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Exploration in sparse reward reinforcement learning remains an open challenge.
Improving generalization for temporal difference learning: The successor representation
Peter Dayan · 1993
Earlier work this paper cites.
Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
Richard S Sutton, Doina Precup, and Satinder Singh · 1999
Earlier work this paper cites.
How can we define intrinsic motivation?
Pierre-Yves Oudeyer and Frederic Kaplan · 2008
Earlier work this paper cites.
Formal theory of creativity, fun, and intrinsic motivation (1990–2010)
Jürgen Schmidhuber · 2010
Earlier work this paper cites.
Design principles of the hippocampal cognitive map
Kimberly L Stachenfeld, Matthew Botvinick, and Samuel J Gershman · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Asynchronous methods for deep reinforcement learning
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Earlier work this paper cites.
Unifying count-based exploration and intrinsic motivation
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Earlier work this paper cites.
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
Earlier work this paper cites.
Reinforcement learning with unsupervised auxiliary tasks
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z Leibo, David Silver, and Koray Kavukcuoglu · 2016
Earlier work this paper cites.
Vizdoom: A doom-based ai research platform for visual reinforcement learning
Michał Kempka, Marek Wydmuch, Grzegorz Runc, Jakub Toczek, and Wojciech Jaśkowski · 2016
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Cited alongside, same era.
Proximal policy optimization algorithms
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Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell · 2017
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Count-based exploration with neural density models
Georg Ostrovski, Marc G Bellemare, Aaron van den Oord, and Rémi Munos · 2017
Eigenoption discovery through the deep successor representation
Marlos C Machado, Clemens Rosenbaum, Xiaoxiao Guo, Miao Liu, Gerald Tesauro, and Murray Campbell · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Later among the works it cites.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
Later among the works it cites.
Distributed prioritized experience replay
Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado Van Hasselt, and David Silver · 2018
Later among the works it cites.
Episodic curiosity through reachability
Nikolay Savinov, Anton Raichuk, Raphaël Marinier, Damien Vincent, Marc Pollefeys, Timothy Lillicrap, and Sylvain Gelly · 2018
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The option-critic architecture
Pierre-Luc Bacon, Jean Harb, and Doina Precup · 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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Ddco: Discovery of deep continuous options for robot learning from demonstrations
Sanjay Krishnan, Roy Fox, Ion Stoica, and Ken Goldberg · 2017
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Feature control as intrinsic motivation for hierarchical reinforcement learning
Nat Dilokthanakul, Christos Kaplanis, Nick Pawlowski, and Murray Shanahan · 2017
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Successor features for transfer in reinforcement learning
André Barreto, Will Dabney, Rémi Munos, Jonathan J Hunt, Tom Schaul, Hado P van Hasselt, and David Silver · 2017
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Deep reinforcement learning with successor features for navigation across similar environments
Jingwei Zhang, Jost Tobias Springenberg, Joschka Boedecker, and Wolfram Burgard · 2017
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Exploration by random network distillation
Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov
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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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Count-based exploration with the successor representation
Marlos C Machado, Marc G Bellemare, and Michael Bowling · 2018
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On value function representation of long horizon problems
Lucas Lehnert, Romain Laroche, and Harm van Seijen · 2018
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Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, et al · 2018
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Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Volodymir Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, et al · 2018
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Successor options : An option discovery algorithm for reinforcement learning, 2019
Manan Tomar*, Rahul Ramesh*, and Balaraman Ravindran · 2019
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