A fundamental task for artificial intelligence is learning. Deep Neural Networks have proven to cope perfectly with all learning paradigms, i.e. supervised, unsupervised, and reinforcement learning. Nevertheless, traditional deep learning approaches make use of cloud computing facilities and do not scale well to autonomous agents with low computational resources. Even in the cloud, they suffer from computational and memory limitations, and they cannot be used to model adequately large physical worlds for agents which assume networks with billions of neurons. These issues are addressed in the last few years by the emerging topic of sparse training, which trains sparse networks from scratch. This paper discusses sparse training state-of-the-art, its challenges and limitations while introducing a couple of new theoretical research directions which has the potential of alleviating sparse training limitations to push deep learning scalability well beyond its current boundaries. Nevertheless, the theoretical advancements impact in complex multi-agents settings is discussed from a real-world perspective, using the smart grid case study.

中文翻译：

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可扩展高效代理的稀疏训练理论

人工智能的一项基本任务是学习。事实证明，深度神经网络可以完美地应对所有学习范例，即监督学习，无监督学习和强化学习。然而，传统的深度学习方法利用云计算工具，无法很好地扩展到具有低计算资源的自治代理。即使在云中，它们也受到计算和内存限制的困扰，并且无法用于为具有数十亿个神经元网络的智能体建模足够大的物理世界。在过去的几年中，新兴的稀疏训练主题解决了这些问题，该主题从头开始训练稀疏网络。本文讨论了稀疏训练的最新技术，它的挑战和局限性，同时引入了两个新的理论研究方向，有可能缓解稀疏训练的局限性，从而将深度学习可扩展性远远超出其当前范围。尽管如此，使用智能电网案例研究还是从现实世界的角度讨论了理论进步对复杂多智能体设置的影响。