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We present Residual Policy Learning (RPL): a simple method for improving nondifferentiable policies using model-free deep reinforcement learning.
Integrated architectures for learning, planning, and reacting based on approximating dynamic programming
Richard S Sutton · 1990
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Automatic gait optimization with Gaussian process regression
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Using response surfaces and expected improvement to optimize snake robot gait parameters
Matthew Tesch, Jeff Schneider, and Howie Choset · 2011
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Gaussian processes for data-efficient learning in robotics and control
Marc Peter Deisenroth, Dieter Fox, and Carl Edward Rasmussen · 2015
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Learning continuous control policies by stochastic value gradients
Nicolas Heess, Gregory Wayne, David Silver, Timothy Lillicrap, Tom Erez, and Yuval Tassa · 2015
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Physically consistent state estimation and system identification for contacts
Svetoslav Kolev and Emanuel Todorov · 2015
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Continuous deep Q-learning with model-based acceleration
Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, and Sergey Levine · 2016
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Combining model-based policy search with online model learning for control of physical humanoids
Igor Mordatch, Nikhil Mishra, Clemens Eppner, and Pieter Abbeel · 2016
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Automatic LQR tuning based on Gaussian process global optimization
Alonso Marco, Philipp Hennig, Jeannette Bohg, Stefan Schaal, and Sebastian Trimpe · 2016
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Continuous control with deep reinforcement learning
Scalable deep reinforcement learning for vision-based robotic manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, et al · 2018
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Learning synergies between pushing and grasping with self-supervised deep reinforcement learning
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Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine · 2018
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Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning
Anusha Nagabandi, Gregory Kahn, Ronald S Fearing, and Sergey Levine · 2018
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Temporal difference models: Model-free deep rl for model-based control
Vitchyr Pong, Shixiang Gu, Murtaza Dalal, and Sergey Levine · 2018
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Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2016
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One-shot visual imitation learning via meta-learning
Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, and Sergey Levine · 2017
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Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap, and Sergey Levine · 2017
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Hindsight experience replay
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, OpenAI Pieter Abbeel, and Wojciech Zaremba · 2017
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Virtual vs. real: Trading off simulations and physical experiments in reinforcement learning with bayesian optimization
Alonso Marco, Felix Berkenkamp, Philipp Hennig, Angela P Schoellig, Andreas Krause, Stefan Schaal, and Sebastian Trimpe · 2017
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Combining learned and analytical models for predicting action effects
Alina Kloss, Stefan Schaal, and Jeannette Bohg · 2017
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Active model learning and diverse action sampling for task and motion planning
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End-to-end differentiable physics for learning and control
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Residual reinforcement learning for robot control
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Multi-goal reinforcement learning: Challenging robotics environments and request for research
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Recurrent experience replay in distributed reinforcement learning
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