How can I scale safe multi-agent control using GNNs for STL?
Scaling Safe Multi-Agent Control for Signal Temporal Logic Specifications
January 13, 2025
https://arxiv.org/pdf/2501.05639This paper introduces a new method for controlling multiple agents (e.g., robots, drones) to achieve complex tasks specified using Signal Temporal Logic (STL), a formal language for describing time-bound objectives. The core problem is coordinating agents to satisfy their individual STL tasks while avoiding collisions.
Key points for LLM-based multi-agent systems:
- Scalability: The proposed GNN-ODE planner addresses the scalability limitations of existing methods, handling more agents and complex tasks than traditional approaches. This is relevant to scaling LLM-based agents in complex environments.
- Decentralization: The GNN component models agent interactions in a decentralized way, allowing agents to make decisions based on local information. This aligns with the distributed nature of many multi-agent LLM applications.
- Safety: A collision-avoidance controller (GCBF+) works in conjunction with the planner to ensure safety during execution. This is crucial for real-world LLM agent deployments where safety is paramount.
- Differentiable STL: The use of differentiable STL robustness allows for end-to-end training of the planner and controller, enabling them to learn from complex temporal specifications. This is analogous to using reward functions derived from LLM outputs to guide agent behavior.
- Co-learning: The planner and safety controller are trained iteratively, leading to a "co-learned" behavior that optimizes both task satisfaction and collision avoidance. This could be applied to LLM-based systems where agent behavior and safety constraints need to be balanced.
- Generalizability: The method has been tested across various robot dynamics models and real-world drone experiments, showcasing its potential for broad applicability in LLM-based multi-agent scenarios.