  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.
