Can evolutionary games improve multi-agent pathfinding?
Multi-agent Path Finding for Timed Tasks using Evolutionary Games
This paper tackles the challenge of coordinating multiple autonomous agents (like robots or drones) to achieve timed tasks efficiently and safely in uncertain environments. It uses weighted automata to specify these tasks, going beyond simple deadlines to prioritize expeditiousness (i.e., completing tasks as quickly as possible within the deadline). The core contribution is MAPF-EGT, an algorithm that leverages evolutionary game theory principles to train a shared policy for homogeneous agents. This shared learning based on collective experiences accelerates the training process and yields superior performance compared to traditional single-agent reinforcement learning techniques like Q-learning and PPO, as well as classic search algorithms like A*.
Key points for LLM-based multi-agent systems:
- Weighted automata specifications: Offer a flexible way to define complex, timed tasks relevant to LLM agent interaction.
- Shared policy learning (via EGT): Facilitates faster and more robust training, applicable to scenarios where multiple LLMs collaborate on a shared objective. This reduces the computational burden of training individual LLMs, especially relevant for large language models.
- Focus on expeditiousness: Aligns well with real-world application requirements where timely completion of tasks is crucial for LLM-driven agents.
- Applicability to uncertain environments: Offers a potential framework for LLM-based agents operating in dynamic and unpredictable environments, crucial for real-world deployments.