Fetching the paper…
Reading the bibliography…
Imitation learning, followed by reinforcement learning algorithms, is a promising paradigm to solve complex control tasks sample-efficiently.
Alvinn: An autonomous land vehicle in a neural network
Dean A Pomerleau · 1989
Earlier work this paper cites.
Q-learning
Christopher JCH Watkins and Peter Dayan · 1992
Earlier work this paper cites.
Learning from demonstration
Stefan Schaal · 1997
Earlier work this paper cites.
A framework for behavioural claning
Michael Bain and Claude Sommut · 1999
Earlier work this paper cites.
Algorithms for inverse reinforcement learning
Andrew Y Ng, Stuart J Russell, et al · 2000
Earlier work this paper cites.
Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y Ng · 2004
Earlier work this paper cites.
Grasp recognition in virtual reality for robot pregrasp planning by demonstration
Jacopo Aleotti and Stefano Caselli · 2006
Earlier work this paper cites.
Maximum entropy inverse reinforcement learning
Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, and Anind K Dey · 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.
Learning stable nonlinear dynamical systems with gaussian mixture models
S Mohammad Khansari-Zadeh and Aude Billard · 2011
Earlier work this paper cites.
A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
Earlier work this paper cites.
Thomas Degris, Martha White, and Richard S Sutton · 2012
Earlier work this paper cites.
Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
Michael U Gutmann and Aapo Hyvärinen · 2012
Earlier work this paper cites.
Feedback motion planning and learning from demonstration in physical robotic assistance: differences and synergies
Martin Lawitzky, Jose Ramon Medina, Dongheui Lee, and Sandra Hirche · 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.
Learning from limited demonstrations
Beomjoon Kim, Amir-massoud Farahmand, Joelle Pineau, and Doina Precup · 2013
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Boosted bellman residual minimization handling expert demonstrations
Bilal Piot, Matthieu Geist, and Olivier Pietquin · 2014
Cited alongside, same era.
Reinforcement and imitation learning via interactive no-regret learning
Stephane Ross and J Andrew Bagnell · 2014
Cited alongside, same era.
An invitation to imitation
J Andrew Bagnell · 2015
Cited alongside, same era.
Direct policy iteration with demonstrations
Jessica Chemali and Alessandro Lazaric · 2015
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.
Human-level control through deep reinforcement learning
Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, and Sergey Levine · 2017
Later among the works it cites.
State aware imitation learning
Yannick Schroecker and Charles L Isbell · 2017
Later among the works it cites.
Deeply aggrevated: Differentiable imitation learning for sequential prediction
Wen Sun, Arun Venkatraman, Geoffrey J Gordon, Byron Boots, and J Andrew Bagnell · 2017
Later among the works it cites.
Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
Matej Večerík, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Heess, Thomas Rothörl, Thomas Lampe, and Martin Riedmiller · 2017
Later among the works it cites.
Robust imitation of diverse behaviors
Ziyu Wang, Josh S Merel, Scott E Reed, Nando de Freitas, Gregory Wayne, and Nicolas Heess · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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
Cited alongside, same era.
Openai gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Cited alongside, same era.
Q-prop: Sample-efficient policy gradient with an off-policy critic
Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani, Richard E Turner, and Sergey Levine · 2016
Cited alongside, same era.
Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
Cited alongside, same era.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
Cited alongside, same era.
Safe and efficient off-policy reinforcement learning
Rémi Munos, Tom Stepleton, Anna Harutyunyan, and Marc Bellemare · 2016
Cited alongside, same era.
Deep reinforcement learning with double q-learning
Hado Van Hasselt, Arthur Guez, and David Silver · 2016
Cited alongside, same era.
Later among the works it cites.
guided motion planning
Gu Ye and Ron Alterovitz · 2017
Later among the works it cites.
Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine · 2018
Later among the works it cites.
Model-based reinforcement learning via meta-policy optimization
Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, and Pieter Abbeel · 2018
Later among the works it cites.
Reinforcement learning from imperfect demonstrations
Yang Gao, Ji Lin, Fisher Yu, Sergey Levine, Trevor Darrell, et al · 2018
Later among the works it cites.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
Later among the works it cites.
Deep q-learning from demonstrations
Todd Hester, Matej Vecerik, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, et al · 2018
Later among the works it cites.
Model-ensemble trust-region policy optimization
Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, and Pieter Abbeel · 2018
Later among the works it cites.
Hierarchical imitation and reinforcement learning
Hoang M Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, and Hal Daumé III · 2018
Later among the works it cites.
Algorithmic framework for model-based deep reinforcement learning with theoretical guarantees
Yuping Luo, Huazhe Xu, Yuanzhi Li, Yuandong Tian, Trevor Darrell, and Tengyu Ma · 2018
Later among the works it cites.
Overcoming exploration in reinforcement learning with demonstrations
Ashvin Nair, Bob McGrew, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2018
Later among the works it cites.
An algorithmic perspective on imitation learning
Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J Andrew Bagnell, Pieter Abbeel, Jan Peters, et al · 2018
Later among the works it cites.
Multi-goal reinforcement learning: Challenging robotics environments and request for research, 2018
Matthias Plappert, Marcin Andrychowicz, Alex Ray, Bob McGrew, Bowen Baker, Glenn Powell, Jonas Schneider, Josh Tobin, Maciek Chociej, Peter Welinder, Vikash Kumar, and Wojciech Zaremba · 2018
Later among the works it cites.
Graph networks as learnable physics engines for inference and control
Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, and Peter Battaglia · 2018
Later among the works it cites.
Generative predecessor models for sample-efficient imitation learning
Yannick Schroecker, Mel Vecerik, and Jon Scholz · 2018
Later among the works it cites.
Behavioral cloning from observation
Faraz Torabi, Garrett Warnell, and Peter Stone · 2018
Later among the works it cites.