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Reinforcement learning (RL) algorithms hold the promise of enabling autonomous skill acquisition for robotic systems.
The cross-entropy method for combinatorial and continuous optimization
R. Rubinstein · 1999
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Reinforcement learning by reward-weighted regression for operational space control
J. Peters and S. Schaal · 2007
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Multi-task reinforcement learning: a hierarchical bayesian approach
A. Wilson, A. Fern, S. Ray, and P. Tadepalli · 2007
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Learning compound multi-step controllers under unknown dynamics
W. Han, S. Levine, and P. Abbeel · 2015
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Actor-mimic: Deep multitask and transfer reinforcement learning
E. Parisotto, J. L. Ba, and R. Salakhutdinov · 2015
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Continuous control with deep reinforcement learning
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra · 2015
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Shapenet: An information-rich 3d model repository
A. X. Chang, T. A. Funkhouser, L. J. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu · 2015
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Pybullet, a python module for physics simulation for games, robotics and machine learning
E. Coumans and Y. Bai · 2016
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Leave no trace: Learning to reset for safe and autonomous reinforcement learning
B. Eysenbach, S. Gu, J. Ibarz, and S. Levine · 2017
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Distral: Robust multitask reinforcement learning
Y. W. Teh, V. Bapst, W. M. Czarnecki, J. Quan, J. Kirkpatrick, R. Hadsell, N. Heess, and R. Pascanu · 2017
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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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Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation
D. Kalashnikov, A. Irpan, P. Pastor, J. Ibarz, A. Herzog, E. Jang, D. Quillen, E. Holly, M. Kalakrishnan, V. Vanhoucke, et al · 2018
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Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
L. Espeholt, H. Soyer, R. Munos, K. Simonyan, V. Mnih, T. Ward, Y. Doron, V. Firoiu, T. Harley, I. Dunning, et al · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine · 2018
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Behavior regularized offline reinforcement learning
Y. Wu, G. Tucker, and O. Nachum · 2019
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Off-policy deep reinforcement learning without exploration
S. Fujimoto, D. Meger, and D. Precup · 2019
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Stabilizing off-policy q-learning via bootstrapping error reduction
A. Kumar, J. Fu, G. Tucker, and S. Levine · 2019
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Advantage-weighted regression: Simple and scalable off-policy reinforcement learning
X. B. Peng, A. Kumar, G. Zhang, and S. Levine · 2019
Cited alongside, same era.
Multi-task deep reinforcement learning with popart
M. Hessel, H. Soyer, L. Espeholt, W. Czarnecki, S. Schmitt, and H. van Hasselt · 2019
Cited alongside, same era.
Reset-free lifelong learning with skill-space planning
K. Lu, A. Grover, P. Abbeel, and I. Mordatch · 2020
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Learning to walk in the real world with minimal human effort
S. Ha, P. Xu, Z. Tan, S. Levine, and J. Tan · 2020
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Leveraging procedural generation to benchmark reinforcement learning
K. Cobbe, C. Hesse, J. Hilton, and J. Schulman · 2020
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Mt-opt: Continuous multi-task robotic reinforcement learning at scale
D. Kalashnikov, J. Varley, Y. Chebotar, B. Swanson, R. Jonschkowski, C. Finn, S. Levine, and K. Hausman · 2021
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Autonomous reinforcement learning via subgoal curricula
A. Sharma, A. Gupta, S. Levine, K. Hausman, and C. Finn · 2021
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Efficient off-policy meta-reinforcement learning via probabilistic context variables
K. Rakelly, A. Zhou, C. Finn, S. Levine, and D. Quillen · 2019
Cited alongside, same era.
Accelerating online reinforcement learning with offline datasets
A. Nair, M. Dalal, A. Gupta, and S. Levine · 2020
Cited alongside, same era.
The ingredients of real-world robotic reinforcement learning
H. Zhu, J. Yu, A. Gupta, D. Shah, K. Hartikainen, A. Singh, V. Kumar, and S. Levine · 2020
Cited alongside, same era.
Continual learning of control primitives: Skill discovery via reset-games
K. Xu, S. Verma, C. Finn, and S. Levine · 2020
Cited alongside, same era.
Z. Wang, A. Novikov, K. Zolna, J. T. Springenberg, S. Reed, B. Shahriari, N. Siegel, J. Merel, C. Gulcehre, N. Heess, et al · 2020
Cited alongside, same era.
Conservative q-learning for offline reinforcement learning
A. Kumar, A. Zhou, G. Tucker, and S. Levine · 2020
Cited alongside, same era.
Gradient surgery for multi-task learning
T. Yu, S. Kumar, A. Gupta, S. Levine, K. Hausman, and C. Finn · 2020
Cited alongside, same era.
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A minimalist approach to offline reinforcement learning
S. Fujimoto and S. S. Gu · 2021
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Multi-task reinforcement learning with context-based representations
S. Sodhani, A. Zhang, and J. Pineau · 2021
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Conservative data sharing for multi-task offline reinforcement learning
T. Yu, A. Kumar, Y. Chebotar, K. Hausman, S. Levine, and C. Finn · 2021
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Autonomous reinforcement learning: Formalism and benchmarking
A. Sharma, K. Xu, N. Sardana, A. Gupta, K. Hausman, S. Levine, and C. Finn · 2021
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A. Gupta, J. Yu, T. Z. Zhao, V. Kumar, A. Rovinsky, K. Xu, T. Devlin, and S. Levine · 2021
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Why generalization in rl is difficult: Epistemic pomdps and implicit partial observability
D. Ghosh, J. Rahme, A. Kumar, A. Zhang, R. P. Adams, and S. Levine · 2021
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Learning to see before learning to act: Visual pre-training for manipulation
Y. Lin, A. Zeng, S. Song, P. Isola, and T. Lin · 2021
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Bootstrapped autonomous practicing via multi-task reinforcement learning
A. Gupta, C. Lynch, B. Kinman, G. Peake, S. Levine, and K. Hausman · 2022
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