Communication is a critical factor for the big multi-agent world to stay
organized and productive. Typically, most previous multi-agent
"learning-to-communicate" studies try to predefine the communication protocols
or use technologies such as tabular reinforcement learning and evolutionary
algorithm, which can not generalize to changing environment or large collection
of agents.
In this paper, we propose an Actor-Coordinator-Critic Net (ACCNet) framework
for solving "learning-to-communicate" problem. The ACCNet naturally combines
the powerful actor-critic reinforcement learning technology with deep learning
technology. It can efficiently learn the communication protocols even from
scratch under partially observable environment. We demonstrate that the ACCNet
can achieve better results than several baselines under both continuous and
discrete action space environments. We also analyse the learned protocols and
discuss some design considerations.

本文通过提出 Actor-Coordinator-Critic Net (ACCNet) 框架，结合强化学习和深度学习技术，在多智能体环境下实现更好的学习通信协议能力，并对学习到的协议进行分析和设计探讨。