Can hybrid MA-Pathfinding improve dynamic web app navigation?
Cooperative Hybrid Multi-Agent Pathfinding Based on Shared Exploration Maps
March 31, 2025
https://arxiv.org/pdf/2503.22162This paper introduces CHS (Cooperative Hybrid Multi-Agent Pathfinding Based on Shared Exploration Map), a new method for coordinating multiple agents navigating a space, especially when the environment is partially unknown or changes frequently. It combines a traditional pathfinding algorithm (D* Lite) with multi-agent reinforcement learning (MARL). Agents share information about changes in the environment incrementally, reducing communication overhead. This hybrid approach improves success rates, minimizes collisions, and optimizes path efficiency, particularly in large-scale deployments with frequent changes.
Key points for LLM-based multi-agent systems:
- Hybrid approach: CHS combines classical planning with learning, a valuable strategy for LLM agents that need to balance pre-existing knowledge with adaptability.
- Incremental updates and shared maps: This reduces communication costs, a crucial factor in complex LLM-based multi-agent systems. The concept of sharing updates rather than full world states can improve efficiency.
- Loop detection and anti-freezing: These mechanisms address common challenges in agent navigation and can inspire similar solutions for LLM agents stuck in repetitive behavior.
- Adaptability to dynamic environments: CHS is designed for changing environments, a critical feature for LLM-based agents interacting with complex, real-world scenarios.
- Focus on partial observability: The framework operates under partial observability, which is highly relevant to LLM agents that often have limited information about their environment and other agents.