Thrilled to share that our new paper has been accepted as an oral presentation at #AAAI2026! 🎉
We’ve been exploring how to truly hybridize learning and search for complex multi-agent planning — and we’re excited about what we found.
🔍 Graph Attention-Guided Search for Dense Multi-Agent Pathfinding Rishabh Jain Keisuke Okumura, Michael Amir & Amanda Prorok 📄 Paper: https://lnkd.in/e67nQsZW 👨💻 Code: https://lnkd.in/e8jrYBjq
In multi-agent pathfinding (#MAPF), learning-based methods have historically lagged behind strong search-based planners. So we asked: Can neural components really outperform SoTA search, and if so, how? For (arguably) the first time, our neural approach — LaGAT — clearly surpasses leading search-based planners 💥
The key: fusing GNN-based imitation learning with the LaCAM search planner to get the best of both worlds. Super excited for the discussions and energy at AAAI 2026 in Singapore 🇸🇬 this January!
More about the conference: https://lnkd.in/dvGu8Cqr