TL;DR我们提出了一个基于角色的多智能体强化学习框架，该框架使用角色分配网络将学习代理分配到团队中，以适应不同的团队大小，并通过 StarCraft II 模拟来展示该方法的有效性。
Abstract
multi-agent reinforcement learning holds the key for solving complex tasks
that demand the coordination of learning agents. However, strong coordination
often leads to expensive exploration over the exponentially large state-action
space. A powerful approach is to decompose team works