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All-goals updating exploits the off-policy nature of Q-learning to update all possible goals an agent could have from each transition in the world, and was introduced into Reinforcement Learning (RL) by Kaelbling (1993).
Learning to achieve goals
Leslie Pack Kaelbling · 1993
Earlier work this paper cites.
Continual learning in reinforcement environments
Mark Bishop Ring · 1994
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Off-policy temporal-difference learning with function approximation
Doina Precup, Richard S Sutton, and Sanjoy Dasgupta · 2001
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Intrinsic motivation systems for autonomous mental development
Pierre-Yves Oudeyer, Frdric Kaplan, and Verena V Hafner · 2007
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
Earlier work this paper cites.
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
Earlier work this paper cites.
Universal value function approximators
Tom Schaul, Daniel Horgan, Karol Gregor, and David Silver · 2015
Earlier work this paper cites.
Learning to poke by poking: Experiential learning of intuitive physics
Pulkit Agrawal, Ashvin V Nair, Pieter Abbeel, Jitendra Malik, and Sergey Levine · 2016
Earlier work this paper cites.
End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
Cited alongside, same era.
Learning to navigate in complex environments
Piotr Mirowski, Razvan Pascanu, Fabio Viola, Hubert Soyer, Andrew J Ballard, Andrea Banino, Misha Denil, Ross Goroshin, Laurent Sifre, Koray Kavukcuoglu, et al · 2016
Cited alongside, same era.
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
Learning state representation for deep actor-critic control
Jelle Munk, Jens Kober, and Robert Babuška · 2016
Cited alongside, same era.
Hindsight experience replay
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, OpenAI Pieter Abbeel, and Wojciech Zaremba · 2017
Cited alongside, same era.
Automatic goal generation for reinforcement learning agents
David Held, Xinyang Geng, Carlos Florensa, and Pieter Abbeel · 2017
Later among the works it cites.
Reinforcement learning with unsupervised auxiliary tasks
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z Leibo, David Silver, and Koray Kavukcuoglu · 2017
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Loss is its own reward: Self-supervision for reinforcement learning
Evan Shelhamer, Parsa Mahmoudieh, Max Argus, and Trevor Darrell · 2017
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Distral: Robust multitask reinforcement learning
Yee Teh, Victor Bapst, Wojciech M Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, and Razvan Pascanu · 2017
Later among the works it cites.
Hybrid reward architecture for reinforcement learning
Harm Van Seijen, Mehdi Fatemi, Joshua Romoff, Romain Laroche, Tavian Barnes, and Jeffrey Tsang · 2017
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The intentional unintentional agent: Learning to solve many continuous control tasks simultaneously
Serkan Cabi, Sergio Gómez Colmenarejo, Matthew W Hoffman, Misha Denil, Ziyu Wang, and Nando Freitas · 2017
Cited alongside, same era.
Openai baselines, 2017
Prafulla Dhariwal, Christopher Hesse, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, and Yuhuai Wu · 2017
Cited alongside, same era.
Automated curriculum learning for neural networks
Alex Graves, Marc G Bellemare, Jacob Menick, Rémi Munos, and Koray Kavukcuoglu · 2017
Cited alongside, same era.
Unicorn: Continual learning with a universal, off-policy agent
Daniel J Mankowitz, Augustin Žídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh, Hado van Hasselt, David Silver, and Tom Schaul · 2018
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Learning by playing-solving sparse reward tasks from scratch
Martin Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Tom Van de Wiele, Volodymyr Mnih, Nicolas Heess, and Jost Tobias Springenberg · 2018
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Intrinsic motivation and automatic curricula via asymmetric self-play
Sainbayar Sukhbaatar, Zeming Lin, Ilya Kostrikov, Gabriel Synnaeve, Arthur Szlam, and Rob Fergus · 2018
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