Can simple imitation learning beat complex MAPF models?
Work Smarter Not Harder: Simple Imitation Learning with CS-PIBT Outperforms Large Scale Imitation Learning for MAPF
September 24, 2024
https://arxiv.org/pdf/2409.14491This research paper investigates how to train AI agents to navigate a shared space without collisions, a problem known as Multi-Agent Path Finding (MAPF). The key finding is that instead of focusing on complex models or massive datasets, using a simple "collision shield" that resolves immediate collisions leads to state-of-the-art performance in minutes.
For LLM-based multi-agent systems, this means:
- Prioritize smart collision handling: Instead of pouring resources into building complex models, invest in robust collision resolution mechanisms.
- Focus on long-term planning: With immediate collisions handled, LLMs can focus on higher-level reasoning and strategic decision-making for better coordination in multi-agent scenarios.
- Simplified training: Leveraging existing strong planners with the collision shield enables efficient training of LLM-based agents even with smaller datasets.