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Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control., , , , and . ICML, volume 80 of Proceedings of Machine Learning Research, page 4739-4748. PMLR, (2018)Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL., , and . CoRR, (2018)One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks., , , and . CoRR, (2018)End-to-End Training of Deep Visuomotor Policies., , , and . J. Mach. Learn. Res., (2016)Reasoning About Physical Interactions with Object-Oriented Prediction and Planning., , , , , and . ICLR (Poster), OpenReview.net, (2019)NoRML: No-Reward Meta Learning., , , , and . AAMAS, page 323-331. International Foundation for Autonomous Agents and Multiagent Systems, (2019)Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning., , , , , , and . CoRL, volume 100 of Proceedings of Machine Learning Research, page 1094-1100. PMLR, (2019)Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning., , , and . CoRR, (2020)Meta-Learning Symmetries by Reparameterization., , and . CoRR, (2020)Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods., , , , , and . ICRA, page 6284-6291. IEEE, (2018)