  Toolpath optimization of metal-based additive manufacturing processes is
currently hampered by the high-dimensionality of its design space. In this
work, a reinforcement learning platform is proposed that dynamically learns
toolpath strategies to build an arbitrary part. To this end, three prominent
model-free reinforcement learning formulations are investigated to design
additive manufacturing toolpaths and demonstrated for two cases of dense and
sparse reward structures. The results indicate that this learning-based
toolpath design approach achieves high scores, especially when a dense reward
structure is present.
