Deep Meta Reinforcement Learning for Rapid Adaptation In Linear Markov Decision Processes: Applications to CERN’s AWAKE Project

Author:

Hirlaender Simon,Pochaba Sabrina,Lukas Lamminger,Garcia Andrea Santamaria,Xu Chenran,Kaiser Jan,Eichler Annika,Kain Verena

Publisher

Springer Nature Switzerland

Reference20 articles.

1. Abdolmaleki, A., Springenberg, J., Tassa, Y., Munos, R., Heess, N., Riedmiller, M.: Maximum a posteriori policy optimisation. In: International Conference on Learning Representations (2018)

2. Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning, Vol. 70, pp. 1126–1135 (2017)

3. Frans, K., Ho, J., Chen, X., Abbeel, P., Schulman, J.: Meta Learning Shared Hierarchies. arXiv:1710.09767 (2017)

4. Fujimoto, S., Hoof, H., Meger, D.: Addressing function approximation error in actor-critic methods. In: International Conference on Machine Learning, pp. 1587–1596 (2018)

5. Golub, G., Van Loan, C.: Matrix Computations. Johns Hopkins Studies in the Mathematical Sciences. Johns Hopkins University Press (2013)

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