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Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning., , , , , and . CoRL, volume 155 of Proceedings of Machine Learning Research, page 2120-2136. PMLR, (2020)BridgeData V2: A Dataset for Robot Learning at Scale., , , , , , , , , and 4 other author(s). CoRL, volume 229 of Proceedings of Machine Learning Research, page 1723-1736. PMLR, (2023)REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation., , , , , and . CoRL, volume 229 of Proceedings of Machine Learning Research, page 1930-1949. PMLR, (2023)Scaling Up Multi-Task Robotic Reinforcement Learning., , , , , , , and . CoRL, volume 164 of Proceedings of Machine Learning Research, page 557-575. PMLR, (2021)AW-Opt: Learning Robotic Skills with Imitation andReinforcement at Scale., , , , , , , , , and 1 other author(s). CoRL, volume 164 of Proceedings of Machine Learning Research, page 1078-1088. PMLR, (2021)SFV: reinforcement learning of physical skills from videos., , , , and . ACM Trans. Graph., 37 (6): 178 (2018)DeepMimic: example-guided deep reinforcement learning of physics-based character skills., , , and . ACM Trans. Graph., 37 (4): 143 (2018)Feature Construction for Inverse Reinforcement Learning., , and . NIPS, page 1342-1350. Curran Associates, Inc., (2010)Nonlinear Inverse Reinforcement Learning with Gaussian Processes., , and . NIPS, page 19-27. (2011)Variational Policy Search via Trajectory Optimization., and . NIPS, page 207-215. (2013)