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This work studies reinforcement learning in the Sim-to-Real setting, in which an agent is first trained on a number of simulators before being deployed in the real world, with the aim of decreasing the real-world sample complexity requirement.
u u -processes: Rates of convergence
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Michel Talagrand · 1996
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Jonathan Baxter · 2000
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On consistency of kernel density estimators for randomly censored data: rates holding uniformly over adaptive intervals
Evarist Giné and Armelle Guillou · 2001
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Rates of strong uniform consistency for multivariate kernel density estimators
Evarist Giné and Armelle Guillou · 2002
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Introduction to Nonparametric Estimation
Alexandre B. Tsybakov · 2009
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Gilles Blanchard, Gyemin Lee, and Clayton Scott · 2011
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Bharath Sriperumbudur and Ingo Steinwart · 2012
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Real-world reinforcement learning via multifidelity simulators
Mark Cutler, Thomas J Walsh, and Jonathan P How · 2015
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Reinforcement learning in rich-observation MDPs using spectral methods
Kamyar Azizzadenesheli, Alessandro Lazaric, and Animashree Anandkumar · 2016
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Pac reinforcement learning with rich observations
Akshay Krishnamurthy, Alekh Agarwal, and John Langford · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Uniform convergence rates for kernel density estimation
Using simulation and domain adaptation to improve efficiency of deep robotic grasping
Konstantinos Bousmalis, Alex Irpan, Paul Wohlhart, Yunfei Bai, Matthew Kelcey, Mrinal Kalakrishnan, Laura Downs, Julian Ibarz, Peter Pastor, Kurt Konolige, et al · 2018
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On oracle-efficient PAC RL with rich observations
Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, and Robert E Schapire · 2018
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PAC reinforcement learning with an imperfect model
Nan Jiang · 2018
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Uniform convergence rate of the kernel density estimator adaptive to intrinsic dimension
Jisu Kim, Jaehyeok Shin, Alessandro Rinaldo, and Larry Wasserman · 2018
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Sim-to-real transfer of robotic control with dynamics randomization
Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2018
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Heinrich Jiang · 2017
Cited alongside, same era.
Contextual decision processes with low Bellman rank are PAC-learnable
Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, and Robert E Schapire · 2017
Cited alongside, same era.
Domain randomization for transferring deep neural networks from simulation to the real world
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
Cited alongside, same era.
Simon S Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, and John Langford · 2019
Closest in time.
Model-based RL in contextual decision processes: PAC bounds and exponential improvements over model-free approaches
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, and John Langford · 2019
Closest in time.