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We propose the challenge of rapid task-solving in novel environments (RTS), wherein an agent must solve a series of tasks as rapidly as possible in an unfamiliar environment.
Improving generalization for temporal difference learning: The successor representation
Peter Dayan · 1993
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Richard S Sutton and A Barto · 1998
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David Foster and Peter Dayan · 2002
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Hippocampal contributions to control: the third way
Máté Lengyel and Peter Dayan · 2008
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Model-based influences on humans’ choices and striatal prediction errors
Nathaniel D Daw, Samuel J Gershman, Ben Seymour, Peter Dayan, and Raymond J Dolan · 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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Universal value function approximators
Tom Schaul, Daniel Horgan, Karol Gregor, and David Silver · 2015
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Charles Blundell, Benigno Uria, Alexander Pritzel, Yazhe Li, Avraham Ruderman, Joel Z Leibo, Jack Rae, Daan Wierstra, and Demis Hassabis · 2016
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Yan Duan, John Schulman, Xi Chen, Peter L Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
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Control of memory, active perception, and action in minecraft
Junhyuk Oh, Valliappa Chockalingam, Satinder Singh, and Honglak Lee · 2016
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Value iteration networks
Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, and Pieter Abbeel · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
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Reinforcement learning and episodic memory in humans and animals: an integrative framework
Samuel J Gershman and Nathaniel D Daw · 2017
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Cognitive mapping and planning for visual navigation
Saurabh Gupta, James Davidson, Sergey Levine, Rahul Sukthankar, and Jitendra Malik · 2017
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Building machines that learn and think like people
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman · 2017
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Neural episodic control
Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adria Puigdomenech Badia, Oriol Vinyals, Demis Hassabis, Daan Wierstra, and Charles Blundell · 2017
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Imagination-augmented agents for deep reinforcement learning
Sébastien Racanière, Théophane Weber, David Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adria Puigdomenech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, et al · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Episodic contributions to model-based reinforcement learning
Oliver Vikbladh, Daphna Shohamy, and Nathaniel Daw · 2017
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Learning to reinforcement learn. arxiv 1611.05763, 2017
Unsupervised predictive memory in a goal-directed agent
Greg Wayne, Chia-Chun Hung, David Amos, Mehdi Mirza, Arun Ahuja, Agnieszka Grabska-Barwinska, Jack Rae, Piotr Mirowski, Joel Z Leibo, Adam Santoro, et al · 2018
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Toward jumpy planning
B Akilesh, Suriya Singh, Anirudh Goyal, Alexander Neitz, and Aaron Courville · 2019
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Reinforcement learning, fast and slow
Mathew Botvinick, Sam Ritter, Jane X Wang, Zeb Kurth-Nelson, Charles Blundell, and Demis Hassabis · 2019
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Go-explore: a new approach for hard-exploration problems
Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O Stanley, and Jeff Clune · 2019
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Generalization of reinforcement learners with working and episodic memory
Meire Fortunato, Melissa Tan, Ryan Faulkner, Steven Hansen, Adrià Puigdomènech Badia, Gavin Buttimore, Charles Deck, Joel Z Leibo, and Charles Blundell · 2019
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JX Wang, Z Kurth-Nelson, D Tirumala, H Soyer, JZ Leibo, R Munos, C Blundell, D Kumaran, and M Botivnick · 2017
Cited alongside, same era.
Machine learning for combinatorial optimization: a methodological tour d’horizon
Yoshua Bengio, Andrea Lodi, and Antoine Prouvost · 2018
Cited alongside, same era.
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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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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Izzeddin Gur, Ulrich Rückert, Aleksandra Faust, and Dilek Hakkani-Tür · 2018
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Attention, learn to solve routing problems!
Wouter Kool, Herke Van Hoof, and Max Welling · 2018
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Learning to navigate in cities without a map
Piotr Mirowski, Matt Grimes, Mateusz Malinowski, Karl Moritz Hermann, Keith Anderson, Denis Teplyashin, Karen Simonyan, Andrew Zisserman, Raia Hadsell, et al · 2018
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From language to goals: Inverse reinforcement learning for vision-based instruction following
Justin Fu, Anoop Korattikara, Sergey Levine, and Sergio Guadarrama · 2019
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Shaping belief states with generative environment models for rl
Karol Gregor, Danilo Jimenez Rezende, Frederic Besse, Yan Wu, Hamza Merzic, and Aaron van den Oord · 2019
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An investigation of model-free planning
Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sébastien Racanière, Théophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, et al · 2019
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The streetlearn environment and dataset
Piotr Mirowski, Andras Banki-Horvath, Keith Anderson, Denis Teplyashin, Karl Moritz Hermann, Mateusz Malinowski, Matthew Koichi Grimes, Karen Simonyan, Koray Kavukcuoglu, Andrew Zisserman, et al · 2019
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Meta-Reinforcement Learning with Episodic Recall: An Integrative Theory of Reward-Driven Learning
Samuel Ritter · 2019
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Mastering atari, go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, et al · 2019
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Agent57: Outperforming the atari human benchmark
Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Daniel Guo, and Charles Blundell · 2020
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Model-based reinforcement learning: A survey
Thomas M Moerland, Joost Broekens, and Catholijn M Jonker · 2020
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