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The DreamerV3 agent recently demonstrated state-of-the-art performance in diverse domains, learning powerful world models in latent space using a pixel reconstruction loss.
Learning to predict by the methods of temporal differences
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Backpropagation applied to handwritten zip code recognition
Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel · 1989
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Dyna, an integrated architecture for learning, planning, and reacting
Richard S Sutton · 1991
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
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Efficient selectivity and backup operators in monte-carlo tree search
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 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, et al · 2015
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Embed to control: A locally linear latent dynamics model for control from raw images
Manuel Watter, Jost Springenberg, Joschka Boedecker, and Martin Riedmiller · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Improving pilco with bayesian neural network dynamics models
Yarin Gal, Rowan McAllister, and Carl Edward Rasmussen · 2016
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Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Model-based planning with discrete and continuous actions
Mikael Henaff, William F Whitney, and Yann LeCun · 2017
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The kinetics human action video dataset
Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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The predictron: End-to-end learning and planning
David Silver, Hado Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David Reichert, Neil Rabinowitz, Andre Barreto, et al · 2017
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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Distributional policy gradients
Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, and Timothy Lillicrap · 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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Rainbow: Combining improvements in deep reinforcement learning
Matteo Hessel, Joseph Modayil, Hado Van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2018
Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
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Whitening for self-supervised representation learning
Aleksandr Ermolov, Aliaksandr Siarohin, Enver Sangineto, and Nicu Sebe · 2021
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Mastering atari with discrete world models
Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, and Jimmy Ba · 2021
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Contrastive variational reinforcement learning for complex observations
Xiao Ma, Siwei Chen, David Hsu, and Wee Sun Lee · 2021
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Temporal predictive coding for model-based planning in latent space
Tung D Nguyen, Rui Shu, Tuan Pham, Hung Bui, and Stefano Ermon · 2021
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Dreaming: Model-based reinforcement learning by latent imagination without reconstruction
Masashi Okada and Tadahiro Taniguchi · 2021
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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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Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
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Exploring model-based planning with policy networks
Tingwu Wang and Jimmy Ba · 2019
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Benchmarking model-based reinforcement learning
Tingwu Wang, Xuchan Bao, Ignasi Clavera, Jerrick Hoang, Yeming Wen, Eric Langlois, Shunshi Zhang, Guodong Zhang, Pieter Abbeel, and Jimmy Ba · 2019
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Blast: Latent dynamics models from bootstrapping
Keiran Paster, Lev E McKinney, Sheila A McIlraith, and Jimmy Ba · 2021
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Mastering visual continuous control: Improved data-augmented reinforcement learning
Denis Yarats, Rob Fergus, Alessandro Lazaric, and Lerrel Pinto · 2021
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Mastering atari games with limited data
Weirui Ye, Shaohuai Liu, Thanard Kurutach, Pieter Abbeel, and Yang Gao · 2021
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Barlow twins: Self-supervised learning via redundancy reduction
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny · 2021
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Data2vec: A general framework for self-supervised learning in speech, vision and language
Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, and Michael Auli · 2022
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Video pretraining (vpt): Learning to act by watching unlabeled online videos
Bowen Baker, Ilge Akkaya, Peter Zhokov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, and Jeff Clune · 2022
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Information prioritization through empowerment in visual model-based rl
Homanga Bharadhwaj, Mohammad Babaeizadeh, Dumitru Erhan, and Sergey Levine · 2022
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Dreamerpro: Reconstruction-free model-based reinforcement learning with prototypical representations
Fei Deng, Ingook Jang, and Sungjin Ahn · 2022
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Masked autoencoders as spatiotemporal learners
Christoph Feichtenhofer, Yanghao Li, Kaiming He, et al · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Dreamingv2: Reinforcement learning with discrete world models without reconstruction
Masashi Okada and Tadahiro Taniguchi · 2022
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Masked feature prediction for self-supervised visual pre-training
Chen Wei, Haoqi Fan, Saining Xie, Chao-Yuan Wu, Alan Yuille, and Christoph Feichtenhofer · 2022
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Simmim: A simple framework for masked image modeling
Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu · 2022
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Self-supervised learning from images with a joint-embedding predictive architecture
Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, and Nicolas Ballas · 2023
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Mastering diverse domains through world models
Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap · 2023
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Transformers are sample efficient world models
Vincent Micheli, Eloi Alonso, and François Fleuret · 2023
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