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In ICML. 1329--1338."},{"key":"e_1_3_2_2_6_1","volume-title":"RL2: Fast reinforcement learning via slow reinforcement learning. arXiv preprint arXiv:1611.02779","author":"Duan Yan","year":"2016","unstructured":"Yan Duan , John Schulman , Xi Chen , Peter L Bartlett , Ilya Sutskever , and Pieter Abbeel . 2016. RL2: Fast reinforcement learning via slow reinforcement learning. arXiv preprint arXiv:1611.02779 ( 2016 ). Yan Duan, John Schulman, Xi Chen, Peter L Bartlett, Ilya Sutskever, and Pieter Abbeel. 2016. RL2: Fast reinforcement learning via slow reinforcement learning. arXiv preprint arXiv:1611.02779 (2016)."},{"key":"e_1_3_2_2_7_1","unstructured":"Chelsea Finn Pieter Abbeel and Sergey Levine. 2017. Model-agnostic metalearning for fast adaptation of deep networks. In ICML. 1126--1135.  Chelsea Finn Pieter Abbeel and Sergey Levine. 2017. Model-agnostic metalearning for fast adaptation of deep networks. In ICML. 1126--1135."},{"key":"e_1_3_2_2_8_1","unstructured":"Carlos Florensa David Held Markus Wulfmeier Michael Zhang and Pieter Abbeel. 2017. Reverse Curriculum Generation for Reinforcement Learning. In CoRL. 482--495.  Carlos Florensa David Held Markus Wulfmeier Michael Zhang and Pieter Abbeel. 2017. Reverse Curriculum Generation for Reinforcement Learning. In CoRL. 482--495."},{"key":"e_1_3_2_2_9_1","unstructured":"Justin Fu Sergey Levine and Pieter Abbeel. 2016. One-shot learning of manipulation skills with online dynamics adaptation and neural network priors. In IROS. 4019--4026.  Justin Fu Sergey Levine and Pieter Abbeel. 2016. One-shot learning of manipulation skills with online dynamics adaptation and neural network priors. 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In AAMAS. 566--574."},{"key":"e_1_3_2_2_20_1","unstructured":"Andrew Y Ng Daishi Harada and Stuart Russell. 1999. Policy invariance under reward transformations: Theory and application to reward shaping. In ICML. 278--287.  Andrew Y Ng Daishi Harada and Stuart Russell. 1999. Policy invariance under reward transformations: Theory and application to reward shaping. In ICML. 278--287."},{"key":"e_1_3_2_2_21_1","unstructured":"Martin Riedmiller Roland Hafner Thomas Lampe Michael Neunert Jonas Degrave Tom Wiele Vlad Mnih Nicolas Heess and Jost Tobias Springenberg. 2018. Learning by Playing Solving Sparse Reward Tasks from Scratch. In ICML. 4344--4353.  Martin Riedmiller Roland Hafner Thomas Lampe Michael Neunert Jonas Degrave Tom Wiele Vlad Mnih Nicolas Heess and Jost Tobias Springenberg. 2018. Learning by Playing Solving Sparse Reward Tasks from Scratch. In ICML. 4344--4353."},{"key":"e_1_3_2_2_22_1","unstructured":"Stephane Ross Brahim Chaib-draa and Joelle Pineau. 2007. 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