MARL-GPT: Foundation Model for Multi-Agent Reinforcement Learning

ArXi:2604.05943v1 Announce Type: new Recent advances in multi-agent reinforcement learning (MARL) have nstrated success in numerous challenging domains and environments, but typically require specialized models for each task. In this work, we propose a coherent methodology that makes it possible for a single GPT-based model to learn and perform well across diverse MARL environments and tasks, including StarCraft Multi-Agent Challenge, Google Research Football and