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What Can Learned Intrinsic Rewards Capture?

, , , , , , , и . ICML, том 119 из Proceedings of Machine Learning Research, стр. 11436-11446. PMLR, (2020)

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Andreas Heßelmann

Peter Heßelmann

Benjamin Heßeler

Dominic Heßel

Michael Andreas Heßelmann

 

Другие публикации лиц с тем же именем

Machine Learning for Parameter Screening in Computer Simulations., , и . MESAS, том 8906 из Lecture Notes in Computer Science, стр. 308-320. Springer, (2014)Rainbow: Combining Improvements in Deep Reinforcement Learning, , , , , , , , , и . (2017)cite arxiv:1710.02298Comment: Under review as a conference paper at AAAI 2018.Rainbow: Combining Improvements in Deep Reinforcement Learning., , , , , , , , , и . AAAI, стр. 3215-3222. AAAI Press, (2018)Discovery of Options via Meta-Learned Subgoals., , , , , , , , и . NeurIPS, стр. 29861-29873. (2021)Automatic Tuning of Computational Models., , , и . SIMULTECH (Selected Papers), том 402 из Advances in Intelligent Systems and Computing, стр. 43-64. Springer, (2014)When to use parametric models in reinforcement learning?, , и . NeurIPS, стр. 14322-14333. (2019)Discovery of Useful Questions as Auxiliary Tasks., , , , , , , , и . NeurIPS, стр. 9306-9317. (2019)Learning by Directional Gradient Descent., , , , и . ICLR, OpenReview.net, (2022)The Predictron: End-To-End Learning and Planning., , , , , , , , , и 1 other автор(ы). ICML, том 70 из Proceedings of Machine Learning Research, стр. 3191-3199. PMLR, (2017)Dueling Network Architectures for Deep Reinforcement Learning., , , , , и . ICML, том 48 из JMLR Workshop and Conference Proceedings, стр. 1995-2003. JMLR.org, (2016)