Model-based offline reinforcement learning framework for optimizing tunnel boring machine operation

Author:

Cao Yupeng,Luo WeiORCID,Xue Yadong,Lin WeirenORCID,Zhang Feng

Publisher

Elsevier BV

Reference98 articles.

1. Application of several optimization techniques for estimating TBM advance rate in granitic rocks;Armaghani;Journal of Rock Mechanics and Geotechnical Engineering,2019

2. Bansal, S., Calandra, R., Chua, K., Levine, S., & Tomlin, C. (2017). MBMF: Model-based priors for model-free reinforcement learning [preprint]. Arxiv. https://doi.org/arXiv:1709.03153.

3. Belletti, F., Beutel, A., Jain, S., & Chi, E. (2018). Factorized recurrent neural architectures for longer range. In Dependence proceedings of the twenty-first international conference on artificial intelligence and statistics, Proceedings of Machine Learning Research. https://proceedings.mlr.press/v84/belletti18a.html.

4. Berner, C., Brockman, G., Chan, B., Cheung, V., Dębiak, P., Dennison, C., Farhi, D., Fischer, Q., Hashme, S., Hesse, C., Józefowicz, R., Gray, S., Olsson, C., Pachocki, J., Petrov, M., Pinto, H., Raiman, J., Salimans, T., Schlatter, J., & Zhang, S. (2019). Dota 2 with Large Scale Deep Reinforcement Learning. Arxiv. https://doi.org/10.48550/arXiv.1912.06680.

5. Real-time hard-rock tunnel prediction model for rock mass classification using CatBoost integrated with Sequential Model-Based Optimization;Bo;Tunnelling and Underground Space Technology,2022

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