How can I train agents for game-theoretic motion planning?
Learning Two-agent Motion Planning Strategies from Generalized Nash Equilibrium for Model Predictive Control
This paper introduces IGT-MPC, a decentralized algorithm for two-agent motion planning. It uses a learned value function, trained on the outcomes of game-theoretic interactions (Generalized Nash Equilibria), within a Model Predictive Control framework. This allows agents to implicitly consider other agents' actions and maximize their own rewards without complex real-time game-theoretic calculations.
Key points for LLM-based multi-agent systems: IGT-MPC demonstrates a way to combine learned strategic behavior with traditional control methods. This could be relevant for LLMs by offering a structured approach to integrate learned cooperative and competitive strategies, potentially improving performance and stability in multi-agent scenarios, especially when explicit modeling of agent interactions is difficult. The reward learning aspect could be particularly useful for training LLM-based agents to achieve specific collaborative or competitive objectives.