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Recent years have witnessed significant advancements in offline reinforcement learning (RL), resulting in the development of numerous algorithms with varying degrees of complexity.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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The dependence of effective planning horizon on model accuracy
Nan Jiang, Alex Kulesza, Satinder Singh, and Richard Lewis · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Large batch training of convolutional networks
Yang You, Igor Gitman, and Boris Ginsburg · 2017
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Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Hoof, and David Meger · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter 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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Crossnorm: Normalization for off-policy td reinforcement learning
Aditya Bhatt, Max Argus, Artemij Amiranashvili, and Thomas Brox · 2019
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Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 2019
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Large batch optimization for deep learning: Training bert in 76 minutes
Yang You, Jing Li, Sashank Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, and Cho-Jui Hsieh · 2019
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Implementation matters in deep policy gradients: A case study on ppo and trpo
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, and Aleksander Madry · 2020
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D4rl: Datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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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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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
Cited alongside, same era.
D2rl: Deep dense architectures in reinforcement learning
Samarth Sinha, Homanga Bharadhwaj, Aravind Srinivas, and Animesh Garg · 2020
Cited alongside, same era.
Deep reinforcement learning at the edge of the statistical precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron Courville, and Marc G Bellemare · 2021
Cited alongside, same era.
Uncertainty-based offline reinforcement learning with diversified q-ensemble
Gaon An, Seungyong Moon, Jang-Hyun Kim, and Hyun Oh Song · 2021
Cited alongside, same era.
A minimalist approach to offline reinforcement learning
Scott Fujimoto and Shixiang Shane Gu · 2021
Cited alongside, same era.
Challenges and opportunities in offline reinforcement learning from visual observations
Cong Lu, Philip J Ball, Tim GJ Rudner, Jack Parker-Holder, Michael A Osborne, and Yee Whye Teh · 2022
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Scaling laws for a multi-agent reinforcement learning model
Oren Neumann and Claudius Gros · 2022
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Q-ensemble for offline rl: Don’t scale the ensemble, scale the batch size
Alexander Nikulin, Vladislav Kurenkov, Denis Tarasov, Dmitry Akimov, and Sergey Kolesnikov · 2022
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A survey on offline reinforcement learning: Taxonomy, review, and open problems
Rafael Figueiredo Prudencio, Marcos ROA Maximo, and Esther Luna Colombini · 2022
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Dropout q-functions for doubly efficient reinforcement learning
Takuya Hiraoka, Takahisa Imagawa, Taisei Hashimoto, Takashi Onishi, and Yoshimasa Tsuruoka · 2021
Cited alongside, same era.
Offline reinforcement learning with implicit q-learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2021
Cited alongside, same era.
Let offline rl flow: Training conservative agents in the latent space of normalizing flows
Dmitriy Akimov, Vladislav Kurenkov, Alexander Nikulin, Denis Tarasov, and Sergey Kolesnikov · 2022
Cited alongside, same era.
Improving td3-bc: Relaxed policy constraint for offline learning and stable online fine-tuning
Alex Beeson and Giovanni Montana · 2022
Cited alongside, same era.
Seyed Kamyar Seyed Ghasemipour, Shixiang Shane Gu, and Ofir Nachum · 2022
Cited alongside, same era.
On the role of discount factor in offline reinforcement learning
Hao Hu, Yiqin Yang, Qianchuan Zhao, and Chongjie Zhang · 2022
Cited alongside, same era.
Offline q-learning on diverse multi-task data both scales and generalizes
Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, and Sergey Levine · 2022
Cited alongside, same era.
Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, and Matthieu Geist · 2022
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A walk in the park: Learning to walk in 20 minutes with model-free reinforcement learning
Laura Smith, Ilya Kostrikov, and Sergey Levine · 2022
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Corl: Research-oriented deep offline reinforcement learning library
Denis Tarasov, Alexander Nikulin, Dmitry Akimov, Vladislav Kurenkov, and Sergey Kolesnikov · 2022
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Supported policy optimization for offline reinforcement learning
Jialong Wu, Haixu Wu, Zihan Qiu, Jianmin Wang, and Mingsheng Long · 2022
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Rorl: Robust offline reinforcement learning via conservative smoothing
Rui Yang, Chenjia Bai, Xiaoteng Ma, Zhaoran Wang, Chongjie Zhang, and Lei Han · 2022
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Efficient online reinforcement learning with offline data
Philip J Ball, Laura Smith, Ilya Kostrikov, and Sergey Levine · 2023
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Cal-ql: Calibrated offline rl pre-training for efficient online fine-tuning
Mitsuhiko Nakamoto, Yuexiang Zhai, Anikait Singh, Max Sobol Mark, Yi Ma, Chelsea Finn, Aviral Kumar, and Sergey Levine · 2023
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Anti-exploration by random network distillation
Alexander Nikulin, Vladislav Kurenkov, Denis Tarasov, and Sergey Kolesnikov · 2023
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Behavior proximal policy optimization
Zifeng Zhuang, Kun LEI, Jinxin Liu, Donglin Wang, and Yilang Guo · 2023
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