Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Toolpath design for additive manufacturing using deep reinforcement
  learning   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.

10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas