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Representation Learning for Out-of-distribution Generalization in Reinforcement Learning Learning

MPS-Authors
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Träuble,  Frederik
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Wüthrich,  Manuel
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Widmaier,  Felix
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Schölkopf,  Bernhard       
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Bauer,  Stefan
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;
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https://openreview.net/forum?id=I8rHTlfITWC
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Citation

Träuble, F., Dittadi, A., Wüthrich, M., Widmaier, F., Gehler, P., Winther, O., et al. (2021). Representation Learning for Out-of-distribution Generalization in Reinforcement Learning Learning. In ICML 2021 Workshop on Unsupervised Reinforcement Learning (ICML 2021). Amherst, MA: OpenReview.net. Retrieved from https://openreview.net/forum?id=I8rHTlfITWC.


Cite as: https://hdl.handle.net/21.11116/0000-000F-EAF2-8
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