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In recent years, a number of reinforcement learning (RL) methods have been proposed to explore complex environments which differ across episodes.
On the generalized distance in statistics
Prasanta Chandra Mahalanobis · 1936
Earlier work this paper cites.
Adjustment of an inverse matrix corresponding to a change in one element of a given matrix
J. Sherman and W. J. Morrison · 1950
Earlier work this paper cites.
A possibility for implementing curiosity and boredom in model-building neural controllers
Jürgen Schmidhuber · 1991
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Using confidence bounds for exploitation-exploration trade-offs
Peter Auer · 2002
Earlier work this paper cites.
R-MAX - A general polynomial time algorithm for near-optimal reinforcement learning
Ronen I. Brafman and Moshe Tennenholtz · 2002
Earlier work this paper cites.
Near-optimal reinforcement learning in polynomial time
Michael Kearns and Satinder Singh · 2002
Earlier work this paper cites.
Intrinsic motivation systems for autonomous mental development
Pierre-Yves Oudeyer, Frdric Kaplan, and Verena V Hafner · 2007
Earlier work this paper cites.
Stochastic linear optimization under bandit feedback
Varsha Dani, Thomas P. Hayes, and Sham M. Kakade · 2008
Earlier work this paper cites.
An analysis of model-based interval estimation for markov decision processes
Alexander L Strehl and Michael L Littman · 2008
Earlier work this paper cites.
What is intrinsic motivation? a typology of computational approaches
Pierre-Yves Oudeyer and Frederic Kaplan · 2009
Earlier work this paper cites.
A contextual-bandit approach to personalized news article recommendation
Lihong Li, Wei Chu, John Langford, and Robert E. Schapire · 2010
Earlier work this paper cites.
Contextual markov decision processes
Assaf Hallak, Dotan Di Castro, and Shie Mannor · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
Earlier work this paper cites.
Incentivizing exploration in reinforcement learning with deep predictive models
Bradly C Stadie, Sergey Levine, and Pieter Abbeel · 2015
Earlier work this paper cites.
Charles Beattie, Joel Z Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich Küttler, Andrew Lefrancq, Simon Green, Víctor Valdés, Amir Sadik, et al · 2016
Earlier work this paper cites.
Unifying count-based exploration and intrinsic motivation
Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Remi Munos · 2016
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Unifying count-based exploration and intrinsic motivation
Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Rémi Munos · 2016
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Fast and accurate deep network learning by exponential linear units (elus)
Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter · 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
Earlier work this paper cites.
Curiosity search: Producing generalists by encouraging individuals to continually explore and acquire skills throughout their lifetime
Christopher Stanton and Jeff Clune · 2016
Earlier work this paper cites.
Surprise-based intrinsic motivation for deep reinforcement learning
Joshua Achiam and Shankar Sastry · 2017
Earlier work this paper cites.
Reverse curriculum generation for reinforcement learning
Carlos Florensa, David Held, Markus Wulfmeier, Michael Zhang, and Pieter Abbeel · 2017
Earlier work this paper cites.
Intrinsically motivated goal exploration processes with automatic curriculum learning
Sébastien Forestier, Rémy Portelas, Yoan Mollard, and Pierre-Yves Oudeyer · 2017
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Count-based exploration in feature space for reinforcement learning
Jarryd Martin, Suraj Narayanan Sasikumar, Tom Everitt, and Marcus Hutter · 2017
Cited alongside, same era.
Count-based exploration with neural density models
Georg Ostrovski, Marc G Bellemare, Aäron Oord, and Rémi Munos · 2017
Cited alongside, same era.
Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell · 2017
Cited alongside, same era.
# exploration: A study of count-based exploration for deep reinforcement learning
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, OpenAI Xi Chen, Yan Duan, John Schulman, Filip DeTurck, and Pieter Abbeel · 2017
Cited alongside, same era.
Exploration by random network distillation
Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov · 2018
Learning with amigo: Adversarially motivated intrinsic goals
Andres Campero, Roberta Raileanu, Heinrich Küttler, Joshua B Tenenbaum, Tim Rocktäschel, and Edward Grefenstette · 2020
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Leveraging procedural generation to benchmark reinforcement learning
Karl Cobbe, Chris Hesse, Jacob Hilton, and John Schulman · 2020
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Intrinsically motivated goal-conditioned reinforcement learning: a short survey
Cédric Colas, Tristan Karch, Olivier Sigaud, and Pierre-Yves Oudeyer · 2020
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Emergent complexity and zero-shot transfer via unsupervised environment design
Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre Bayen, Stuart Russell, Andrew Critch, and Sergey Levine · 2020
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Threedworld: A platform for interactive multi-modal physical simulation
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Cited alongside, same era.
Minimalistic gridworld environment for openai gym
Maxime Chevalier-Boisvert, Lucas Willems, and Suman Pal · 2018
Cited alongside, same era.
IMPALA: scalable distributed deep-rl with importance weighted actor-learner architectures
Lasse Espeholt, Hubert Soyer, Rémi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, and Koray Kavukcuoglu · 2018
Cited alongside, same era.
Procedural level generation improves generality of deep reinforcement learning
Niels Justesen, Ruben Rodriguez Torrado, Philip Bontrager, Ahmed Khalifa, Julian Togelius, and Sebastian Risi · 2018
Cited alongside, same era.
Christopher Stanton and Jeff Clune · 2018
Cited alongside, same era.
Intrinsic motivation and automatic curricula via asymmetric self-play
Sainbayar Sukhbaatar, Ilya Kostrikov, Arthur D. Szlam, and Rob Fergus · 2018
Cited alongside, same era.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
Cited alongside, same era.
A dissection of overfitting and generalization in continuous reinforcement learning
Amy Zhang, Nicolas Ballas, and Joelle Pineau · 2018
Cited alongside, same era.
Chuang Gan, Jeremy Schwartz, Seth Alter, Martin Schrimpf, James Traer, Julian De Freitas, Jonas Kubilius, Abhishek Bhandwaldar, Nick Haber, Megumi Sano, et al · 2020
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The nethack learning environment
Heinrich Küttler, Nantas Nardelli, Alexander H. Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette, and Tim Rocktäschel · 2020
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Count-based exploration with the successor representation
Marlos C Machado, Marc G Bellemare, and Michael Bowling · 2020
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Ride: Rewarding impact-driven exploration for procedurally-generated environments
Roberta Raileanu and Tim Rocktäschel · 2020
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igibson 1.0: A simulation environment for interactive tasks in large realistic scenes
Bokui Shen, Fei Xia, Chengshu Li, Roberto Martín-Martín, Linxi Fan, Guanzhi Wang, Claudia Pérez-D’Arpino, Shyamal Buch, Sanjana Srivastava, Lyne Tchapmi, et al · 2020
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Dd-ppo: Learning near-perfect pointgoal navigators from 2.5 billion frames
Erik Wijmans, Abhishek Kadian, Ari S. Morcos, Stefan Lee, Irfan Essa, Devi Parikh, Manolis Savva, and Dhruv Batra · 2020
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Sapien: A simulated part-based interactive environment
Fanbo Xiang, Yuzhe Qin, Kaichun Mo, Yikuan Xia, Hao Zhu, Fangchen Liu, Minghua Liu, Hanxiao Jiang, Yifu Yuan, He Wang, et al · 2020
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Deep reinforcement learning at the edge of the statistical precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron Courville, and Marc G Bellemare · 2021
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Anti-concentrated confidence bonuses for scalable exploration
Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, and Sham M. Kakade · 2021
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Adversarially guided actor-critic
Yannis Flet-Berliac, Johan Ferret, Olivier Pietquin, Philippe Preux, and Matthieu Geist · 2021
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Benchmarking the spectrum of agent capabilities
Danijar Hafner · 2021
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Prioritized level replay
Minqi Jiang, Edward Grefenstette, and Tim Rocktäschel · 2021
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A survey of generalisation in deep reinforcement learning
Robert Kirk, Amy Zhang, Edward Grefenstette, and Tim Rocktäschel · 2021
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A survey of generalisation in deep reinforcement learning
Robert Kirk, Amy Zhang, Edward Grefenstette, and Tim Rocktäschel · 2021
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Megaverse: Simulating embodied agents at one million experiences per second
Aleksei Petrenko, Erik Wijmans, Brennan Shacklett, and Vladlen Koltun · 2021
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Habitat-matterport 3d dataset (HM3d): 1000 large-scale 3d environments for embodied AI
Santhosh Kumar Ramakrishnan, Aaron Gokaslan, Erik Wijmans, Oleksandr Maksymets, Alexander Clegg, John M Turner, Eric Undersander, Wojciech Galuba, Andrew Westbury, Angel X Chang, Manolis Savva, Yili Zhao, and Dhruv Batra · 2021
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Minihack the planet: A sandbox for open-ended reinforcement learning research
Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro, Fabio Petroni, Heinrich Küttler, Edward Grefenstette, and Tim Rocktäschel · 2021
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Habitat 2.0: Training home assistants to rearrange their habitat
Andrew Szot, Alex Clegg, Eric Undersander, Erik Wijmans, Yili Zhao, John Turner, Noah Maestre, Mustafa Mukadam, Devendra Chaplot, Oleksandr Maksymets, Aaron Gokaslan, Vladimir Vondrus, Sameer Dharur, Franziska Meier, Wojciech Galuba, Angel Chang, Zsolt Kira, Vladlen Koltun, Jitendra Malik, Manolis Savva, and Dhruv Batra · 2021
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Rank the episodes: A simple approach for exploration in procedurally-generated environments
Daochen Zha, Wenye Ma, Lei Yuan, Xia Hu, and Ji Liu · 2021
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Noveld: A simple yet effective exploration criterion
Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E. Gonzalez, and Yuandong Tian · 2021
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Improving intrinsic exploration with language abstractions
Jesse Mu, Victor Zhong, Roberta Raileanu, Minqi Jiang, Noah D. Goodman, Tim Rocktäschel, and Edward Grefenstette · 2022
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