Fetching the paper…
Reading the bibliography…
Existing offline reinforcement learning (RL) methods face a few major challenges, particularly the distributional shift between the learned policy and the behavior policy.
Lanqing Li, Rui Yang, and Dijun Luo · 2010
Earlier work this paper cites.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Earlier work this paper cites.
Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Hoof, and David Meger · 2018
Earlier work this paper cites.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
Earlier work this paper cites.
On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
Earlier work this paper cites.
Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2019
Earlier work this paper cites.
When to trust your model: Model-based policy optimization
Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine · 2019
Earlier work this paper cites.
Stabilizing off-policy q-learning via bootstrapping error reduction
Aviral Kumar, Justin Fu, George Tucker, and Sergey Levine · 2019
Earlier work this paper cites.
Algaedice: Policy gradient from arbitrary experience
Ofir Nachum, Bo Dai, Ilya Kostrikov, Yinlam Chow, Lihong Li, and Dale Schuurmans · 2019
Cited alongside, same era.
Efficient off-policy meta-reinforcement learning via probabilistic context variables
Kate Rakelly, Aurick Zhou, Deirdre Quillen, Chelsea Finn, and Sergey Levine · 2019
Cited alongside, same era.
Efficient meta learning via minibatch proximal update
Pan Zhou, Xiaotong Yuan, Huan Xu, Shuicheng Yan, and Jiashi Feng · 2019
Cited alongside, same era.
Varibad: A very good method for bayes-adaptive deep rl via meta-learning
Luisa Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, and Shimon Whiteson · 2019
Cited alongside, same era.
Off-policy policy gradient with stationary distribution correction
Yao Liu, Adith Swaminathan, Alekh Agarwal, and Emma Brunskill · 2020
Later among the works it cites.
Deployment-efficient reinforcement learning via model-based offline optimization
Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo, Ofir Nachum, and Shixiang Gu · 2020
Later among the works it cites.
Offline meta-reinforcement learning with advantage weighting
Eric Mitchell, Rafael Rafailov, Xue Bin Peng, Sergey Levine, and Chelsea Finn · 2020
Later among the works it cites.
Meta-learning requires meta-augmentation
Janarthanan Rajendran, Alex Irpan, and Eric Jang · 2020
Later among the works it cites.
On the global optimality of model-agnostic meta-learning
Lingxiao Wang, Qi Cai, Zhuoran Yang, and Zhaoran Wang · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ron Dorfman and Aviv Tamar · 2020
Cited alongside, same era.
D4rl: Datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
Cited alongside, same era.
Morel: Model-based offline reinforcement learning
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, and Thorsten Joachims · 2020
Cited alongside, same era.
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
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
Cited alongside, same era.
Multi-task batch reinforcement learning with metric learning
Jiachen Li, Quan Vuong, Shuang Liu, Minghua Liu, Kamil Ciosek, Henrik Christensen, and Hao Su
Cited in the paper.
Conservative data sharing for multi-task offline reinforcement learning
Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Sergey Levine, and Chelsea Finn
Cited in the paper.
Combo: Conservative offline model-based policy optimization
Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, and Chelsea Finn
Cited in the paper.
Later among the works it cites.
Mopo: Model-based offline policy optimization
Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Zou, Sergey Levine, Chelsea Finn, and Tengyu Ma · 2020
Later among the works it cites.
Continuous doubly constrained batch reinforcement learning
Rasool Fakoor, Jonas Mueller, Pratik Chaudhari, and Alexander J Smola · 2021
Later among the works it cites.
Improving generalization in meta-learning via task augmentation
Huaxiu Yao, Long-Kai Huang, Linjun Zhang, Ying Wei, Li Tian, James Zou, Junzhou Huang, et al · 2021
Later among the works it cites.