs and transformers can be combined to model complex multi-agent interactions under communication constraints, potentially applicable to LLMs coordinating with each other or other agents.
- Partial Observability: The system addresses the challenge of limited visibility, which is relevant for LLMs operating with incomplete information.
- Adaptive Graph Construction: The dynamic graph updates based on drone proximity and visibility could inspire similar adaptive communication structures for LLM-based agents.
- Prioritized Experience Replay: The DQN's learning from experience using prioritized replay could be adapted for LLM agents to improve efficiency and focus on significant events.
- Scalability: The framework's performance in larger environments suggests potential for scaling to complex multi-agent LLM systems.