Jul 12, 2020
Multi-agent coordination and routing is a complex problem and has a wide range of applications in areas from vehicle fleet coordination to autonomous mapping. Whereas traditional methods are not designed for realistic environments such as sparse connectivity and unknown traffics and are often slow in runtime; in this paper, we propose a graph neural network based model that is able to perform multiagent routing in a sparsely connected graph with dynamically changing traffic conditions, outperforming existing methods. Our learned communication module in the proposed model enables the agents to coordinate online and adapt to changes to their environment. We also show that our model trained with only two agents on graphs with a maximum of twenty-five nodes can easily generalize to five agents with a hundred nodes.
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
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