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In reinforcement learning, pre-trained low-level skills have the potential to greatly facilitate exploration.
Learning and executing generalized robot plans
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MuJoCo: A physics engine for model-based control
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Swing-twist decomposition in Clifford algebra
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Adam: A Method for Stochastic Optimization
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Benchmarking Deep Reinforcement Learning for Continuous Control
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Reinforcement Learning with Parameterized Actions
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The Option-Critic Architecture
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Stochastic Neural Networks for Hierarchical Reinforcement Learning
C. Florensa, Y. Duan, and P. Abbeel · 2017
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Variational Intrinsic Control
K. Gregor, D. J. Rezende, and D. Wierstra · 2017
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Emergence of Locomotion Behaviours in Rich Environments
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A Laplacian Framework for Option Discovery in Reinforcement Learning
M. C. Machado, M. G. Bellemare, and M. Bowling · 2017
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Learning human behaviors from motion capture by adversarial imitation
J. Merel, Y. Tassa, D. TB, S. Srinivasan, J. Lemmon, Z. Wang, G. Wayne, and N. Heess · 2017
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Sub-policy Adaptation for Hierarchical Reinforcement Learning
A. Li, C. Florensa, I. Clavera, and P. Abbeel · 2019
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Hierarchical RL Using an Ensemble of Proprioceptive Periodic Policies
K. Marino, A. Gupta, R. Fergus, and A. Szlam · 2019
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Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?
O. Nachum, H. Tang, X. Lu, S. Gu, H. Lee, and S. Levine · 2019
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MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies
X. B. Peng, M. Chang, G. Zhang, P. Abbeel, and S. Levine · 2019
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Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills
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Catch & Carry: Reusable Neural Controllers for Vision-Guided Whole-Body Tasks
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H. Tang, R. Houthooft, D. Foote, A. Stooke, X. Chen, Y. Duan, J. Schulman, F. De Turck, and P. Abbeel · 2017
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FeUdal Networks for Hierarchical Reinforcement Learning
A. S. Vezhnevets, S. Osindero, T. Schaul, N. Heess, M. Jaderberg, D. Silver, and K. Kavukcuoglu · 2017
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Variational Option Discovery Algorithms
J. Achiam, H. Edwards, D. Amodei, and P. Abbeel · 2018
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Divide-and-Conquer Reinforcement Learning
D. Ghosh, A. Singh, A. Rajeswaran, V. Kumar, and S. Levine · 2018
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Soft Actor-Critic Algorithms and Applications
T. Haarnoja, A. Zhou, K. Hartikainen, G. Tucker, S. Ha, J. Tan, V. Kumar, H. Zhu, A. Gupta, P. Abbeel, and S. Levine · 2018
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Learning an Embedding Space for Transferable Robot Skills
K. Hausman, J. T. Springenberg, Z. Wang, N. Heess, and M. Riedmiller · 2018
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Data-Efficient Hierarchical Reinforcement Learning
O. Nachum, S. S. Gu, H. Lee, and S. Levine · 2018
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J. Merel, S. Tunyasuvunakool, A. Ahuja, Y. Tassa, L. Hasenclever, V. Pham, T. Erez, G. Wayne, and N. Heess · 2020
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Accelerating Reinforcement Learning with Learned Skill Priors
K. Pertsch, Y. Lee, and J. J. Lim · 2020
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Composing Task-Agnostic Policies with Deep Reinforcement Learning
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Learning Robot Skills with Temporal Variational Inference
T. Shankar and A. Gupta · 2020
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Dynamics-Aware Unsupervised Discovery of Skills
A. Sharma, S. Gu, S. Levine, V. Kumar, and K. Hausman · 2020
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D2RL: Deep Dense Architectures in Reinforcement Learning
S. Sinha, H. Bharadhwaj, A. Srinivas, and A. Garg · 2020
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Dm_control: Software and Tasks for Continuous Control
Y. Tassa, S. Tunyasuvunakool, A. Muldal, Y. Doron, P. Trochim, S. Liu, S. Bohez, J. Merel, T. Erez, T. Lillicrap, and N. Heess · 2020
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Dynamics-aware Embeddings
W. Whitney, R. Agarwal, K. Cho, and A. Gupta · 2020
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Hierarchical Reinforcement Learning by Discovering Intrinsic Options
J. Zhang, H. Yu, and W. Xu · 2020
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OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning
A. Ajay, A. Kumar, P. Agrawal, S. Levine, and O. Nachum · 2021
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Planning in Learned Latent Action Spaces for Generalizable Legged Locomotion
T. Li, R. Calandra, D. Pathak, Y. Tian, F. Meier, and A. Rai · 2021
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