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Publications

2020

M. Holzleitner, L. Gruber, J. Arjona-Medina, J. Brandstetter, and S. Hochreiter (2020) Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER. arXiv:2012.01399, 2020-12-02. (more) (download)

L. Servadei, J. Zheng, J. Arjona-Medina, M. Werner, V. Esen, S. Hochreiter, W. Ecker, and R. Wille (2020) Cost Optimization at Early Stages of Design Using Deep Reinforcement Learning. Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD, 37-42, 2020-11-16. (more) (download)

T. Adler, J. Brandstetter, M. Widrich, A. Mayr, D. Kreil, M. Kopp, G. Klambauer, and S. Hochreiter (2020) Cross-Domain Few-Shot Learning by Representation Fusion. arXiv:2010.06498, 2020-10-13. (more) (download)

V. P. Patil, M. Hofmarcher, M.-C. Dinu, M. Dorfer, P. M. Blies, J. Brandstetter, J. A. Arjona-Medina, and S. Hochreiter (2020) Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution. arXiv:2009.14108, 2020-09-29. (more) (download)

2019

J. A. Arjona-Medina, M. Gillhofer, M. Widrich, T. Unterthiner, J. Brandstetter, and S. Hochreiter (2019) RUDDER – Return Decomposition with Delayed Rewards. NeurIPS 2019, Vancouver, 10-12 Dec 2019, or pre-print on arXiv, 1806.07857v3, Machine Learning (cs.LG), 2019-09-10. (more) (download)

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