  Advancements in additive manufacturing have enabled design and fabrication of
materials and structures not previously realizable. In particular, the design
space of composite materials and structures has vastly expanded, and the
resulting size and complexity has challenged traditional design methodologies,
such as brute force exploration and one factor at a time (OFAT) exploration, to
find optimum or tailored designs. To address this challenge, supervised machine
learning approaches have emerged to model the design space using curated
training data; however, the selection of the training data is often determined
by the user. In this work, we develop and utilize a Reinforcement learning
(RL)-based framework for the design of composite structures which avoids the
need for user-selected training data. For a 5 $\times$ 5 composite design space
comprised of soft and compliant blocks of constituent material, we find that
using this approach, the model can be trained using 2.78% of the total design
space consists of $2^{25}$ design possibilities. Additionally, the developed
RL-based framework is capable of finding designs at a success rate exceeding
90%. The success of this approach motivates future learning frameworks to
utilize RL for the design of composites and other material systems.
