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This work proposes a neural network architecture that learns policies formultiple agent classes in a heterogeneous multi-agent reinforcement setting.The proposed network uses directed labeled graph representations for states,encodes feature vectors of different sizes for different entity classes, usesrelational graph convolution layers to model different communication channelsbetween entity types and learns distinct policies for different agent classes,sharing parameters wherever possible. Results have shown that specializing thecommunication channels between entity classes is a promising step to achievehigher performance in environments composed of heterogeneous entities.

Towards Heterogeneous Multi-Agent Reinforcement Learning with Graph Neural Networks