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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)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/.Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning., , , and . CoRR, (2020)Optimal control with learned local models: Application to dexterous manipulation., , and . ICRA, page 378-383. IEEE, (2016)Self-Supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation., , , , and . ICRA, page 1-8. IEEE, (2018)Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight., , , , and . ICRA, page 6008-6014. IEEE, (2019)Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost., , , , and . ICRA, page 3651-3657. IEEE, (2019)Deep visual foresight for planning robot motion., and . ICRA, page 2786-2793. IEEE, (2017)Collective robot reinforcement learning with distributed asynchronous guided policy search., , , , and . IROS, page 79-86. IEEE, (2017)