Publications

2022

L. Servadei, J. H. Lee, J. A. A. Medina, M. Werner, S. Hochreiter, W. Ecker, and R. Wille (2022) Deep Reinforcement Learning for Optimization at Early Design Stages. IEEE Design & Test, 2022-01-20. (more) (download)

2021

K. Schweighofer, M. Hofmarcher, M.-C. Dinu, P. Renz, A. Bitto-Nemling, V. Patil, and S. Hochreiter (2021) Understanding the Effects of Dataset Characteristics on Offline Reinforcement Learning. arXiv:2111.04714, 2021-11-08. (more) (download)

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)

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. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 13566;  e-print also at arXiv:1806.07857v3, 2019-09-10. (more) (download)

©2022 IARAI - INSTITUTE OF ADVANCED RESEARCH IN ARTIFICIAL INTELLIGENCE

Imprint | Privacy Policy

Stay in the know with developments at IARAI

We can let you know if there’s any

updates from the Institute.
You can later also tailor your news feed to specific research areas or keywords (Privacy)
Loading

Log in with your credentials

or    

Forgot your details?

Create Account