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TD-MPC is a model-based reinforcement learning (RL) algorithm that performs local trajectory optimization in the latent space of a learned implicit (decoder-free) world model.
A markovian decision process
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Rudy R. Negenborn, Bart De Schutter, Marco A. Wiering, and Hans Hellendoorn · 2005
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Efficient selectivity and backup operators in monte-carlo tree search
Rémi Coulom · 2007
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Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
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Maximum entropy inverse reinforcement learning
Brian D Ziebart, Andrew Maas, J Andrew Bagnell, and Anind K Dey · 2008
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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The ycb object and model set: Towards common benchmarks for manipulation research
Berk Calli, Arjun Singh, Aaron Walsman, Siddhartha Srinivasa, Pieter Abbeel, and Aaron M. Dollar · 2015
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Fast and accurate deep network learning by exponential linear units (elus)
Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter · 2015
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Model predictive path integral control using covariance variable importance sampling
Grady Williams, Andrew Aldrich, and Evangelos A. Theodorou · 2015
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Layer normalization
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Faulty reward functions in the wild
Jack Clark and Dario Amodei · 2016
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Deep reinforcement learning with double q-learning
H. V. Hasselt, A. Guez, and D. Silver · 2016
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Continuous control with deep reinforcement learning
T. Lillicrap, J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and Daan Wierstra · 2016
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A distributional perspective on reinforcement learning
Marc G Bellemare, Will Dabney, and Rémi Munos · 2017
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Neural discrete representation learning
Aaron 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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Soft actor-critic algorithms and applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, G. Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, P. Abbeel, and Sergey Levine · 2018
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Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2018
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Deepmind control suite
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Dota 2 with large scale deep reinforcement learning
Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemyslaw Debiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, et al · 2019
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Mish: A self regularized non-monotonic neural activation function
Diganta Misra · 2019
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine · 2019
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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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Bootstrap your own latent: A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altch’e, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko · 2020
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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Ilya Kostrikov, Denis Yarats, and Rob Fergus · 2020
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Objective mismatch in model-based reinforcement learning
Nathan Lambert, Brandon Amos, Omry Yadan, and Roberto Calandra · 2020
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Multi-game decision transformers
Kuang-Huei Lee, Ofir Nachum, Mengjiao Sherry Yang, Lisa Lee, Daniel Freeman, Sergio Guadarrama, Ian Fischer, Winnie Xu, Eric Jang, Henryk Michalewski, et al · 2022
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Vip: Towards universal visual reward and representation via value-implicit pre-training
Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar, and Amy Zhang · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, et al · 2022
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Euclid: Towards efficient unsupervised reinforcement learning with multi-choice dynamics model
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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, et al · 2020
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Curl: Contrastive unsupervised representations for reinforcement learning
Aravind Srinivas, Michael Laskin, and Pieter Abbeel · 2020
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Randomized ensembled double q-learning: Learning fast without a model
Xinyue Chen, Che Wang, Zijian Zhou, and Keith Ross · 2021
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Stabilizing deep q-learning with convnets and vision transformers under data augmentation
Nicklas Hansen, Hao Su, and Xiaolong Wang · 2021
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Learning and planning in complex action spaces, 2021
Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Mohammadamin Barekatain, Simon Schmitt, and David Silver · 2021
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Mastering visual continuous control: Improved data-augmented reinforcement learning
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Finetuning offline world models in the real world
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Offline q-learning on diverse multi-task data both scales and generalizes
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Cal-ql: Calibrated offline rl pre-training for efficient online fine-tuning, 2023
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On the feasibility of cross-task transfer with model-based reinforcement learning
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