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Recent improvements in conditional generative modeling have made it possible to generate high-quality images from language descriptions alone.
Learning to predict by the methods of temporal differences
Richard S. Sutton · 1988
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Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen · 2005
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Markov decision processes: discrete stochastic dynamic programming
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Adam: A method for stochastic optimization
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Deep unsupervised learning using nonequilibrium thermodynamics
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Deep reinforcement learning and the deadly triad
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Group normalization
Yuxin Wu and Kaiming He · 2018
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2019
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Aviral Kumar, Xue Bin Peng, and Sergey Levine · 2019
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Mish: A self regularized non-monotonic neural activation function
Diganta Misra · 2019
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Learning non-convergent non-persistent short-run MCMC toward energy-based model
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Juergen Schmidhuber · 2019
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Training agents using upside-down reinforcement learning
Rupesh Kumar Srivastava, Pranav Shyam, Filipe Mutz, Wojciech Jaśkowski, and Jürgen Schmidhuber · 2019
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Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 2019
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Opal: Offline primitive discovery for accelerating offline reinforcement learning
Anurag Ajay, Aviral Kumar, Pulkit Agrawal, Sergey Levine, and Ofir Nachum · 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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
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Guidance: a cheat code for diffusion models, 2022
Sander Dieleman · 2022
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Offline rl policies should be trained to be adaptive
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Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
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Learning the stein discrepancy for training and evaluating energy-based models without sampling
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Offline reinforcement learning with pseudometric learning
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Solving quantitative reasoning problems with language models
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Imitating human behaviour with diffusion models
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