  We propose a bottom-up approach, based on Reinforcement Learning, to the
design of a chain achieving efficient excitation-transfer performances. We
assume distance-dependent interactions among particles arranged in a chain
under tight-binding conditions. Starting from two particles and a localised
excitation, we gradually increase the number of constitutents of the system so
as to improve the transfer probability. We formulate the problem of finding the
optimal locations and numbers of particles as a Markov Decision Process: we use
Proximal Policy Optimization to find the optimal chain-building policies and
the optimal chain configurations under different scenarios. We consider both
the case in which the target is a sink connected to the end of the chain and
the case in which the target is the right-most particle in the chain. We
address the problem of disorder in the chain induced by particle positioning
errors. We are able to achieve extremely high excitation transfer in all cases,
with different chain configurations and properties depending on the specific
conditions.
