Towards Heterogeneous Multi-Agent Reinforcement Learning with Graph Neural Networks

• URL: http://arxiv.org/abs/2009.13161v3
• Date: Tue, 20 Oct 2020 20:47:02 GMT
• Title: Towards Heterogeneous Multi-Agent Reinforcement Learning with Graph Neural Networks
• Authors: Douglas De Rizzo Meneghetti and Reinaldo Augusto da Costa Bianchi
• Abstract summary: This work proposes a neural network architecture that learns policies for multiple agent classes in a heterogeneous multi-agent reinforcement setting. Results have shown that specializing the communication channels between entity classes is a promising step to achieve higher performance in environments composed of heterogeneous entities.
• Score: 1.370633147306388
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work proposes a neural network architecture that learns policies for multiple 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, uses relational graph convolution layers to model different communication channels between entity types and learns distinct policies for different agent classes, sharing parameters wherever possible. Results have shown that specializing the communication channels between entity classes is a promising step to achieve higher performance in environments composed of heterogeneous entities.

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