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World models power some of the most efficient reinforcement learning algorithms.
Random sampling with a reservoir
Jeffrey Scott Vitter · 1985
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Dyna, an integrated architecture for learning, planning, and reacting
Richard S Sutton · 1991
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Self-improving reactive agents based on reinforcement learning, planning and teaching
Long-Ji Lin · 1992
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Continual learning in reinforcement environments
Mark Bishop Ring · 1994
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Lifelong robot learning
Sebastian Thrun and Tom M. Mitchell · 1995
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Planning and acting in partially observable stochastic domains
Leslie Pack Kaelbling, Michael L Littman, and Anthony R Cassandra · 1998
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio · 2014
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Deep recurrent q-learning for partially observable mdps
Matthew Hausknecht and Peter Stone · 2015
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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
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Universal value function approximators
Tom Schaul, Daniel Horgan, Karol Gregor, and David Silver · 2015
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Convolutional lstm network: A machine learning approach for precipitation nowcasting
Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo · 2015
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 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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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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Neuroscience-inspired artificial intelligence
Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, and Matthew Botvinick · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Learning without Forgetting
Zhizhong Li and Derek Hoiem · 2017
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Gradient Episodic Memory for Continual Learning
David Lopez-Paz and Marc ’ Aurelio Ranzato · 2017
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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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Continual Learning with Deep Generative Replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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Continual Learning Through Synaptic Intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Minimalistic gridworld environment for openai gym
Maxime Chevalier-Boisvert, Lucas Willems, and Suman Pal · 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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David Ha and Jürgen Schmidhuber · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Re-evaluating continual learning scenarios: A categorization and case for strong baselines
Yen-Chang Hsu, Yen-Cheng Liu, Anita Ramasamy, and Zsolt Kira · 2018
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Selective experience replay for lifelong learning
David Isele and Akansel Cosgun · 2018
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
Towards continual reinforcement learning: A review and perspectives
Khimya Khetarpal, Matthew Riemer, Irina Rish, and Doina Precup · 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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A neural dirichlet process mixture model for task-free continual learning
Soochan Lee, Junsoo Ha, Dongsu Zhang, and Gunhee Kim · 2020
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Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting
Jorge A Mendez, Boyu Wang, and Eric Eaton · 2020
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Planning to explore via self-supervised world models
Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner, and Deepak Pathak · 2020
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Cited alongside, same era.
Deep online learning via meta-learning: Continual adaptation for model-based rl
Anusha Nagabandi, Chelsea Finn, and Sergey Levine · 2018
Cited alongside, same era.
Variational continual learning
Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, and Richard E. Turner · 2018
Cited alongside, same era.
Learning dexterous in-hand manipulation
OpenAI, Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Józefowicz, Bob McGrew, Jakub W. Pachocki, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, Jonas Schneider, Szymon Sidor, Josh Tobin, Peter Welinder, Lilian Weng, and Wojciech Zaremba · 2018
Cited alongside, same era.
Progress & compress: A scalable framework for continual learning
Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
Cited alongside, same era.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
Cited alongside, same era.
Gradient based sample selection for online continual learning
Rahaf Aljundi, Min Lin, Baptiste Goujaud, and Yoshua Bengio · 2019
Cited alongside, same era.
The option keyboard: Combining skills in reinforcement learning
André Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel K Toyama, Jonathan J Hunt, Shibl Mourad, David Silver, et al · 2019
Cited alongside, same era.
The loca regret: a consistent metric to evaluate model-based behavior in reinforcement learning
Harm Van Seijen, Hadi Nekoei, Evan Racah, and Sarath Chandar · 2020
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Deep reinforcement learning amidst lifelong non-stationarity
Annie Xie, James Harrison, and Chelsea Finn · 2020
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Deep reinforcement learning at the edge of the statistical precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C Courville, and Marc Bellemare · 2021
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Continual model-based reinforcement learning with hypernetworks
Yizhou Huang, Kevin Xie, Homanga Bharadhwaj, and Florian Shkurti · 2021
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Same state, different task: Continual reinforcement learning without interference
Samuel Kessler, Jack Parker-Holder, Philip Ball, Stefan Zohren, and Stephen J Roberts · 2021
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Deep reinforcement learning for efficient measurement of quantum devices
V Nguyen, SB Orbell, Dominic T Lennon, Hyungil Moon, Florian Vigneau, Leon C Camenzind, Liuqi Yu, Dominik M Zumbühl, G Andrew D Briggs, Michael A Osborne, et al · 2021
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Cora: Benchmarks, baselines, and metrics as a platform for continual reinforcement learning agents
Sam Powers, Eliot Xing, Eric Kolve, Roozbeh Mottaghi, and Abhinav Gupta · 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 Kuttler, Edward Grefenstette, and Tim Rocktäschel · 2021
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Open-ended learning leads to generally capable agents
Open Ended Learning Team, Adam Stooke, Anuj Mahajan, Catarina Barros, Charlie Deck, Jakob Bauer, Jakub Sygnowski, Maja Trebacz, Max Jaderberg, Michael Mathieu, et al · 2021
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Continual world: A robotic benchmark for continual reinforcement learning
Maciej Wolczyk, Michal Zajac, Razvan Pascanu, Lukasz Kucinski, and Piotr Milos · 2021
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Task-agnostic continual reinforcement learning: In praise of a simple baseline
Massimo Caccia, Jonas Mueller, Taesup Kim, Laurent Charlin, and Rasool Fakoor · 2022
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Magnetic control of tokamak plasmas through deep reinforcement learning
Jonas Degrave, Federico Felici, Jonas Buchli, Michael Neunert, Brendan Tracey, Francesco Carpanese, Timo Ewalds, Roland Hafner, Abbas Abdolmaleki, Diego de Las Casas, et al · 2022
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Model-based lifelong reinforcement learning with bayesian exploration
Haotian Fu, Shangqun Yu, Michael Littman, and George Konidaris · 2022
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Building a subspace of policies for scalable continual learning
Jean-Baptiste Gaya, Thang Doan, Lucas Caccia, Laure Soulier, Ludovic Denoyer, and Roberta Raileanu · 2022
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Evolving curricula with regret-based environment design
Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob Foerster, Edward Grefenstette, and Tim Rocktäschel · 2022
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Reactive exploration to cope with non-stationarity in lifelong reinforcement learning
Christian Steinparz, Thomas Schmied, Fabian Paischer, Marius-Constantin Dinu, Vihang Patil, Angela Bitto-Nemling, Hamid Eghbal-zadeh, and Sepp Hochreiter · 2022
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Towards evaluating adaptivity of model-based reinforcement learning methods
Yi Wan, Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Sarath Chandar, and Harm H Van Seijen · 2022
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Mastering diverse domains through world models
Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap · 2023
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