Much work has been dedicated to the exploration of Multi-Agent Reinforcement Learning (MARL) paradigms implementing a centralized learning with decentralized execution (CLDE) approach to achieve human-like collaboration in cooperative tasks. Here, we discuss variations of centralized training and describe a recent survey of algorithmic approaches. The goal is to explore how different implementations of information sharing mechanism in centralized learning may give rise to distinct group coordinated behaviors in multi-agent systems performing cooperative tasks.

中文翻译：

---

最近利用集中训练的多智能体强化学习算法调查

许多工作致力于探索多智能体强化学习 (MARL) 范式，实施集中学习与分散执行 (CLDE) 方法，以在协作任务中实现类人协作。在这里，我们讨论集中训练的变化，并描述最近对算法方法的调查。目的是探索集中学习中信息共享机制的不同实现如何在执行协作任务的多代理系统中产生不同的群体协调行为。