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Towards robust and domain agnostic reinforcement learning competitions. <i>arXiv</i>. 2021. doi:<a href=\"https://doi.org/10.48550/ARXIV.2106.03748\">10.48550/ARXIV.2106.03748</a>","mla":"Guss, William Hebgen, et al. “Towards Robust and Domain Agnostic Reinforcement Learning Competitions.” <i>ArXiv</i>, arXiv, 2021, doi:<a href=\"https://doi.org/10.48550/ARXIV.2106.03748\">10.48550/ARXIV.2106.03748</a>.","short":"W.H. Guss, S. Milani, N. Topin, B. Houghton, S. Mohanty, A. Melnik, A. Harter, B. Buschmaas, B. Jaster, C. Berganski, D. Heitkamp, M. Henning, H. Ritter, C. Wu, X. Hao, Y. Lu, H. Mao, Y. Mao, C. Wang, M. Opanowicz, A. Kanervisto, Y. Schraner, C. Scheller, X. Zhou, L. Liu, D. Nishio, T. Tsuneda, K. Ramanauskas, G. Juceviciute, ArXiv (2021).","chicago":"Guss, William Hebgen, Stephanie Milani, Nicholay Topin, Brandon Houghton, Sharada Mohanty, Andrew Melnik, Augustin Harter, et al. “Towards Robust and Domain Agnostic Reinforcement Learning Competitions.” <i>ArXiv</i>, 2021. <a href=\"https://doi.org/10.48550/ARXIV.2106.03748\">https://doi.org/10.48550/ARXIV.2106.03748</a>.","apa":"Guss, W. H., Milani, S., Topin, N., Houghton, B., Mohanty, S., Melnik, A., … Juceviciute, G. (2021). Towards robust and domain agnostic reinforcement learning competitions. <i>ArXiv</i>. <a href=\"https://doi.org/10.48550/ARXIV.2106.03748\">https://doi.org/10.48550/ARXIV.2106.03748</a>","bibtex":"@article{Guss_Milani_Topin_Houghton_Mohanty_Melnik_Harter_Buschmaas_Jaster_Berganski_et al._2021, title={Towards robust and domain agnostic reinforcement learning competitions}, DOI={<a href=\"https://doi.org/10.48550/ARXIV.2106.03748\">10.48550/ARXIV.2106.03748</a>}, journal={arXiv}, publisher={arXiv}, author={Guss, William Hebgen and Milani, Stephanie and Topin, Nicholay and Houghton, Brandon and Mohanty, Sharada and Melnik, Andrew and Harter, Augustin and Buschmaas, Benoit and Jaster, Bjarne and Berganski, Christoph and et al.}, year={2021} }","alphadin":"<span style=\"font-variant:small-caps;\"><span style=\"font-variant:small-caps;\">Guss, William Hebgen</span> ; <span style=\"font-variant:small-caps;\">Milani, Stephanie</span> ; <span style=\"font-variant:small-caps;\">Topin, Nicholay</span> ; <span style=\"font-variant:small-caps;\">Houghton, Brandon</span> ; <span style=\"font-variant:small-caps;\">Mohanty, Sharada</span> ; <span style=\"font-variant:small-caps;\">Melnik, Andrew</span> ; <span style=\"font-variant:small-caps;\">Harter, Augustin</span> ; <span style=\"font-variant:small-caps;\">Buschmaas, Benoit</span> ; u. a.</span>: Towards robust and domain agnostic reinforcement learning competitions. In: <i>arXiv</i>, arXiv (2021)","ieee":"W. H. Guss <i>et al.</i>, “Towards robust and domain agnostic reinforcement learning competitions,” <i>arXiv</i>, 2021."},"status":"public","doi":"10.48550/ARXIV.2106.03748","publisher":"arXiv","date_updated":"2026-03-17T15:29:31Z","type":"journal_article","oa":"1","user_id":"263827","main_file_link":[{"open_access":"1"}],"publication":"arXiv","publication_status":"published","title":"Towards robust and domain agnostic reinforcement learning competitions","date_created":"2026-02-02T13:56:47Z"}]