  By learning the optimal policy with a double deep Q-learning network, we
design ultra-broadband, biomimetic, perfect absorbers with various materials,
based the structure of a moths eye. All absorbers achieve over 90% average
absorption from 400 to 1,600 nm. By training a DDQN with motheye structures
made up of chromium, we transfer the learned knowledge to other, similar
materials to quickly and efficiently find the optimal parameters from the
around 1 billion possible options. The knowledge learned from previous
optimisations helps the network to find the best solution for a new material in
fewer steps, dramatically increasing the efficiency of finding designs with
ultra-broadband absorption.
