Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games

TLDR

AI systems often require multiple agents to collaborate, and efficient learning of intra‑agent communication and coordination is essential for general AI. This study aims to coordinate multiple agents as a team to defeat enemies in StarCraft combat games. The authors introduce a Multiagent Bidirectionally‑Coordinated Network (BiCNet) that extends the actor‑critic framework with a vectorized communication protocol to enable scalable coordination. BiCNet learns advanced coordination strategies without supervision, handles arbitrary numbers of agents across various combats, and achieves state‑of‑the‑art performance against multiple baselines.

Abstract

Many artificial intelligence (AI) applications often require multiple intelligent 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 a case study, 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 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 advanced coordination strategies that have been commonly used by experienced game players. 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.