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Most reinforcement learning (RL) algorithms assume online access to the environment, in which one may readily interleave updates to the policy with experience collection using that policy.
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Off-policy temporal-difference learning with function approximation
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Tree-based batch mode reinforcement learning
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PILCO: A model-based and data-efficient approach to policy search
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Batch reinforcement learning
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Mujoco: A physics engine for model-based control
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Adam: A method for stochastic optimization
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Offline policy evaluation across representations with applications to educational games
Travis Mandel, Yun-En Liu, Sergey Levine, Emma Brunskill, and Zoran Popovic · 2014
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Risk-sensitive and robust decision-making: a cvar optimization approach
Yinlam Chow, Aviv Tamar, Shie Mannor, and Marco Pavone · 2015
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Zhaohan Guo and Emma Brunskill · 2015
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Learning continuous control policies by stochastic value gradients
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Human-level control through deep reinforcement learning
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Massively parallel methods for deep reinforcement learning
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Trust region policy optimization
John Schulman, Sergey Levine, Philipp Moritz, Michael Jordan, and Pieter Abbeel · 2015
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Continuous deep q-learning with model-based acceleration
Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, and Sergey Levine · 2016
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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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Efficient iterative policy optimization
Nicolas Le Roux · 2016
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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
Regularizing model-based planning with energy-based models
Rinu Boney, Juho Kannala, and Alexander Ilin · 2019
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Lyapunov-based safe policy optimization for continuous control
Yinlam Chow, Ofir Nachum, Aleksandra Faust, Edgar Duenez-Guzman, and Mohammad Ghavamzadeh · 2019
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SEED RL: Scalable and efficient deep-rl with accelerated central inference
Lasse Espeholt, Raphaël Marinier, Piotr Stanczyk, Ke Wang, and Marcin Michalski · 2019
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2019
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When to trust your model: Model-based policy optimization
Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine · 2019
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Distributed distributional deterministic policy gradients
Gabriel Barth-Maron, Matthew Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, and Timothy Lillicrap · 2018
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A lyapunov-based approach to safe reinforcement learning
Yinlam Chow, Ofir Nachum, Edgar Duenez-Guzman, and Mohammad Ghavamzadeh · 2018
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Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine · 2018
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Model-based reinforcement learning via meta-policy optimization
Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, and Pieter Abbeel · 2018
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IMPALA: Scalable distributed deep-rl with importance weighted actor-learner architectures
Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Vlad Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, et al · 2018
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Leave no trace: Learning to reset for safe and autonomous reinforcement learning
Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, and Sergey Levine · 2018
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Evaluating reinforcement learning algorithms in observational health settings
Omer Gottesman, Fredrik Johansson, Joshua Meier, Jack Dent, Donghun Lee, Srivatsan Srinivasan, Linying Zhang, Yi Ding, David Wihl, Xuefeng Peng, Jiayu Yao, Isaac Lage, Christopher Mosch, Li wei H. Lehman, Matthieu Komorowski, Matthieu Komorowski, Aldo Faisal, Leo Anthony Celi, David Sontag, and Finale Doshi-Velez · 2018
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Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, and Rosalind Picard · 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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Off-policy policy gradient with state distribution correction
Yao Liu, Adith Swaminathan, Alekh Agarwal, and Emma Brunskill · 2019
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Ajay Mandlekar, Fabio Ramos, Byron Boots, Li Fei-Fei, Animesh Garg, and Dieter Fox · 2019
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Multi-agent manipulation via locomotion using hierarchical sim2real
Ofir Nachum, Michael Ahn, Hugo Ponte, Shixiang Gu, and Vikash Kumar · 2019
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Generalization in deep networks: The role of distance from initialization
Vaishnavh Nagarajan and J Zico Kolter · 2019
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Benchmarking safe exploration in deep reinforcement learning
Alex Ray, Joshua Achiam, and Dario Amodei · 2019
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Benchmarking model-based reinforcement learning
Tingwu Wang, Xuchan Bao, Ignasi Clavera, Jerrick Hoang, Yeming Wen, Eric Langlois, Shunshi Zhang, Guodong Zhang, Pieter Abbeel, and Jimmy Ba · 2019
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Behavior Regularized Offline Reinforcement Learning
Yifan Wu, George Tucker, and Ofir Nachum · 2019
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MOReL : Model-based offline reinforcement learning
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, and Thorsten Joachims · 2020
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
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A game theoretic framework for model based reinforcement learning
Aravind Rajeswaran, Igor Mordatch, and Vikash Kumar · 2020
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Keep doing what worked: Behavioral modelling priors for offline reinforcement learning
Noah Y. Siegel, Jost Tobias Springenberg, Felix Berkenkamp, Abbas Abdolmaleki, Michael Neunert, Thomas Lampe, Roland Hafner, and Martin A. Riedmiller · 2020
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BRPO: Batch residual policy optimization
Sungryull Sohn, Yinlam Chow, Jayden Ooi, Ofir Nachum, Honglak Lee, Ed Chi, and Craig Boutilier · 2020
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MOPO: Model-based offline policy optimization
Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Zou, Sergey Levine, Chelsea Finn, and Tengyu Ma · 2020
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