  Machine learning, the core of artificial intelligence and big data science,
is one of today's most rapidly growing interdisciplinary fields. Recently, its
tools and techniques have been adopted to tackle intricate quantum many-body
problems. In this work, we introduce machine learning techniques to the
detection of quantum nonlocality in many-body systems, with a focus on the
restricted-Boltzmann-machine (RBM) architecture. Using reinforcement learning,
we demonstrate that RBM is capable of finding the maximum quantum violations of
multipartite Bell inequalities with given measurement settings. Our results
build a novel bridge between computer-science-based machine learning and
quantum many-body nonlocality, which will benefit future studies in both areas.
