Information- Theoretic Multi-Agent Algorithm Based on the CTDE Framework

We propose a Multi-Agent Reinforcement Learning (MARL) model based on the Centralized Training with Decentralized Execution (CTDE) framework, aimed at addressing cooperation and coordination issues in complex environments. Our model is based on an information sharing mechanism and introduces a global value assessment method to make the model's value evaluation more precise. During the training process, adversarial networks are used to promote the iterative improvement of communication networks among agents. Experiments conducted in various scenarios demonstrate the advantages of our model in improving the efficiency of agent collaboration and task completion rates. This research provides new insights into designing efficient MARL models and lays the foundation for future applications in more complex multi-agent systems.