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Recently, methods such as Decision Transformer that reduce reinforcement learning to a prediction task and solve it via supervised learning (RvS) have become popular due to their simplicity, robustness to hyperparameters, and strong overall performance on offline RL tasks.
Behavior Regularized Offline Reinforcement Learning
Yifan Wu, George Tucker, and Ofir Nachum · 1911
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Reinforcement Learning Upside Down: Don’t Predict Rewards - Just Map Them to Actions
Jürgen Schmidhuber · 1912
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Aviral Kumar, Xue Bin Peng, and Sergey Levine · 1912
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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D4RL: Datasets for Deep Data-Driven Reinforcement Learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2004
Earlier work this paper cites.
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2005
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MuJoCo: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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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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2048, March 2014
Gabriele Cirulli · 2014
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Generative Adversarial Networks
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014
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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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Categorical Reparameterization with Gumbel-Softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
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The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
Chris J. Maddison, Andriy Mnih, and Yee Whye Teh · 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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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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Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations
Alex Beutel, Jilin Chen, Zhe Zhao, and Ed H. Chi · 2017
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Domain-Adversarial Training of Neural Networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor S. Lempitsky · 2017
Cited alongside, same era.
Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents
Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew J. Hausknecht, and Michael Bowling · 2018
Cited alongside, same era.
Mitigating Unwanted Biases with Adversarial Learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell · 2018
Cited alongside, same era.
Decision Transformer: Reinforcement Learning via Sequence Modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch · 2021
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RvS: What is Essential for Offline RL via Supervised Learning?
Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov, and Sergey Levine · 2021
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Learning to Reach Goals via Iterated Supervised Learning
Dibya Ghosh, Abhishek Gupta, Ashwin Reddy, Justin Fu, Coline Manon Devin, Benjamin Eysenbach, and Sergey Levine · 2021
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Planning from Pixels using Inverse Dynamics Models
Keiran Paster, Sheila A. McIlraith, and Jimmy Ba · 2021
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A Minimalist Approach to Offline Reinforcement Learning
Scott Fujimoto and Shixiang Shane Gu · 2021
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Language Models are Unsupervised Multitask Learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Cited alongside, same era.
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction
Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, and Sergey Levine · 2019
Cited alongside, same era.
Decoupled Weight Decay Regularization
Ilya Loshchilov and Frank Hutter · 2019
Cited alongside, same era.
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, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Conservative Q-Learning for Offline Reinforcement Learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
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An Optimistic Perspective on Offline Reinforcement Learning
Rishabh Agarwal, Dale Schuurmans, and Mohammad Norouzi · 2020
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Language Models are Few-Shot Learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared 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 M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher 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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Pedro A. Ortega, Markus Kunesch, Grégoire Delétang, Tim Genewein, Jordi Grau-Moya, Joel Veness, Jonas Buchli, Jonas Degrave, Bilal Piot, Julien Pérolat, Tom Everitt, Corentin Tallec, Emilio Parisotto, Tom Erez, Yutian Chen, Scott E. Reed, Marcus Hutter, Nando de Freitas, and Shane Legg · 2021
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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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Stable-Baselines3: Reliable Reinforcement Learning Implementations
Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, and Noah Dormann · 2021
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d3rlpy: An Offline Deep Reinforcement Learning Library
Takuma Seno and Michita Imai · 2021
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Training Compute-Optimal Large Language Models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and Laurent Sifre · 2022
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Flamingo: a Visual Language Model for Few-Shot Learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andrew Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, and Karen Simonyan · 2022
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Generalized Decision Transformer for Offline Hindsight Information Matching
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Can Wikipedia Help Offline Reinforcement Learning?
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Dichotomy of Control: Separating What You Can Control from What You Cannot
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