How can I build robust AI agents using game theory?
STLGame: Signal Temporal Logic Games in Adversarial Multi-Agent Systems
December 3, 2024
https://arxiv.org/pdf/2412.01656This paper introduces STLGame, a framework for creating robust control policies for autonomous agents (like self-driving cars or drones) operating in environments with other potentially adversarial agents. It uses Signal Temporal Logic (STL) to define complex tasks and safety requirements. The system models interactions as a two-player zero-sum game, where the ego agent maximizes STL satisfaction (completing its task) and the opponent minimizes it. STLGame aims to find a Nash Equilibrium policy, the most robust strategy against any unknown opponent behavior.
Key points for LLM-based multi-agent systems:
- STL for task specification: STL provides a formal language to define complex, time-bound tasks, which could be generated or interpreted by LLMs in a multi-agent setting.
- Adversarial training: The zero-sum game formulation allows training robust agents even when the behavior of other agents is unknown or adversarial, a valuable characteristic in open multi-agent systems.
- Nash Equilibrium as robustness: Finding the Nash Equilibrium policy provides the best possible outcome for an agent given the worst-case behavior of others, maximizing robustness and safety.
- Differentiable STL: Using differentiable STL formulas significantly improves the efficiency of policy learning, which is crucial for complex multi-agent systems. This could be coupled with LLM-based reasoning.
- Gradient-based methods: The paper champions gradient-based methods for finding best-response policies over traditional reinforcement learning due to the sample efficiency gains when dealing with sparse reward signals, an important consideration for complex LLM-driven agent interactions.