Personalized Training with Distilled Execution: Enhancing Multi-Agent Reinforcement Learning through Agent-Specific Global Information

The core message of this paper is that utilizing agent-personalized global information, rather than uniform global information, can significantly improve the performance of multi-agent reinforcement learning. The authors propose a novel two-stage training paradigm called Personalized Training with Distilled Execution (PTDE) to achieve this.