  Profile extrusion is a continuous production process for manufacturing
plastic profiles from molten polymer. Especially interesting is the design of
the die, through which the melt is pressed to attain the desired shape.
However, due to an inhomogeneous velocity distribution at the die exit or
residual stresses inside the extrudate, the final shape of the manufactured
part often deviates from the desired one. To avoid these deviations, the shape
of the die can be computationally optimized, which has already been
investigated in the literature using classical optimization approaches.
  A new approach in the field of shape optimization is the utilization of
Reinforcement Learning (RL) as a learning-based optimization algorithm. RL is
based on trial-and-error interactions of an agent with an environment. For each
action, the agent is rewarded and informed about the subsequent state of the
environment. While not necessarily superior to classical, e.g., gradient-based
or evolutionary, optimization algorithms for one single problem, RL techniques
are expected to perform especially well when similar optimization tasks are
repeated since the agent learns a more general strategy for generating optimal
shapes instead of concentrating on just one single problem.
  In this work, we investigate this approach by applying it to two 2D test
cases. The flow-channel geometry can be modified by the RL agent using
so-called Free-Form Deformation, a method where the computational mesh is
embedded into a transformation spline, which is then manipulated based on the
control-point positions. In particular, we investigate the impact of utilizing
different agents on the training progress and the potential of wall time saving
by utilizing multiple environments during training.
