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A key challenge in reinforcement learning (RL) is environment generalization: a policy trained to solve a task in one environment often fails to solve the same task in a slightly different test environment.
On finding ’exciting’ trajectories for identification experiments involving systems with non-linear dynamics
B. Armstrong · 1987
Earlier work this paper cites.
Reinforcement learning: A survey
Leslie Pack Kaelbling, Michael L Littman, and Andrew W Moore · 1996
Earlier work this paper cites.
Reinforcement learning: An introduction , volume 1
Richard S Sutton and Andrew G Barto · 1998
Earlier work this paper cites.
Nonlinear system identification using coevolution of models and tests
Josh C Bongard and Hod Lipson · 2005
Earlier work this paper cites.
Intrinsically motivated reinforcement learning
Nuttapong Chentanez, Andrew G Barto, and Satinder P Singh · 2005
Earlier work this paper cites.
System identification without lennart ljung: what would have been different?
Michel Gevers et al · 2006
Earlier work this paper cites.
Learning optimal adaptation strategies in unpredictable motor tasks
Daniel A Braun, Ad Aertsen, Daniel M Wolpert, and Carsten Mehring · 2009
Earlier work this paper cites.
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms
Brian Kulis, Kate Saenko, and Trevor Darrell · 2011
Earlier work this paper cites.
Learning with augmented features for heterogeneous domain adaptation
Lixin Duan, Dong Xu, and Ivor Tsang · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
Earlier work this paper cites.
Bounding approaches to system identification
Mario Milanese, John Norton, Hélène Piet-Lahanier, and Éric Walter · 2013
Earlier work this paper cites.
Lsda: Large scale detection through adaptation
Judy Hoffman, Sergio Guadarrama, Eric S Tzeng, Ronghang Hu, Jeff Donahue, Ross Girshick, Trevor Darrell, and Kate Saenko · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Deep domain confusion: Maximizing for domain invariance
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
Cited alongside, same era.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
Cited alongside, same era.
Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael I Jordan · 2015
Cited alongside, same era.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Later among the works it cites.
Learning invariant feature spaces to transfer skills with reinforcement learning
Abhishek Gupta, Coline Devin, YuXuan Liu, Pieter Abbeel, and Sergey Levine · 2017
Later among the works it cites.
Transferring end-to-end visuomotor control from simulation to real world for a multi-stage task
Stephen James, Andrew J Davison, and Edward Johns · 2017
Later among the works it cites.
Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell · 2017
Later among the works it cites.
Sim-to-real transfer of robotic control with dynamics randomization
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Trust region policy optimization
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Learning to perform physics experiments via deep reinforcement learning
Misha Denil, Pulkit Agrawal, Tejas D Kulkarni, Tom Erez, Peter Battaglia, and Nando de Freitas · 2016
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Revisiting batch normalization for practical domain adaptation
Yanghao Li, Naiyan Wang, Jianping Shi, Jiaying Liu, and Xiaodi Hou · 2016
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Sim-to-real robot learning from pixels with progressive nets
Andrei A Rusu, Matej Vecerik, Thomas Rothörl, Nicolas Heess, Razvan Pascanu, and Raia Hadsell · 2016
Cited alongside, same era.
(cad)2rl: Real single-image flight without a single real image
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Cited alongside, same era.
Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Cited alongside, same era.
Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2017
Later among the works it cites.
Epopt: Learning robust neural network policies using model ensembles
Aravind Rajeswaran, Sarvjeet Ghotra, Sergey Levine, and Balaraman Ravindran · 2017
Later among the works it cites.
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
Later among the works it cites.
Learning to adapt: Meta-learning for model-based control
Ignasi Clavera, Anusha Nagabandi, Ronald S Fearing, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2018
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Learning an embedding space for transferable robot skills
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Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A Efros, and Trevor Darrell · 2018
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Deepmimic: Example-guided deep reinforcement learning of physics-based character skills
Xue Bin Peng, Pieter Abbeel, Sergey Levine, and Michiel van de Panne · 2018
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Meta reinforcement learning with latent variable gaussian processes
Steindór Sæmundsson, Katja Hofmann, and Marc Peter Deisenroth · 2018
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One-shot imitation from observing humans via domain-adaptive meta-learning
Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, Pieter Abbeel, and Sergey Levine · 2018
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