Monte Carlo Tree Search (MCTS) has shown its strength for a lot of deterministic and stochastic examples, but literature lacks reports of applications to real world industrial processes. Common reasons for this are that there is no efficient simulator of the process available or there exist problems in applying MCTS to the complex rules of the process. In this paper, we apply MCTS for optimizing a high-precision manufacturing process that has stochastic and partially observable outcomes. We make use of an expert-knowledge-based simulator and adapt the MCTS default policy to deal with the manufacturing process.
Monte Carlo Tree Search for high precision manufacturing
Type: Inproceedings
Author: D. Weichert, F. Horchler, A. Kister, M. Trost, J. Hartung, S. Risse
Journal: RL4RL Workshop at ICML
Booktitle: Reinforcement Learning for Real Life Workshop (RL4RealLife) at the Conference on Neural Information Processing Systems (NeurIPS)
Year: 2021
Citation information
D. Weichert, F. Horchler, A. Kister, M. Trost, J. Hartung, S. Risse:
Monte Carlo Tree Search for high precision manufacturing.
Reinforcement Learning for Real Life Workshop (RL4RealLife) at the Conference on Neural Information Processing Systems (NeurIPS),
RL4RL Workshop at ICML,
2021,
https://doi.org/10.48550/arXiv.2108.01789