  Though robustness of networks to random attacks has been widely studied,
intentional destruction by an intelligent agent is not tractable with previous
methods. Here we devise a single-player game on a lattice that mimics the logic
of an attacker attempting to destroy a network. The objective of the game is to
disable all nodes in the fewest number of steps. We develop a reinforcement
learning approach using deep Q-learning that is capable of learning to play
this game successfully, and in so doing, to optimally attack a network. Because
the learning algorithm is universal, we train agents on different definitions
of robustness and compare the learned strategies. We find that superficially
similar definitions of robustness induce different strategies in the trained
agent, implying that optimally attacking or defending a network is sensitive
the particular objective. Our method provides a new approach to understand
network robustness, with potential applications to other discrete processes in
disordered systems.
