In a multi-agent environment, In order to overcome and alleviate the non-stationarity of the multi-agent environment, the mainstream method is to adopt the framework of Centralized Training Decentralized Execution (CTDE). This thesis is based on the framework of CTDE, and studies the cooperative decision-making of multi-agent based on the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm for multi-agent proximal policy optimization. In order to alleviate the non-stationarity of the multi-agent environment, a multi-agent communication mechanism based on weight scheduling and attention module is introduced. Different agents can alleviate the non-stationarity caused by local observations through information exchange between agents, assisting in the collaborative decision-making of agents. The specific method is to introduce a communication module in the policy network part. The communication module is composed of a weight generator, a weight scheduler, a message encoder, a message pool and an attention module. Among them, the weight generator and weight scheduler will generate weights as the selection basis for communication, the message encoder is used to compress and encode communication information, the message pool is used to store communication messages, and the attention module realizes the interactive processing of the agent's own information and communication information. This thesis proposes a Multi-Agent Communication and Global Information Optimization Proximal Policy Optimization(MCGOPPO)algorithm, and conducted experiments in the SMAC and the MPE. The experimental results show that the improvement has achieved certain effects, which can better alleviate the non-stationarity of the multi-agent environment, and improve the collaborative decision-making ability among the agents.

翻译：在多智能体环境中，为了克服和缓解多智能体环境的非平稳性，主流方法是采用集中式训练分散式执行框架。本文基于CTDE框架，研究基于多智能体近端策略优化算法的协同决策问题。为缓解多智能体环境的非平稳性，引入一种基于权重调度与注意力模块的多智能体通信机制。不同智能体可通过智能体间的信息交换缓解局部观测导致的非平稳性，辅助智能体进行协同决策。具体方法是在策略网络部分引入通信模块，该模块由权重生成器、权重调度器、消息编码器、消息池和注意力模块组成。其中，权重生成器与权重调度器将生成权重作为通信选择依据，消息编码器用于压缩编码通信信息，消息池用于存储通信消息，注意力模块实现智能体自身信息与通信信息的交互处理。本文提出一种多智能体通信与全局信息优化近端策略优化算法，并在SMAC和MPE环境中进行实验。实验结果表明，该改进取得一定成效，能更好地缓解多智能体环境的非平稳性，提升智能体间的协同决策能力。