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Learning predictive world models is crucial for enhancing the planning capabilities of reinforcement learning (RL) agents.
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, Timothy P. Lillicrap, and David Silver · 1911
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The optimal control of partially observable Markov processes
Edward Jay Sondik · 1971
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Visualizing data using t-SNE
L. V. D. Maaten and G. E. Hinton · 2008
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Multi-armed bandits with episode context
Christopher D Rosin · 2011
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The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 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, 2014
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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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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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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Model predictive control
Basil Kouvaritakis and Mark Cannon · 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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A distributional perspective on reinforcement learning, 2017
Marc G. Bellemare, Will Dabney, and Rémi Munos · 2017
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Deep recurrent q-learning for partially observable mdps, 2017
Matthew Hausknecht and Peter Stone · 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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Mastering chess and shogi by self-play with a general reinforcement learning algorithm
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2017
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Neural discrete representation learning
Aäron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu · 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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David Ha and Jürgen Schmidhuber · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Transformer-xl: Attentive language models beyond a fixed-length context, 2019
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, and Ruslan Salakhutdinov · 2019
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Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2019
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When to trust your model: Model-based policy optimization
Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine · 2019
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Stabilizing transformers for reinforcement learning, 2019
Emilio Parisotto, H. Francis Song, Jack W. Rae, Razvan Pascanu, Caglar Gulcehre, Siddhant M. Jayakumar, Max Jaderberg, Raphael Lopez Kaufman, Aidan Clark, Seb Noury, Matthew M. Botvinick, Nicolas Heess, and Raia Hadsell · 2019
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Never give up: Learning directed exploration strategies
Adrià Puigdomènech Badia, Pablo Sprechmann, Alex Vitvitskyi, Daniel Guo, Bilal Piot, Steven Kapturowski, Olivier Tieleman, Martín Arjovsky, Alexander Pritzel, Andew Bolt, et al · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Monte-carlo tree search as regularized policy optimization, 2020
Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, and Rémi Munos · 2020
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The value equivalence principle for model-based reinforcement learning, 2020
Christopher Grimm, André Barreto, Satinder Singh, and David Silver · 2020
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Mastering atari with discrete world models
Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, and Jimmy Ba · 2020
Transformers are sample-efficient world models
Vincent Micheli, Eloi Alonso, and François Fleuret · 2022
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A generalist agent, 2022
Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, and Nando de Freitas · 2022
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The alberta plan for ai research
Richard S Sutton, Michael Bowling, and Patrick M Pilarski · 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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Predictive auxiliary objectives in deep rl mimic learning in the brain, 2023
Ching Fang and Kimberly L Stachenfeld · 2023
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dm control: Software and tasks for continuous control
Saran Tunyasuvunakool, Alistair Muldal, Yotam Doron, Siqi Liu, Steven Bohez, Josh Merel, Tom Erez, Timothy Lillicrap, Nicolas Heess, and Yuval Tassa · 2020
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Gradient surgery for multi-task learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn · 2020
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Planning in stochastic environments with a learned model
Ioannis Antonoglou, Julian Schrittwieser, Sherjil Ozair, Thomas K Hubert, and David Silver · 2021
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Decision transformer: Reinforcement learning via sequence modeling, 2021
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch · 2021
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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
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Joery A de Vries, Ken S Voskuil, Thomas M Moerland, and Aske Plaat · 2021
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Learning and planning in complex action spaces
Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Mohammadamin Barekatain, Simon Schmitt, and David Silver · 2021
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Model tells you what to discard: Adaptive kv cache compression for llms, 2023
Suyu Ge, Yunan Zhang, Liyuan Liu, Minjia Zhang, Jiawei Han, and Jianfeng Gao · 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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Td-mpc2: Scalable, robust world models for continuous control
Nicklas Hansen, Hao Su, and Xiaolong Wang · 2023
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Reasoning with language model is planning with world model
Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, and Zhiting Hu · 2023
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Teaching arithmetic to small transformers, 2023
Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, Kangwook Lee, and Dimitris Papailiopoulos · 2023
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Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
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Transformer-based world models are happy with 100k interactions, 2023
Jan Robine, Marc Höftmann, Tobias Uelwer, and Stefan Harmeling · 2023
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Bigger, better, faster: Human-level atari with human-level efficiency
Max Schwarzer, Johan Samir Obando Ceron, Aaron Courville, Marc G Bellemare, Rishabh Agarwal, and Pablo Samuel Castro · 2023
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Roformer: Enhanced transformer with rotary position embedding, 2023
Jianlin Su, Yu Lu, Shengfeng Pan, Ahmed Murtadha, Bo Wen, and Yunfeng Liu · 2023
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Monte carlo tree search: A review of recent modifications and applications
Maciej Świechowski, Konrad Godlewski, Bartosz Sawicki, and Jacek Mańdziuk · 2023
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Storm: Efficient stochastic transformer based world models for reinforcement learning, 2023
Weipu Zhang, Gang Wang, Jian Sun, Yetian Yuan, and Gao Huang · 2023
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Bench2drive: Towards multi-ability benchmarking of closed-loop end-to-end autonomous driving
Xiaosong Jia, Zhenjie Yang, Qifeng Li, Zhiyuan Zhang, and Junchi Yan · 2024
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Model-based reinforcement learning for atari, 2024
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When do transformers shine in rl? decoupling memory from credit assignment
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