Fetching the paper…
Reading the bibliography…
Humans often learn how to perform tasks via imitation: they observe others perform a task, and then very quickly infer the appropriate actions to take based on their observations.
Learning from demonstration
Stefan Schaal · 1997
Earlier work this paper cites.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 1998
Earlier work this paper cites.
A framework for behavioural claning
Michael Bain and Claude Sommut · 1999
Earlier work this paper cites.
A framework for transfer in reinforcement learning
George D Konidaris · 2006
Earlier work this paper cites.
Transferring instances for model-based reinforcement learning
Matthew E Taylor, Nicholas K Jong, and Peter Stone · 2008
Earlier work this paper cites.
A survey of robot learning from demonstration
Brenna D Argall, Sonia Chernova, Manuela Veloso, and Brett Browning · 2009
Earlier work this paper cites.
Efficient reductions for imitation learning
Stéphane Ross and Drew Bagnell · 2010
Earlier work this paper cites.
A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey J Gordon, and Drew Bagnell · 2011
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Learning grounded finite-state representations from unstructured demonstrations
Scott Niekum, Sarah Osentoski, George Konidaris, Sachin Chitta, Bhaskara Marthi, and Andrew G Barto · 2015
Cited alongside, same era.
Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
Cited alongside, same era.
End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al · 2016
A machine learning approach to visual perception of forest trails for mobile robots
Alessandro Giusti, Jérôme Guzzi, Dan C Cireşan, Fang-Lin He, Juan P Rodríguez, Flavio Fontana, Matthias Faessler, Christian Forster, Jürgen Schmidhuber, Gianni Di Caro, et al · 2016
Later among the works it cites.
Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
Later among the works it cites.
Model-free imitation learning with policy optimization
Jonathan Ho, Jayesh Gupta, and Stefano Ermon · 2016
Later among the works it cites.
Combining model-based and model-free updates for trajectory-centric reinforcement learning
Yevgen Chebotar, Karol Hausman, Marvin Zhang, Gaurav Sukhatme, Stefan Schaal, 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
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Cited alongside, same era.
Learning transferable policies for monocular reactive mav control
Shreyansh Daftry, J Andrew Bagnell, and Martial Hebert · 2016
Cited alongside, same era.
Guided cost learning: Deep inverse optimal control via policy optimization
Chelsea Finn, Sergey Levine, and Pieter Abbeel · 2016
Cited alongside, same era.
Later among the works it cites.
Grounded action transformation for robot learning in simulation
Josiah P Hanna and Peter Stone · 2017
Later among the works it cites.
Imitation from observation: Learning to imitate behaviors from raw video via context translation
YuXuan Liu, Abhishek Gupta, Pieter Abbeel, and Sergey Levine · 2017
Later among the works it cites.