Learning to cooperate is crucially important in multi-agent environments. The
key is to understand the mutual interplay between agents. However, multi-agent
environments are highly dynamic, where agents keep moving and their neighbors
change quickly. This makes it hard to learn abstract representations of mutual
interplay between agents. To tackle these difficulties, we propose graph
convolutional reinforcement learning, where graph convolution adapts to the
dynamics of the underlying graph of the multi-agent environment, and relation
kernels capture the interplay between agents by their relation representations.
Latent features produced by convolutional layers from gradually increased
receptive fields are exploited to learn cooperation, and cooperation is further
improved by temporal relation regularization for consistency. Empirically, we
show that our method substantially outperforms existing methods in a variety of
cooperative scenarios.

本文提出了一种使用图卷积强化学习的方法，通过使用关系内核捕获代理之间的相互作用来适应多代理环境的动态，并利用逐渐增大感受野的卷积层产生潜在特征来学习合作，此外，为了保持一致性，还使用了时间关系正则化方法。实验表明，该方法在各种合作场景中显着优于现有方法。