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World models are a fundamental component in model-based reinforcement learning (MBRL).
Memory and consciousness
Endel Tulving · 1985
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Prefix sums and their applications
Guy Blelloch · 1990
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Mental time travel and the shaping of the human mind
Thomas Suddendorf, Donna Rose Addis, and Michael C Corballis · 2009
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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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On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio · 2013
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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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Jimmy Ba, Jamie Ryan Kiros, and Geoffrey Hinton · 2016
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Charles Beattie, Joel Z Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich Küttler, Andrew Lefrancq, Simon Green, Víctor Valdés, Amir Sadik, et al · 2016
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Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V Le · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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Minimalistic gridworld environment for Gymnasium, 2018
Maxime Chevalier-Boisvert, Lucas Willems, and Suman Pal · 2018
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Recurrent world models facilitate policy evolution
David Ha and Jürgen Schmidhuber · 2018
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ViZDoom competitions: Playing Doom from pixels
Marek Wydmuch, Michał Kempka, and Wojciech Jaśkowski · 2018
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Transformer-XL: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc Le, and Ruslan Salakhutdinov · 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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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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The human imagination: the cognitive neuroscience of visual mental imagery
Joel Pearson · 2019
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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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HiPPO: Recurrent memory with optimal polynomial projections
Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, and Christopher Ré · 2020
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Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2020
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Stabilizing Transformers for reinforcement learning
Emilio Parisotto, Francis Song, Jack Rae, Razvan Pascanu, Caglar Gulcehre, Siddhant Jayakumar, Max Jaderberg, Raphaël Lopez Kaufman, Aidan Clark, Seb Noury, Matthew Botvinick, Nicolas Heess, and Raia Hadsell · 2020
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TransDreamer: Reinforcement learning with Transformer world models
Chang Chen, Yi-Fu Wu, Jaesik Yoon, and Sungjin Ahn · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Taming Transformers for high-resolution image synthesis
Patrick Esser, Robin Rombach, and Bjorn Ommer · 2021
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Learning task informed abstractions
Xiang Fu, Ge Yang, Pulkit Agrawal, and Tommi Jaakkola · 2021
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Combining recurrent, convolutional, and continuous-time models with linear state space layers
Albert Gu, Isys Johnson, Karan Goel, Khaled Kamal Saab, Tri Dao, Atri Rudra, and Christopher Ré · 2021
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Mastering Atari with discrete world models
Efficient long sequence modeling via state space augmented Transformer
Simiao Zuo, Xiaodong Liu, Jian Jiao, Denis Charles, Eren Manavoglu, Tuo Zhao, and Jianfeng Gao · 2022
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Decision S4: Efficient sequence-based RL via state spaces layers
Shmuel Bar David, Itamar Zimerman, Eliya Nachmani, and Lior Wolf · 2023
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Hungry Hungry Hippos: Towards language modeling with state space models
Daniel Y Fu, Tri Dao, Khaled Kamal Saab, Armin W Thomas, Atri Rudra, and Christopher Ré · 2023
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How to train your HiPPO: State space models with generalized orthogonal basis projections
Albert Gu, Isys Johnson, Aman Timalsina, Atri Rudra, and Christopher Ré · 2023
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Dream to generalize: Zero-shot model-based reinforcement learning for unseen visual distractions
Jeongsoo Ha, Kyungsoo Kim, and Yusung Kim · 2023
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Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, and Jimmy Ba · 2021
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Towards mental time travel: a hierarchical memory for reinforcement learning agents
Andrew Kyle Lampinen, Stephanie C.Y. Chan, Andrea Banino, and Felix Hill · 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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Clockwork variational autoencoders
Vaibhav Saxena, Jimmy Ba, and Danijar Hafner · 2021
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Long Range Arena: A benchmark for efficient Transformers
Yi Tay, Mostafa Dehghani, Samira Abnar, Yikang Shen, Dara Bahri, Philip Pham, Jinfeng Rao, Liu Yang, Sebastian Ruder, and Donald Metzler · 2021
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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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Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap · 2023
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Modelling long range dependencies in N N D: From task-specific to a general purpose CNN
David M Knigge, David W Romero, Albert Gu, Efstratios Gavves, Erik J Bekkers, Jakub Mikolaj Tomczak, Mark Hoogendoorn, and Jan-jakob Sonke · 2023
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Structured state space models for in-context reinforcement learning
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Long range language modeling via gated state spaces
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Transformers are sample-efficient world models
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Model-based reinforcement learning: A survey
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Resurrecting recurrent neural networks for long sequences
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Memory Gym: Partially observable challenges to memory-based agents
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Transformer-based world models are happy with 100k interactions
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Masked world models for visual control
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Simplified state space layers for sequence modeling
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Selective structured state-spaces for long-form video understanding
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Temporally consistent Transformers for video generation
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Reward informed dreamer for task generalization in reinforcement learning
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Deep latent state space models for time-series generation
Linqi Zhou, Michael Poli, Winnie Xu, Stefano Massaroli, and Stefano Ermon · 2023
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