Real time evolution in QFT poses a severe sign problem, which may be alleviated via a complex
Langevin approach. However, so far simulation results consistently fail to converge with a large
real-time extent. A kernel in a complex Langevin equation is known to influence the appearance
of the boundary terms and integration cycles, and thus kernel choice can improve the range of
real-time extents with correct results. For multi-dimensional models the optimal kernel is searched
for using machine learning methods. We test this approach by simulating the simplest possible case,
a 0+1-dimensional scalar field theory in Minkowski space. The performance of band-diagonal
kernels as well as the existence of integration cycles in the theory is also discussed.