  In this paper we present a novel technique based on deep reinforcement
learning that allows for numerical analytic continuation of integrals that are
often encountered in one-loop diagrams in quantum field theory. In order to
extract certain quantities of two-point functions, such as spectral densities,
mass poles or multi-particle thresholds, it is necessary to perform an analytic
continuation of the correlator in question. At one-loop level in Euclidean
space, this results in the necessity to deform the integration contour of the
loop integral in the complex plane of the square of the loop momentum, in order
to avoid non-analyticities in the integration plane. Using a toy model for
which an exact solution is known, we train a reinforcement learning agent to
perform the required contour deformations. Our study shows great promise for an
agent to be deployed in iterative numerical approaches used to compute
non-perturbative 2-point functions, such as the quark propagator
Dyson-Schwinger equation, or more generally, Fredholm equations of the second
kind, in the complex domain.

High Energy Physics - Phenomenology;Machine Learning;Computational Physics