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End-to-End Robotic Reinforcement Learning without Reward Engineering., , , , and . CoRR, (2019)Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards., , , , , , , , , and . ICLR, OpenReview.net, (2020)Training and Evaluation of Deep Policies Using Reinforcement Learning and Generative Models., , , , , , and . J. Mach. Learn. Res., (2022)Recovery RL: Safe Reinforcement Learning With Learned Recovery Zones., , , , , , , , , and . IEEE Robotics Autom. Lett., 6 (3): 4915-4922 (2021)Disentanglement via Latent Quantization., , , , and . CoRR, (2023)Reset-Free Guided Policy Search: Efficient Deep Reinforcement Learning with Stochastic Initial States., , , , and . CoRR, (2016)Deep visual foresight for planning robot motion., and . ICRA, page 2786-2793. IEEE, (2017)Generalizing Skills with Semi-Supervised Reinforcement Learning., , , , and . ICLR (Poster), OpenReview.net, (2017)Deep Online Learning Via Meta-Learning: Continual Adaptation for Model-Based RL., , and . ICLR (Poster), OpenReview.net, (2019)Permutation Equivariant Neural Functionals., , , , , , and . CoRR, (2023)