Reinforcement Learning For Spline-Based Shape Optimization Of Flow Channels In Profile Extrusion Dies
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In this contribution, we investigate the feasibility of Reinforcement Learning (RL) as learning-based algorithm for shape optimization. Reinforcement Learning is based on a trial-and-error interaction of an agent with an environment. For each action, the agent is informed about a reward and the subsequent state of the environment, but there is no information about long-term interests as classical optimization algorithms would provide. While not necessarily superior to classical, e.g., gradient-based, optimization algorithms for one single optimization problem, Reinforcement Learning techniques are expected to perform especially well on similar optimization tasks, since the agent learns a general strategy for solving a problem instead of just concentrating on the solution of a single problem.
