  A major goal of materials design is to find material structures with desired
properties and in a second step to find a processing path to reach one of these
structures. In this paper, we propose and investigate a deep reinforcement
learning approach for the optimization of processing paths. The goal is to find
optimal processing paths in the material structure space that lead to
target-structures, which have been identified beforehand to result in desired
material properties. There exists a target set containing one or multiple
different structures. Our proposed methods can find an optimal path from a
start structure to a single target structure, or optimize the processing paths
to one of the equivalent target-structures in the set. In the latter case, the
algorithm learns during processing to simultaneously identify the best
reachable target structure and the optimal path to it. The proposed methods
belong to the family of model-free deep reinforcement learning algorithms. They
are guided by structure representations as features of the process state and by
a reward signal, which is formulated based on a distance function in the
structure space. Model-free reinforcement learning algorithms learn through
trial and error while interacting with the process. Thereby, they are not
restricted to information from a priori sampled processing data and are able to
adapt to the specific process. The optimization itself is model-free and does
not require any prior knowledge about the process itself. We instantiate and
evaluate the proposed methods by optimizing paths of a generic metal forming
process. We show the ability of both methods to find processing paths leading
close to target structures and the ability of the extended method to identify
target-structures that can be reached effectively and efficiently and to focus
on these targets for sample efficient processing path optimization.
