Real-world artificial intelligence (AI) applications often require multiple agents to work in a collaborative effort. Efficient learning for intra-agent communication and coordination is an indispensable step towards general AI. In this paper, we take StarCraft combat game as the test scenario, where the task is to coordinate multiple agents as a team to defeat their enemies. To maintain a scalable yet effective communication protocol, we introduce a multiagent bidirectionally-coordinated network (BiCNet ['bIknet]) with a vectorised extension of actor-critic formulation. We show that BiCNet can handle different types of combats under diverse terrains with arbitrary numbers of AI agents for both sides. Our analysis demonstrates that without any supervisions such as human demonstrations or labelled data, BiCNet could learn various types of coordination strategies that is similar to these of experienced game players. Moreover, BiCNet is easily adaptable to the tasks with heterogeneous agents. In our experiments, we evaluate our approach against multiple baselines under different scenarios; it shows state-of-the-art performance, and possesses potential values for large-scale real-world applications.

本文以StarCraft战斗游戏为案例研究，介绍了一种名为Multiagent Bidirectionally-Coordinated Network (BiCNet)的协议，该协议采用了有向图网络和actor-critic学习方式，精准、高效地处理多智能体之间的通信和协作问题，实验结果表现优异，具备广泛的实际应用价值。

多智能体双向协调网络：在学习玩《星际争霸》游戏中出现人类水平的协调现象