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Planning-based reinforcement learning has shown strong performance in tasks in discrete and low-dimensional continuous action spaces.
Learning latent plans from play
Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, and Pierre Sermanet · 1903
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Reinforcement learning upside down: Don’t predict rewards - just map them to actions
Jürgen Schmidhuber · 1912
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Integrated architectures for learning, planning, and reacting based on approximating dynamic programming
Richard S. Sutton · 1990
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Accelerated dp based search for statistical translation
C. Tillmann, Stephan Vogel, Hermann Ney, Arkaitz Zubiaga, and H. Sawaf · 1997
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Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
Richard S. Sutton, Doina Precup, and Satinder Singh · 1999
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Learning options in reinforcement learning
Martin Stolle and Doina Precup · 2002
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D4RL: datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2004
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Tree-based batch mode reinforcement learning
Damien Ernst, Pierre Geurts, and Louis Wehenkel · 2005
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Reinforcement learning by value gradients
Michael Fairbank · 2008
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Sample-based learning and search with permanent and transient memories
David Silver, Richard S. Sutton, and Martin Müller · 2008
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PILCO: A model-based and data-efficient approach to policy search
Marc Peter Deisenroth and Carl Edward Rasmussen · 2011
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Approximate model-assisted neural fitted q-iteration
Thomas Lampe and Martin A. Riedmiller · 2014
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Learning continuous control policies by stochastic value gradients
Nicolas Heess, Gregory Wayne, David Silver, Timothy P. Lillicrap, Tom Erez, and Yuval Tassa · 2015
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Pixel recurrent neural networks
Aäron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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The option-critic architecture
Pierre-Luc Bacon, Jean Harb, and Doina Precup · 2017
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Neural discrete representation learning
Aäron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Self-consistent trajectory autoencoder: Hierarchical reinforcement learning with trajectory embeddings
John D. Co-Reyes, Yuxuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel, and Sergey Levine · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2018
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Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy P. Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
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Neural probabilistic motor primitives for humanoid control
Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, and Nicolas Heess · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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A minimalist approach to offline reinforcement learning
Scott Fujimoto and Shixiang Gu · 2021
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Mastering atari with discrete world models
Danijar Hafner, Timothy P. Lillicrap, Mohammad Norouzi, and Jimmy Ba · 2021
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On the role of planning in model-based deep reinforcement learning
Jessica B. Hamrick, Abram L. Friesen, Feryal Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Holger Buesing, Petar Velickovic, and Theophane Weber · 2021
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Offline reinforcement learning as one big sequence modeling problem
Michael Janner, Qiyang Li, and Sergey Levine · 2021
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Learning visually guided latent actions for assistive teleoperation
Siddharth Karamcheti, Albert J. Zhai, Dylan P. Losey, and Dorsa Sadigh · 2021
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Vector quantized models for planning
Sherjil Ozair, Yazhe Li, Ali Razavi, Ioannis Antonoglou, Aäron van den Oord, and Oriol Vinyals · 2021
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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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IRIS: implicit reinforcement without interaction at scale for learning control from offline robot manipulation data
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Mastering atari, go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, et al · 2020
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Learning search space partition for black-box optimization using monte carlo tree search
Linnan Wang, Rodrigo Fonseca, and Yuandong Tian · 2020
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Exploring model-based planning with policy networks
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MOPO: model-based offline policy optimization
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Learning space partitions for path planning
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Mastering atari games with limited data
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Continuous control with action quantization from demonstrations
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Offline reinforcement learning with implicit q-learning
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Learning latent actions to control assistive robots
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