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Reinforcement learning (RL) is a versatile framework for optimizing long-term goals.
On-line Q-learning using connectionist systems , volume 37
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David McAllester, Satinder Singh, and Yishay Mansour · 1999
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Planning treatment of ischemic heart disease with partially observable markov decision processes
Milos Hauskrecht and Hamish Fraser · 2000
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A contextual-bandit approach to personalized news article recommendation
Lihong Li, Wei Chu, John Langford, and Robert E Schapire · 2010
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Doubly robust policy evaluation and learning
Miroslav Dudík, John Langford, and Lihong Li · 2011
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Towards fully autonomous driving: Systems and algorithms
Jesse Levinson, Jake Askeland, Jan Becker, Jennifer Dolson, David Held, Soeren Kammel, J Zico Kolter, Dirk Langer, Oliver Pink, Vaughan Pratt, et al · 2011
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Thompson sampling for contextual bandits with linear payoffs
Shipra Agrawal and Navin Goyal · 2013
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Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller · 2014
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A comprehensive survey on safe reinforcement learning
Javier García and Fernando Fernández · 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 · 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, et al · 2015
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
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Deep exploration via bootstrapped DQN
Ian Osband, Charles Blundell, Alexander Pritzel, and Benjamin Van Roy · 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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Boltzmann exploration done right
Nicolò Cesa-Bianchi, Claudio Gentile, Gábor Lugosi, and Gergely Neu · 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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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 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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Practical contextual bandits with regression oracles
Dylan Foster, Alekh Agarwal, Miroslav Dudík, Haipeng Luo, and Robert Schapire · 2018
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Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Hoof, and David Meger · 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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Offline reinforcement learning with implicit Q-learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2021
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Mbrl-lib: A modular library for model-based reinforcement learning
Luis Pineda, Brandon Amos, Amy Zhang, Nathan O Lambert, and Roberto Calandra · 2021
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Stable-baselines3: Reliable reinforcement learning implementations
Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, and Noah Dormann · 2021
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Neural contextual bandits with deep representation and shallow exploration
Pan Xu, Zheng Wen, Handong Zhao, and Quanquan Gu · 2021
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Off-policy actor-critic for recommender systems
Minmin Chen, Can Xu, Vince Gatto, Devanshu Jain, Aviral Kumar, and Ed Chi · 2022
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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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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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Sim-to-real transfer of robotic control with dynamics randomization
Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Reward constrained policy optimization
Chen Tessler, Daniel J Mankowitz, and Shie Mannor · 2018
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Challenges of real-world reinforcement learning
Gabriel Dulac-Arnold, Daniel Mankowitz, and Todd Hester · 2019
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Deep exploration via randomized value functions
Ian Osband, Benjamin Van Roy, Daniel J Russo, Zheng Wen, et al · 2019
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Cleanrl: High-quality single-file implementations of deep reinforcement learning algorithms
Shengyi Huang, Rousslan Fernand Julien Dossa, Chang Ye, Jeff Braga, Dipam Chakraborty, Kinal Mehta, and João G.M. Araújo · 2022
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Recurrent networks, hidden states and beliefs in partially observable environments
Gaspard Lambrechts, Adrien Bolland, and Damien Ernst · 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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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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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 · 2022
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Torchrl: A data-driven decision-making library for pytorch
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Marllib: A scalable and efficient multi-agent reinforcement learning library
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Offline reinforcement learning for optimizing production bidding policies
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Optimizing long-term value for auction-based recommender systems via on-policy reinforcement learning
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Actor-critic alignment for offline-to-online reinforcement learning
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Malib: A parallel framework for population-based multi-agent reinforcement learning
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Cal-ql: Calibrated offline rl pre-training for efficient online fine-tuning
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