  We propose reinforcement learning to control the dynamical self-assembly of
the dodecagonal quasicrystal (DDQC) from patchy particles. The patchy particles
have anisotropic interactions with other particles and form DDQC. However,
their structures at steady states are significantly influenced by the kinetic
pathways of their structural formation. We estimate the best policy of
temperature control trained by the Q-learning method and demonstrate that we
can generate DDQC with few defects using the estimated policy. The temperature
schedule obtained by reinforcement learning can reproduce the desired structure
more efficiently than the conventional pre-fixed temperature schedule, such as
annealing. To clarify the success of the learning, we also analyse a simple
model describing the kinetics of structural changes through the motion in a
triple-well potential. We have found that reinforcement learning autonomously
discovers the critical temperature at which structural fluctuations enhance the
chance of forming a globally stable state. The estimated policy guides the
system toward the critical temperature to assist the formation of DDQC.
