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Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design., , , , и . ICML, том 97 из Proceedings of Machine Learning Research, стр. 2722-2730. PMLR, (2019)Preferences Implicit in the State of the World., , , , и . ICLR (Poster), OpenReview.net, (2019)Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model., , , и . CoRR, (2019)rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch., и . CoRR, (2019)Learning Factor Graphs in Polynomial Time and Sample Complexity., , и . J. Mach. Learn. Res., (2006)End-to-End Training of Deep Visuomotor Policies., , , и . J. Mach. Learn. Res., (2016)Overcoming Exploration in Reinforcement Learning with Demonstrations., , , , и . ICRA, стр. 6292-6299. IEEE, (2018)Learning contact-rich manipulation skills with guided policy search., , и . ICRA, стр. 156-163. IEEE, (2015)Deep learning helicopter dynamics models., и . ICRA, стр. 3223-3230. IEEE, (2015)Reset-free guided policy search: Efficient deep reinforcement learning with stochastic initial states., , , , и . ICRA, стр. 3373-3380. IEEE, (2017)