Communication lays the foundation for human cooperation. It is also crucial
for multi-agent cooperation. However, existing work focuses on broadcast
communication, which is not only impractical but also leads to information
redundancy that could even impair the learning process. To tackle these
difficulties, we propose Individually Inferred Communication (I2C), a simple
yet effective model to enable agents to learn a prior for agent-agent
communication. The prior knowledge is learned via causal inference and realized
by a feed-forward neural network that maps the agent's local observation to a
belief about who to communicate with. The influence of one agent on another is
inferred via the joint action-value function in multi-agent reinforcement
learning and quantified to label the necessity of agent-agent communication.
Furthermore, the agent policy is regularized to better exploit communicated
messages. Empirically, we show that I2C can not only reduce communication
overhead but also improve the performance in a variety of multi-agent
cooperative scenarios, comparing to existing methods. The code is available at
this https URL

提出了 Individually Inferred Communication 模型，通过因果推断学习通信先验，并利用前馈神经网络实现代理之间的通信。该模型成功地减少了通信开销并提高了多种多代理合作场景中的表现。