Can MPC beat MARL for drone delivery path planning?
Optimal Path Planning and Cost Minimization for a Drone Delivery System Via Model Predictive Control
March 26, 2025
https://arxiv.org/pdf/2503.19699This paper explores using Model Predictive Control (MPC) to optimize drone delivery paths, minimizing costs and ensuring collision avoidance. It compares MPC against three Multi-Agent Reinforcement Learning (MARL) algorithms: Independent Q-Learning (IQL), Joint Action Learners (JAL), and Value-Decomposition Networks (VDN). Experiments show MPC finds solutions faster and uses fewer drones than MARL, although at a slightly higher cost. Key takeaways relevant to LLM-based multi-agent systems are:
- MPC offers a model-based approach to multi-agent control, which can be more interpretable and reliable than MARL, especially in real-time applications.
- MPC's ability to handle constraints makes it suitable for complex scenarios like drone navigation.
- The challenges of MARL highlighted (scalability, stability, exploration/exploitation balance, etc.) are relevant to LLM-based agents.
- The paper demonstrates how traditional control algorithms like MPC can provide valuable alternatives to RL in multi-agent settings. This is especially relevant for LLM-based agents that may require explicit control mechanisms.