  We reimplement here the recent approach of Adam Zsolt Wagner
[arXiv:2104.14516], which applies reinforcement learning to construct
(counter)examples in graph theory, in order to make it more readable, more
stable and much faster. The presented concepts are illustrated by constructing
counterexamples for a number of published conjectured bounds for the Laplacian
spectral radius of graphs.
