  Motivated by the recent success of reinforcement learning in games such as Go
and Dota2, we formulate Bell non-local games as a reinforcement learning
problem. Such a formulation helps us to explore Bell non-locality in a range of
scenarios. The measurement settings and the quantum states are selected by the
learner randomly in the beginning. Still, eventually, it starts understanding
the underlying patterns and discovers an optimal (or near-optimal) quantum
configuration corresponding to the task at hand. We provide a proof of
principle approach to learning quantum configurations for violating various
Bell inequalities. The algorithm also works for non-convex optimization
problems where convex methods fail, thus offering a possibility to explore
optimal quantum configurations for Bell inequalities corresponding to large
quantum networks. We also implement a hybrid quantum-classical variational
algorithm with reinforcement learning.
