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Offline-to-online reinforcement learning (RL), by combining the benefits of offline pretraining and online finetuning, promises enhanced sample efficiency and policy performance.
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Reinforcement learning in robotics: Applications and real-world challenges
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Markov decision processes: discrete stochastic dynamic programming
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Continuous control with deep reinforcement learning
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Human-level control through deep reinforcement learning
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Reinforcement learning with deep energy-based policies
Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, and Sergey Levine · 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
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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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Trust-PCL: An off-policy trust region method for continuous control
Ofir Nachum, Mohammad Norouzi, Kelvin Xu, and Dale Schuurmans · 2018
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Challenges of real-world reinforcement learning
Gabriel Dulac-Arnold, Daniel Mankowitz, and Todd Hester · 2019
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Imitation learning via off-policy distribution matching
Ilya Kostrikov, Ofir Nachum, and Jonathan Tompson · 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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Batch policy learning under constraints
Hoang Le, Cameron Voloshin, and Yisong Yue · 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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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
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Curl: Contrastive unsupervised representations for reinforcement learning
Michael Laskin, Aravind Srinivas, and Pieter Abbeel · 2020
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d3rlpy: An offline deep reinforcement learning library
Takuma Seno and Michita Imai · 2022
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Jump-start reinforcement learning
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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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A policy-guided imitation approach for offline reinforcement learning
Haoran Xu, Li Jiang, Jianxiong Li, and Xianyuan Zhan · 2022
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Discriminator-weighted offline imitation learning from suboptimal demonstrations
Haoran Xu, Xianyuan Zhan, Honglei Yin, and Huiling Qin · 2022
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Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 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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Leverage the average: an analysis of kl regularization in reinforcement learning
Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos, and Matthieu Geist · 2020
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Pessimistic bootstrapping for uncertainty-driven offline reinforcement learning
Chenjia Bai, Lingxiao Wang, Zhuoran Yang, Zhi-Hong Deng, Animesh Garg, Peng Liu, and Zhaoran Wang · 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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Randomized ensembled double q-learning: Learning fast without a model
Xinyue Chen, Che Wang, Zijian Zhou, and Keith W Ross · 2021
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Score: Spurious correlation reduction for offline reinforcement learning
Zhihong Deng, Zuyue Fu, Lingxiao Wang, Zhuoran Yang, Chenjia Bai, Zhaoran Wang, and Jing Jiang · 2021
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Yi Zhao, Rinu Boney, Alexander Ilin, Juho Kannala, and Joni Pajarinen · 2022
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Online decision transformer
Qinqing Zheng, Amy Zhang, and Aditya Grover · 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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Extreme q-learning: Maxent RL without entropy
Divyansh Garg, Joey Hejna, Matthieu Geist, and Stefano Ermon · 2023
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Confidence-conditioned value functions for offline reinforcement learning
Joey Hong, Aviral Kumar, and Sergey Levine · 2023
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Mind the gap: Offline policy optimization for imperfect rewards
Jianxiong Li, Xiao Hu, Haoran Xu, Jingjing Liu, Xianyuan Zhan, Qing-Shan Jia, and Ya-Qin Zhang · 2023
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When data geometry meets deep function: Generalizing offline reinforcement learning
Jianxiong Li, Xianyuan Zhan, Haoran Xu, Xiangyu Zhu, Jingjing Liu, and Ya-Qin Zhang · 2023
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Finetuning from offline reinforcement learning: Challenges, trade-offs and practical solutions
Yicheng Luo, Kay Jackie, Grefenstette Edward, and Deisenroth Marc, Peter · 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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The in-sample softmax for offline reinforcement learning
Chenjun Xiao, Han Wang, Yangchen Pan, Adam White, and Martha White · 2023
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Offline RL with no OOD actions: In-sample learning via implicit value regularization
Haoran Xu, Li Jiang, Jianxiong Li, Zhuoran Yang, Zhaoran Wang, Victor Wai Kin Chan, and Xianyuan Zhan · 2023
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Actor-critic alignment for offline-to-online reinforcement learning, 2023
Zishun Yu and Xinhua Zhang · 2023
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Policy expansion for bridging offline-to-online reinforcement learning
Haichao Zhang, Wei Xu, and Haonan Yu · 2023
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Adaptive policy learning for offline-to-online reinforcement learning
Han Zheng, Xufang Luo, Pengfei Wei, Xuan Song, Dongsheng Li, and Jing Jiang · 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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