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Offline reinforcement learning (RL) aims to learn optimal policies from previously collected datasets.
Rank analysis of incomplete block designs: I. the method of paired comparisons
Ralph Allan Bradley and Milton E Terry · 1952
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Markov decision processes: discrete stochastic dynamic programming
Martin L Puterman · 2014
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Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015
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Variational inference: A review for statisticians
David M Blei, Alp Kucukelbir, and Jon D McAuliffe · 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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Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Hoof, and David Meger · 2018
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2019
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Advantage-weighted regression: Simple and scalable off-policy reinforcement learning
Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Mish: A self regularized non-monotonic activation function
Diganta Misra · 2019
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Stabilizing off-policy q-learning via bootstrapping error reduction
Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, and Sergey Levine · 2019
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Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 2019
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 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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Awac: Accelerating online reinforcement learning with offline datasets
Ashvin Nair, Abhishek Gupta, Murtaza Dalal, and Sergey Levine · 2020
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Critic regularized regression
Ziyu Wang, Alexander Novikov, Konrad Zolna, Josh S Merel, Jost Tobias Springenberg, Scott E Reed, Bobak Shahriari, Noah Siegel, Caglar Gulcehre, Nicolas Heess, et al · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 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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Bail: Best-action imitation learning for batch deep reinforcement learning
Xinyue Chen, Zijian Zhou, Zheng Wang, Che Wang, Yanqiu Wu, and Keith Ross · 2020
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An optimistic perspective on offline reinforcement learning
Rishabh Agarwal, Dale Schuurmans, and Mohammad Norouzi · 2020
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Plas: Latent action space for offline reinforcement learning
Wenxuan Zhou, Sujay Bajracharya, and David Held · 2020
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Offline reinforcement learning via high-fidelity generative behavior modeling
Huayu Chen, Cheng Lu, Chengyang Ying, Hang Su, and Jun Zhu · 2022
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Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu · 2022
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Is conditional generative modeling all you need for decision making?
Anurag Ajay, Yilun Du, Abhi Gupta, Joshua B Tenenbaum, Tommi S Jaakkola, and Pulkit Agrawal · 2022
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Prompting decision transformer for few-shot policy generalization
Mengdi Xu, Yikang Shen, Shun Zhang, Yuchen Lu, Ding Zhao, Joshua Tenenbaum, and Chuang Gan · 2022
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A note on dpo with noisy preferences and relationship to ipo
Eric Mitchell · 2023
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Hierarchical diffusion for offline decision making
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Combo: Conservative offline model-based policy optimization
Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, and Chelsea Finn · 2021
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Offline reinforcement learning with implicit q-learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2021
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A minimalist approach to offline reinforcement learning
Scott Fujimoto and Shixiang Shane Gu · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Conservative offline distributional reinforcement learning
Yecheng Ma, Dinesh Jayaraman, and Osbert Bastani · 2021
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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
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Uncertainty-based offline reinforcement learning with diversified q-ensemble
Gaon An, Seungyong Moon, Jang-Hyun Kim, and Hyun Oh Song · 2021
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Wenhao Li, Xiangfeng Wang, Bo Jin, and Hongyuan Zha · 2023
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Contrastive energy prediction for exact energy-guided diffusion sampling in offline reinforcement learning
Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, and Jun Zhu · 2023
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Idql: Implicit q-learning as an actor-critic method with diffusion policies
Philippe Hansen-Estruch, Ilya Kostrikov, Michael Janner, Jakub Grudzien Kuba, and Sergey Levine · 2023
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Equidiff: A conditional equivariant diffusion model for trajectory prediction
Kehua Chen, Xianda Chen, Zihan Yu, Meixin Zhu, and Hai Yang · 2023
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Mutual information regularized offline reinforcement learning
Xiao Ma, Bingyi Kang, Zhongwen Xu, Min Lin, and Shuicheng Yan · 2024
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Efficient diffusion policies for offline reinforcement learning
Bingyi Kang, Xiao Ma, Chao Du, Tianyu Pang, and Shuicheng Yan · 2024
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2024
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Inverse preference learning: Preference-based rl without a reward function
Joey Hejna and Dorsa Sadigh · 2024
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Provably robust dpo: Aligning language models with noisy feedback
Sayak Ray Chowdhury, Anush Kini, and Nagarajan Natarajan · 2024
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POCE: Primal policy optimization with conservative estimation for multi-constraint offline reinforcement learning
Jiayi Guan, Li Shen, Ao Zhou, Lusong Li, Han Hu, Xiaodong He, Guang Chen, and Changjun Jiang · 2024
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Maximum diffusion reinforcement learning
Thomas A Berrueta, Allison Pinosky, and Todd D Murphey · 2024
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Diffusion policies for out-of-distribution generalization in offline reinforcement learning
Suzan Ece Ada, Erhan Oztop, and Emre Ugur · 2024
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