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The objective of lifelong reinforcement learning (RL) is to optimize agents which can continuously adapt and interact in changing environments.
The general problem of the stability of motion
Aleksandr Mikhailovich Lyapunov · 1992
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Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning, 1999
Richard Sutton, Doina Precup, and Satinder Singh · 1999
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Reinforcement learning in pomdps without resets
Eyal Even-Dar, Sham M Kakade, and Yishay Mansour · 2005
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Empowerment: A universal agent-centric measure of control
Alexander S Klyubin, Daniel Polani, and Chrystopher L Nehaniv · 2005
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Empowerment–an introduction
Christoph Salge, Cornelius Glackin, and Daniel Polani · 2014
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Learning compound multi-step controllers under unknown dynamics
Weiqiao Han, Sergey Levine, and Pieter Abbeel · 2015
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Improving multi-step prediction of learned time series models
Arun Venkatraman, Martial Hebert, and J Bagnell · 2015
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Model predictive path integral control using covariance variable importance sampling
Grady Williams, Andrew Aldrich, and Evangelos Theodorou · 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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Karol Gregor, Danilo Jimenez Rezende, and Daan Wierstra · 2016
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Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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The option-critic architecture
Pierre-Luc Bacon, Jean Harb, and Doina Precup · 2017
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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 · 2017
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Uncertainty-aware reinforcement learning for collision avoidance
Gregory Kahn, Adam Villaflor, Vitchyr Pong, Pieter Abbeel, and Sergey Levine · 2017
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Unsupervised real-time control through variational empowerment
Maximilian Karl, Maximilian Soelch, Philip Becker-Ehmck, Djalel Benbouzid, Patrick van der Smagt, and Justin Bayer · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Prediction and control with temporal segment models
Nikhil Mishra, Pieter Abbeel, and Igor Mordatch · 2017
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Variational option discovery algorithms
Joshua Achiam, Harrison Edwards, Dario Amodei, and Pieter Abbeel · 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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Diversity is all you need: Learning skills without a reward function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 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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Model-ensemble trust-region policy optimization
Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, and Pieter Abbeel · 2018
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Plan online, learn offline: Efficient learning and exploration via model-based control
Kendall Lowrey, Aravind Rajeswaran, Sham Kakade, Emanuel Todorov, and Igor Mordatch · 2018
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Data-efficient hierarchical reinforcement learning
Ofir Nachum, Shixiang Gu, Honglak Lee, and Sergey Levine · 2018
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Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning
Dynamics-aware unsupervised discovery of skills
Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, and Karol Hausman · 2019
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Exploring model-based planning with policy networks
Tingwu Wang 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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An optimistic perspective on offline reinforcement learning
Rishabh Agarwal, Dale Schuurmans, and Mohammad Norouzi · 2020
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Arthur Argenson and Gabriel Dulac-Arnold · 2020
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Anusha Nagabandi, Gregory Kahn, Ronald S Fearing, and Sergey Levine · 2018
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Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P Lillicrap, and Greg Wayne · 2018
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Progress & compress: A scalable framework for continual learning, 2018
Jonathan Schwarz, Jelena Luketina, Wojciech M. Czarnecki, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
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Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto · 2018
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Unsupervised control through non-parametric discriminative rewards
David Warde-Farley, Tom Van de Wiele, Tejas Kulkarni, Catalin Ionescu, Steven Hansen, and Volodymyr Mnih · 2018
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The ingredients of real-world robotic reinforcement learning
Henry Zhu, Justin Yu, Abhishek Gupta, Dhruv Shah, Kristian Hartikainen, Avi Singh, Vikash Kumar, and Sergey Levine · 2018
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Online meta-learning
Chelsea Finn, Aravind Rajeswaran, Sham Kakade, and Sergey Levine · 2019
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The minerl competition on sample efficient reinforcement learning using human priors
William H Guss, Cayden Codel, Katja Hofmann, Brandon Houghton, Noboru Kuno, Stephanie Milani, Sharada Mohanty, Diego Perez Liebana, Ruslan Salakhutdinov, Nicholay Topin, et al · 2019
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Explore, discover and learn: Unsupervised discovery of state-covering skills
Víctor Campos, Alexander Trott, Caiming Xiong, Richard Socher, Xavier Giro-i Nieto, and Jordi Torres · 2020
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Ecological reinforcement learning
John D Co-Reyes, Suvansh Sanjeev, Glen Berseth, Abhishek Gupta, and Sergey Levine · 2020
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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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Options of interest: Temporal abstraction with interest functions
Khimya Khetarpal, Martin Klissarov, Maxime Chevalier-Boisvert, Pierre-Luc Bacon, and Doina Precup · 2020
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Morel: Model-based offline reinforcement learning
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, and Thorsten Joachims · 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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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
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Deep dynamics models for learning dexterous manipulation
Anusha Nagabandi, Kurt Konolige, Sergey Levine, and Vikash Kumar · 2020
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Emergent real-world robotic skills via unsupervised off-policy reinforcement learning
Archit Sharma, Michael Ahn, Sergey Levine, Vikash Kumar, Karol Hausman, and Shixiang Gu · 2020
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Learning off-policy with online planning
Harshit Sikchi, Wenxuan Zhou, and David Held · 2020
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Deep reinforcement learning amidst lifelong non-stationarity
Annie Xie, James Harrison, and Chelsea Finn · 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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Cautious adaptation for reinforcement learning in safety-critical settings
Jesse Zhang, Brian Cheung, Chelsea Finn, Sergey Levine, and Dinesh Jayaraman · 2020
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Efficient empowerment estimation for unsupervised stabilization
Ruihan Zhao, Kevin Lu, Pieter Abbeel, and Stas Tiomkin · 2021
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