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CORL is an open-source library that provides thoroughly benchmarked single-file implementations of both deep offline and offline-to-online reinforcement learning algorithms.
Catalyst.rl: A distributed framework for reproducible rl research, 2019
Sergey Kolesnikov and Oleksii Hrinchuk · 1903
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rlpyt: A research code base for deep reinforcement learning in pytorch, 2019
Adam Stooke and Pieter Abbeel · 1909
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
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Benchmarking deep reinforcement learning for continuous control
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, and Pieter Abbeel · 2016
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Openai baselines
Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, Yuhuai Wu, and Peter Zhokhov · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Dopamine: A research framework for deep reinforcement learning
Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, and Marc G Bellemare · 2018
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Horizon: Facebook’s open source applied reinforcement learning platform
Jason Gauci, Edoardo Conti, Yitao Liang, Kittipat Virochsiri, Zhengxing Chen, Yuchen He, Zachary Kaden, Vivek Narayanan, and Xiaohui Ye · 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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Stable baselines
Ashley Hill, Antonin Raffin, Maximilian Ernestus, Adam Gleave, Anssi Kanervisto, Rene Traore, Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, and Yuhuai Wu · 2018
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RLlib: Abstractions for distributed reinforcement learning
Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph E. Gonzalez, Michael I. Jordan, and Ion Stoica · 2018
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Garage: A toolkit for reproducible reinforcement learning research
The garage contributors · 2019
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Aim, 6 2020
Gor Arakelyan, Gevorg Soghomonyan, and The Aim team · 2020
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Experiment tracking with weights and biases, 2020
Lukas Biewald · 2020
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Implementation matters in deep rl: A case study on ppo and trpo
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, L. 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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Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
Cited alongside, same era.
Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Offline reinforcement learning with implicit q-learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2021
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A workflow for offline model-free robotic reinforcement learning
Aviral Kumar, Anikait Singh, Stephen Tian, Chelsea Finn, and Sergey Levine · 2021
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ElegantRL: Massively parallel framework for cloud-native deep reinforcement learning
Xiao-Yang Liu, Zechu Li, Zhaoran Wang, and Jiahao Zheng · 2021
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d3rlpy: An offline deep reinforcement library
Michita Imai Takuma Seno · 2021
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Tianshou: A highly modularized deep reinforcement learning library
Jiayi Weng, Huayu Chen, Dong Yan, Kaichao You, Alexis Duburcq, Minghao Zhang, Yi Su, Hang Su, and Jun Zhu · 2021
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Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
Cited alongside, same era.
Accelerating online reinforcement learning with offline datasets
Ashvin Nair, Murtaza Dalal, Abhishek Gupta, and Sergey Levine · 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.
Decision transformer: Reinforcement learning via sequence modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Misha Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch · 2021
Cited alongside, same era.
Umbrella: Uncertainty-aware model-based offline reinforcement learning leveraging planning
Christopher P. Diehl, Timo Sievernich, Martin Krüger, Frank Hoffmann, and Torsten Bertram · 2021
Cited alongside, same era.
A minimalist approach to offline reinforcement learning
Scott Fujimoto and Shixiang Shane Gu · 2021
Cited alongside, same era.
Off-policy actor-critic for recommender systems
Minmin Chen, Can Xu, Vince Gatto, Devanshu Jain, Aviral Kumar, and Ed H. Chi · 2022
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Why so pessimistic? estimating uncertainties for offline rl through ensembles, and why their independence matters
Kamyar Ghasemipour, Shixiang Shane Gu, and Ofir Nachum · 2022
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Showing your offline reinforcement learning work: Online evaluation budget matters
Vladislav Kurenkov and Sergey Kolesnikov · 2022
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A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning, August 2022
Laura Smith, Ilya Kostrikov, and Sergey Levine · 2022
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EnvPool: A highly parallel reinforcement learning environment execution engine
Jiayi Weng, Min Lin, Shengyi Huang, Bo Liu, Denys Makoviichuk, Viktor Makoviychuk, Zichen Liu, Yufan Song, Ting Luo, Yukun Jiang, Zhongwen Xu, and Shuicheng Yan · 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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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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Revisiting the minimalist approach to offline reinforcement learning
Denis Tarasov, Vladislav Kurenkov, Alexander Nikulin, and Sergey Kolesnikov · 2023
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