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Contemporary artificial intelligence systems exhibit rapidly growing abilities accompanied by the growth of required resources, expansive datasets and corresponding investments into computing infrastructure.
The influence of improvement in one mental function upon the efficiency of other functions.(i)
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Input generalization in delayed reinforcement learning: An algorithm and performance comparisons
David Chapman and Leslie Pack Kaelbling · 1991
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Dyna, an integrated architecture for learning, planning, and reacting
Richard S Sutton · 1991
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Transfer of learning by composing solutions of elemental sequential tasks
Satinder Pal Singh · 1992
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Origins of knowledge
Elizabeth S Spelke, Karen Breinlinger, Janet Macomber, and Kristen Jacobson · 1992
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Q-learning
Christopher JCH Watkins and Peter Dayan · 1992
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Improving generalization for temporal difference learning: The successor representation
Peter Dayan · 1993
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Feudal reinforcement learning
Peter Dayan and Geoffrey E Hinton · 1993
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Learning to achieve goals
Leslie Pack Kaelbling · 1993
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Vision-based behavior acquisition for a shooting robot by using a reinforcement learning
Minoru Asada, Shoichi Noda, Sukoya Tawaratsumida, and Koh Hosoda · 1994
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Continual learning in reinforcement environments
Mark Bishop Ring et al · 1994
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On learning how to learn learning strategies, 1994
Juergen Schmidhuber · 1994
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Genetic and environmental contributions to the acquisition of a motor skill
P W Fox, S L Hershberger, and T J Bouchard, Jr · 1996
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Average reward reinforcement learning: Foundations, algorithms, and empirical results
Sridhar Mahadevan · 1996
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Multitask learning
Rich Caruana · 1997
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System identification
Lennart Ljung · 1998
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Lifelong learning algorithms
Sebastian Thrun · 1998
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Model predictive control: past, present and future
Manfred Morari and Jay H Lee · 1999
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Policy invariance under reward transformations: Theory and application to reward shaping
A. Ng, D. Harada, and Stuart J. Russell · 1999
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Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Richard S. Sutton, Doina Precup, and Satinder Singh · 1999
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Darwinian dynamics: evolutionary transitions in fitness and individuality
Richard E Michod · 2000
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Algorithms for inverse reinforcement learning
Andrew Y. Ng and Stuart J. Russell · 2000
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Nonlinear dynamic system identification
Oliver Nelles · 2001
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Perspectives and problems in motor learning
Daniel M Wolpert, Zoubin Ghahramani, and J Randall Flanagan · 2001
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Apprenticeship learning via inverse reinforcement learning
P. Abbeel and A. Ng · 2004
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The ingredients of real-world robotic reinforcement learning
Henry Zhu, Justin Yu, Abhishek Gupta, Dhruv Shah, Kristian Hartikainen, Avi Singh, Vikash Kumar, and Sergey Levine · 2004
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Reducing the dimensionality of data with neural networks
Geoffrey E Hinton and Ruslan R Salakhutdinov · 2006
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Autonomous shaping: knowledge transfer in reinforcement learning
G. Konidaris and A. Barto · 2006
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Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2006
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Accelerating online reinforcement learning with offline datasets
Ashvin Nair, Murtaza Dalal, Abhishek Gupta, and Sergey Levine · 2006
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Maximum margin planning
Nathan D. Ratliff, J. Andrew Bagnell, and Martin A. Zinkevich · 2006
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Self-supervised policy adaptation during deployment
Nicklas Hansen, Yu Sun, P. Abbeel, Alexei A. Efros, Lerrel Pinto, and Xiaolong Wang · 2007
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Building portable options: Skill transfer in reinforcement learning
George Konidaris and Andrew Barto · 2007
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Hippocampal contributions to control: The third way
Máté Lengyel and Peter Dayan · 2007
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Representation transfer for reinforcement learning
Matthew E Taylor and Peter Stone · 2007
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Relational macros for transfer in reinforcement learning
Lisa Torrey, Jude Shavlik, Trevor Walker, and Richard Maclin · 2007
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Natural actor-critic
Jan Peters and Stefan Schaal · 2008
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Maximum entropy inverse reinforcement learning
Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, Anind K Dey, et al · 2008
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Where do rewards come from
Satinder Singh, Richard L Lewis, and Andrew G Barto · 2009
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Transfer learning for reinforcement learning domains: A survey
Matthew E Taylor and Peter Stone · 2009
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robosuite: A modular simulation framework and benchmark for robot learning
Yuke Zhu, Josiah Wong, Ajay Mandlekar, Roberto Martín-Martín, Abhishek Joshi, Soroush Nasiriany, and Yifeng Zhu · 2009
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Transfer learning in deep reinforcement learning: A survey
Zhuangdi Zhu, Kaixiang Lin, and Jiayu Zhou · 2009
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Learning dexterous manipulation from suboptimal experts
Rae Jeong, Jost Tobias Springenberg, Jackie Kay, Daniel Zheng, Yuxiang Zhou, Alexandre Galashov, Nicolas Heess, and Francesco Nori · 2010
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Deep auto-encoder neural networks in reinforcement learning
Sascha Lange and Martin Riedmiller · 2010
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Cog: Connecting new skills to past experience with offline reinforcement learning
Avi Singh, Albert Yu, Jonathan Yang, Jesse Zhang, Aviral Kumar, and Sergey Levine · 2010
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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Parrot: Data-driven behavioral priors for reinforcement learning
Avi Singh, Huihan Liu, Gaoyue Zhou, Albert Yu, Nicholas Rhinehart, and Sergey Levine · 2011
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Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction
Richard S. Sutton, Joseph Modayil, Michael Delp, Thomas Degris, Patrick M. Pilarski, Adam White, and Doina Precup · 2011
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Deep learning of representations for unsupervised and transfer learning
Yoshua Bengio · 2012
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Hierarchical relative entropy policy search
Christian Daniel, Gerhard Neumann, and Jan Peters · 2012
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Transfer in reinforcement learning: A framework and a survey
Alessandro Lazaric · 2012
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Unsupervised and transfer learning challenge: a deep learning approach
Grégoire Mesnil, Yann Dauphin, Xavier Glorot, Salah Rifai, Yoshua Bengio, Ian Goodfellow, Erick Lavoie, Xavier Muller, Guillaume Desjardins, David Warde-Farley, et al · 2012
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The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
Ian J Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio · 2013
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Pac-inspired option discovery in lifelong reinforcement learning
Emma Brunskill and Lihong Li · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
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Model predictive control: Recent developments and future promise
David Q Mayne · 2014
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Policy transfer using reward shaping
T. Brys, A. Harutyunyan, Matthew E. Taylor, and A. Nowé · 2015
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Complexity and Creative Capacity: Rethinking knowledge transfer, adaptive management and wicked environmental problems
Kelly Chapman · 2015
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Active reward learning with a novel acquisition function
Christian Daniel, Oliver Kroemer, Malte Viering, Jan Metz, and Jan Peters · 2015
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Deepmpc: Learning deep latent features for model predictive control
Ian Lenz, Ross A Knepper, and Ashutosh Saxena · 2015
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Actor-mimic: Deep multitask and transfer reinforcement learning
Emilio Parisotto, Jimmy Lei Ba, and Ruslan Salakhutdinov · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Andrei A Rusu, Sergio Gomez Colmenarejo, Caglar Gulcehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, and Raia Hadsell · 2015
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Universal value function approximators
Tom Schaul, Daniel Horgan, Karol Gregor, and David Silver · 2015
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Maximum entropy deep inverse reinforcement learning
Markus Wulfmeier, Peter Ondruska, and Ingmar Posner · 2015
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Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, and Nando de Freitas · 2016
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Successor features for transfer in reinforcement learning
André Barreto, Will Dabney, Rémi Munos, Jonathan J Hunt, Tom Schaul, Hado Van Hasselt, and David Silver · 2016
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Charlie Beattie, Joel Z. Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich Küttler, Andrew Lefrancq, Simon Green, Víctor Valdés, Amir Sadik, Julian Schrittwieser, Keith Anderson, Sarah York, Max Cant, Adam Cain, Adrian Bolton, Stephen Gaffney, Helen King, Demis Hassabis, Shane Legg, and Stig Petersen · 2016
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Openai gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Transfer from simulation to real world through learning deep inverse dynamics model, 2016
Paul Christiano, Zain Shah, Igor Mordatch, Jonas Schneider, Trevor Blackwell, Joshua Tobin, Pieter Abbeel, and Wojciech Zaremba · 2016
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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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Rl 2 : Fast reinforcement learning via slow reinforcement learning, 2016
Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
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Guided cost learning: Deep inverse optimal control via policy optimization
Chelsea Finn, Sergey Levine, and P. Abbeel · 2016
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One-shot learning of manipulation skills with online dynamics adaptation and neural network priors
Justin Fu, Sergey Levine, and Pieter Abbeel · 2016
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Learning and transfer of modulated locomotor controllers
Nicolas Heess, Greg Wayne, Yuval Tassa, Timothy Lillicrap, Martin Riedmiller, and David Silver · 2016
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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
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What makes imagenet good for transfer learning?
Minyoung Huh, Pulkit Agrawal, and Alexei A Efros · 2016
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Deep successor reinforcement learning
Tejas D Kulkarni, Ardavan Saeedi, Simanta Gautam, and Samuel J Gershman · 2016
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End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
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Pointer sentinel mixture models, 2016
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2016
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Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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Cad2rl: Real single-image flight without a single real image
Fereshteh Sadeghi and Sergey Levine · 2016
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When the music’s over. does music skill transfer to children’s and young adolescents’ cognitive and academic skills? a meta-analysis
Giovanni Sala and Fernand Gobet · 2016
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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, Vedavyas Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy P. Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
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Learning to reinforcement learn, 2016
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 2016
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Learning deep control policies for autonomous aerial vehicles with mpc-guided policy search
Tianhao Zhang, Gregory Kahn, Sergey Levine, and Pieter Abbeel · 2016
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Modular multitask reinforcement learning with policy sketches
Jacob Andreas, Dan Klein, and Sergey Levine · 2017
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Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, and Wojciech Zaremba · 2017
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The option-critic architecture
Pierre-Luc Bacon, Jean Harb, and Doina Precup · 2017
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Se3-pose-nets: Structured deep dynamics models for visuomotor planning and control
Arunkumar Byravan, Felix Leeb, Franziska Meier, and Dieter Fox · 2017
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Pre-training neural networks with human demonstrations for deep reinforcement learning
Gabriel V Cruz Jr, Yunshu Du, and Matthew E Taylor · 2017
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Yan Duan, Marcin Andrychowicz, Bradly C Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, and Wojciech Zaremba · 2017
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Deep visual foresight for planning robot motion
Chelsea Finn and Sergey Levine · 2017
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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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Reverse curriculum generation for reinforcement learning
Carlos Florensa, David Held, Markus Wulfmeier, Michael Zhang, and Pieter Abbeel · 2017
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Learning robust rewards with adversarial inverse reinforcement learning
Justin Fu, Katie Luo, and Sergey Levine · 2017
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Karol Gregor, Danilo Jimenez Rezende, and Daan Wierstra · 2017
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Learning invariant feature spaces to transfer skills with reinforcement learning
Abhishek Gupta, Coline Devin, Yuxuan Liu, P. Abbeel, and Sergey Levine · 2017
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Reinforcement learning with deep energy-based policies
Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, and Sergey Levine · 2017
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Probabilistically safe policy transfer
David Held, Zoe McCarthy, Michael Zhang, Fred Shentu, and Pieter Abbeel · 2017
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Reinforcement learning with unsupervised auxiliary tasks
Max Jaderberg, Volodymyr Mnih, Wojciech M. Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, and Koray Kavukcuoglu · 2017
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Robust and efficient transfer learning with hidden parameter markov decision processes
Taylor W Killian, Samuel Daulton, George Konidaris, and Finale Doshi-Velez · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Model-based contextual policy search for data-efficient generalization of robot skills
Andras Kupcsik, Marc Peter Deisenroth, Jan Peters, Ai Poh Loh, Prahlad Vadakkepat, and Gerhard Neumann · 2017
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Transfer reinforcement learning with shared dynamics
Romain Laroche and Merwan Barlier · 2017
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Learning human behaviors from motion capture by adversarial imitation
Josh Merel, Yuval Tassa, Dhruva TB, Sriram Srinivasan, Jay Lemmon, Ziyu Wang, Greg Wayne, and Nicolas Heess · 2017
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A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2017
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Zero-shot task generalization with multi-task deep reinforcement learning
Junhyuk Oh, Satinder Singh, Honglak Lee, and P. Kohli · 2017
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Data-efficient deep reinforcement learning for dexterous manipulation
Ivaylo Popov, Nicolas Manfred Otto Heess, Timothy P. Lillicrap, Roland Hafner, Gabriel Barth-Maron, Matej Vecerík, Thomas Lampe, Yuval Tassa, Tom Erez, and Martin A. Riedmiller · 2017
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Third-person imitation learning
Bradly C. Stadie, P. Abbeel, and Ilya Sutskever · 2017
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Learning to learn: Meta-critic networks for sample efficient learning
Flood Sung, Li Zhang, Tao Xiang, Timothy Hospedales, and Yongxin Yang · 2017
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Distral: Robust multitask reinforcement learning
Yee Whye Teh, Victor Bapst, Wojciech Marian Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, and Razvan Pascanu · 2017
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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
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Attention is all you need
Ashish Vaswani, Noam M. Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
Mel Vecerik, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Heess, Thomas Rothörl, Thomas Lampe, and Martin Riedmiller · 2017
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Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
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Feudal networks for hierarchical reinforcement learning
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, and Koray Kavukcuoglu · 2017
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Robust imitation of diverse behaviors
Ziyu Wang, Josh Merel, Scott E. Reed, Greg Wayne, Nando de Freitas, and Nicolas Heess · 2017
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Structure learning in motor control: A deep reinforcement learning model
Ari Weinstein and Matthew M Botvinick · 2017
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Unifying task specification in reinforcement learning
Martha White · 2017
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Information theoretic mpc for model-based reinforcement learning
Grady Williams, Nolan Wagener, Brian Goldfain, Paul Drews, James M Rehg, Byron Boots, and Evangelos A Theodorou · 2017
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Addressing appearance change in outdoor robotics with adversarial domain adaptation
Markus Wulfmeier, Alex Bewley, and Ingmar Posner · 2017
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Preparing for the unknown: Learning a universal policy with online system identification
Wenhao Yu, Jie Tan, C Karen Liu, and Greg Turk · 2017
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Playing hard exploration games by watching youtube
Yusuf Aytar, Tobias Pfaff, David Budden, Thomas Paine, Ziyu Wang, and Nando De Freitas · 2018
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Transfer in deep reinforcement learning using successor features and generalised policy improvement
Andre Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel Mankowitz, Augustin Zidek, and Remi Munos · 2018
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Universal successor features approximators
Diana Borsa, André Barreto, John Quan, Daniel Mankowitz, Rémi Munos, Hado van Hasselt, David Silver, and Tom Schaul · 2018
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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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Model-based reinforcement learning via meta-policy optimization
Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, and Pieter Abbeel · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Visual foresight: Model-based deep reinforcement learning for vision-based robotic control
Real robot challenge: A robotics competition in the cloud
Stefan Bauer, Felix Widmaier, Manuel Wuthrich, Annika Buchholz, Sebastian Stark, Anirudh Goyal, Thomas Steinbrenner, Joel Bessekon Akpo, Shruti Joshi, Vincent Berenz, Vaibhav Agrawal, Niklas Funk, Julen Urain De Jesus, Jan Peters, Joe Watson, Claire Chen, Krishna Parasuram Srinivasan, Junwu Zhang, Jeffrey Zhang, Matthew Walter, Rishabh Madan, Charles B. Schaff, Takahiro Maeda, Takuma Yoneda, Denis Yarats, Arthur Allshire, Ethan K. Gordon, Tapomayukh Bhattacharjee, Siddhartha S. Srinivasa, Animesh Garg, Harshit S. Sikchi, Jilong Wang, Qingfeng Yao, Shuyu Yang, Robert McCarthy, Francisco Roldán Sánchez, Qiang Wang, David Cordova Bulens, Kevin McGuinness, Noel E. O’Connor, Stephen James Redmond, and Bernhard Scholkopf · 2021
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Meta learning via learned loss
Sarah Bechtle, Artem Molchanov, Yevgen Chebotar, Edward Grefenstette, Ludovic Righetti, Gaurav Sukhatme, and Franziska Meier · 2021
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Model-based meta-reinforcement learning for flight with suspended payloads
Suneel Belkhale, Rachel Li, Gregory Kahn, Rowan McAllister, Roberto Calandra, and Sergey Levine · 2021
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Carl: A benchmark for contextual and adaptive reinforcement learning
Carolin Benjamins, Theresa Eimer, Frederik Schubert, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, and Marius Lindauer · 2021
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Frederik Ebert, Chelsea Finn, Sudeep Dasari, Annie Xie, Alex Lee, and Sergey Levine · 2018
Cited alongside, same era.
Off-policy deep reinforcement learning without exploration, 2018
Scott Fujimoto, David Meger, and Doina Precup · 2018
Cited alongside, same era.
David Ha and Jürgen Schmidhuber · 2018
Cited alongside, same era.
Composable deep reinforcement learning for robotic manipulation
Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou, Murtaza Dalal, Pieter Abbeel, and Sergey Levine · 2018
Cited alongside, same era.
Learning an embedding space for transferable robot skills
Karol Hausman, Jost Tobias Springenberg, Ziyu Wang, Nicolas Heess, and Martin Riedmiller · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Evolved policy gradients
Rein Houthooft, Yuhua Chen, Phillip Isola, Bradly Stadie, Filip Wolski, OpenAI Jonathan Ho, and Pieter Abbeel · 2018
Cited alongside, same era.
Imitation learning via kernel mean embedding
Kee-Eung Kim and Hyun Soo Park · 2018
Cited alongside, same era.
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
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Evaluating model-based planning and planner amortization for continuous control
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URL https://docs.omniverse.nvidia.com/index.html
Nvidia, 2023 · 2023
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