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Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards., , , , , , , , , and . ICLR, OpenReview.net, (2020)End-to-End Robotic Reinforcement Learning without Reward Engineering., , , , and . CoRR, (2019)Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems, , , and . (2020)cite arxiv:2005.01643.Contextual Imagined Goals for Self-Supervised Robotic Learning., , , , , and . CoRL, volume 100 of Proceedings of Machine Learning Research, page 530-539. PMLR, (2019)Optimism-driven exploration for nonlinear systems., , , and . ICRA, page 3239-3246. IEEE, (2015)Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings., , , , , and . ICML, volume 80 of Proceedings of Machine Learning Research, page 1008-1017. PMLR, (2018)Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning., , , , and . ICLR (Poster), OpenReview.net, (2017)Simple and Effective VAE Training with Calibrated Decoders, , and . (2020)cite arxiv:2006.13202Comment: International Conference on Machine Learning (ICML), 2021. Project website is at https://orybkin.github.io/sigma-vae/.(CAD)$^2$RL: Real Single-Image Flight without a Single Real Image., and . CoRR, (2016)Reset-Free Guided Policy Search: Efficient Deep Reinforcement Learning with Stochastic Initial States., , , , and . CoRR, (2016)