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We consider a hybrid reinforcement learning setting (Hybrid RL), in which an agent has access to an offline dataset and the ability to collect experience via real-world online interaction.
A framework for behavioural cloning
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A natural policy gradient
Sham M Kakade · 2001
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Approximately optimal approximate reinforcement learning
Sham M Kakade and John Langford · 2002
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Policy search by dynamic programming
James Bagnell, Sham M Kakade, Jeff Schneider, and Andrew Ng · 2003
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Stochastic linear optimization under bandit feedback
Varsha Dani, Thomas P Hayes, and Sham M Kakade · 2008
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Finite-time bounds for fitted value iteration
Rémi Munos and Csaba Szepesvári · 2008
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Contextual bandit algorithms with supervised learning guarantees
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Agnostic system identification for model-based reinforcement learning
Stephane Ross and J Andrew Bagnell · 2012
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The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
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Boosted bellman residual minimization handling expert demonstrations
Bilal Piot, Matthieu Geist, and Olivier Pietquin · 2014
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Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
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Convex optimization: Algorithms and complexity
Sébastien Bubeck et al · 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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Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2015
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2016
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Deep reinforcement learning with double Q-learning
Hado Van Hasselt, Arthur Guez, and David Silver · 2016
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Dueling network architectures for deep reinforcement learning
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot, and Nando Freitas · 2016
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Contextual decision processes with low Bellman rank are PAC-learnable
Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, and Robert E Schapire · 2017
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Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, 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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Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
Mel Vecerik, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Heess, Thomas Rothörl, Thomas Lampe, and Martin Riedmiller · 2017
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Exploration by random network distillation
Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov · 2018
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On oracle-efficient PAC RL with rich observations
Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, and Robert E Schapire · 2018
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Soft actor-critic algorithms and applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, and Sergey Levine · 2018
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Deep Q-learning from demonstrations
Todd Hester, Matej Vecerik, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, John Agapiou, Joel Z. Leibo, and Audrunas Gruslys · 2018
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Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, et al · 2018
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Bilinear classes: A structural framework for provable generalization in RL
Simon Du, Sham Kakade, Jason Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, and Ruosong Wang · 2021
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Offline reinforcement learning: Fundamental barriers for value function approximation
Dylan J Foster, Akshay Krishnamurthy, David Simchi-Levi, and Yunzong Xu · 2021
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A minimalist approach to offline reinforcement learning
Scott Fujimoto and Shixiang Shane Gu · 2021
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Offline reinforcement learning with implicit Q-learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2021
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Model-free representation learning and exploration in low-rank MDPs
Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, and Alekh Agarwal · 2021
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Yao Liu and Emma Brunskill · 2018
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Overcoming exploration in reinforcement learning with demonstrations
Ashvin Nair, Bob McGrew, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2018
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Politex: Regret bounds for policy iteration using expert prediction
Yasin Abbasi-Yadkori, Peter Bartlett, Kush Bhatia, Nevena Lazic, Csaba Szepesvari, and Gellért Weisz · 2019
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Reinforcement learning: Theory and algorithms
Alekh Agarwal, Nan Jiang, Sham M Kakade, and Wen Sun · 2019
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Information-theoretic considerations in batch reinforcement learning
Jinglin Chen and Nan Jiang · 2019
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Model-based RL in contextual decision processes: PAC bounds and exponential improvements over model-free approaches
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, and John Langford · 2019
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Sample-optimal parametric Q-learning using linearly additive features
Lin Yang and Mengdi Wang · 2019
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Agent57: Outperforming the Atari human benchmark
Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Zhaohan Daniel Guo, and Charles Blundell · 2020
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Agnostic reinforcement learning with low-rank MDPs and rich observations
Ayush Sekhari, Christoph Dann, Mehryar Mohri, Yishay Mansour, and Karthik Sridharan · 2021
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Pessimistic model-based offline reinforcement learning under partial coverage
Masatoshi Uehara and Wen Sun · 2021
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Representation learning for online and offline RL in low-rank MDPs
Masatoshi Uehara, Xuezhou Zhang, and Wen Sun · 2021
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Instabilities of offline rl with pre-trained neural representation
Ruosong Wang, Yifan Wu, Ruslan Salakhutdinov, and Sham Kakade · 2021
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Batch value-function approximation with only realizability
Tengyang Xie and Nan Jiang · 2021
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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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Provable benefits of actor-critic methods for offline reinforcement learning
Andrea Zanette, Martin J Wainwright, and Emma Brunskill · 2021
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Offline reinforcement learning under value and density-ratio realizability: the power of gaps
Jinglin Chen and Nan Jiang · 2022
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Guarantees for epsilon-greedy reinforcement learning with function approximation
Chris Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, and Karthik Sridharan · 2022
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BYOL-explore: Exploration by bootstrapped prediction
Zhaohan Daniel Guo, Shantanu Thakoor, Miruna Pîslar, Bernardo Avila Pires, Florent Altché, Corentin Tallec, Alaa Saade, Daniele Calandriello, Jean-Bastien Grill, Yunhao Tang, Michal Valko, R’emi Munos, Mohammad Gheshlaghi Azar, and Bilal Piot · 2022
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Improving policy optimization with generalist-specialist learning
Zhiwei Jia, Xuanlin Li, Zhan Ling, Shuang Liu, Yiran Wu, and Hao Su · 2022
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Computational-statistical gaps in reinforcement learning
Daniel Kane, Sihan Liu, Shachar Lovett, and Gaurav Mahajan · 2022
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Offline-to-online reinforcement learning via balanced replay and pessimistic q-ensemble
Seunghyun Lee, Younggyo Seo, Kimin Lee, Pieter Abbeel, and Jinwoo Shin · 2022
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When to trust your simulator: Dynamics-aware hybrid offline-and-online reinforcement learning
Haoyi Niu, Shubham Sharma, Yiwen Qiu, Ming Li, Guyue Zhou, Jianming Hu, and Xianyuan Zhan · 2022
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Contrastive UCB: Provably efficient contrastive self-supervised learning in online reinforcement learning
Shuang Qiu, Lingxiao Wang, Chenjia Bai, Zhuoran Yang, and Zhaoran Wang · 2022
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Jump-start reinforcement learning
Ikechukwu Uchendu, Ted Xiao, Yao Lu, Banghua Zhu, Mengyuan Yan, Joséphine Simon, Matthew Bennice, Chuyuan Fu, Cong Ma, Jiantao Jiao, Sergey Levine, and Karol Hausman · 2022
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Offline reinforcement learning with realizability and single-policy concentrability
Wenhao Zhan, Baihe Huang, Audrey Huang, Nan Jiang, and Jason Lee · 2022
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