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Current model-based reinforcement learning approaches use the model simply as a learned black-box simulator to augment the data for policy optimization or value function learning.
Task-agnostic dynamics priors for deep reinforcement learning
Yilun Du and Karthik Narasimhan · 1905
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Differentiable algorithm networks for composable robot learning
Péter Karkus, Xiao Ma, David Hsu, Leslie Pack Kaelbling, Wee Sun Lee, and Tomás Lozano-Pérez · 1905
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Exploring model-based planning with policy networks
Tingwu Wang and Jimmy Ba · 1906
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Neuronlike adaptive elements that can solve difficult learning control problems
A. G. Barto, R. S. Sutton, and C. W. Anderson · 1983
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Planning by incremental dynamic programming
Richard S Sutton · 1991
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Self-improving reactive agents based on reinforcement learning, planning and teaching
Long-Ji Lin · 1992
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Introduction to Reinforcement Learning
Richard S. Sutton and Andrew G. Barto · 1998
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Probability and random processes , volume 80
G.R. Grimmett and D.R. Stirzaker · 2001
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Approximately optimal approximate reinforcement learning
Sham Kakade and John Langford · 2002
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Policy gradient methods for robotics
J. Peters and S. Schaal · 2006
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Pilco: A model-based and data-efficient approach to policy search
Marc Deisenroth and Carl E Rasmussen · 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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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Learning neural network policies with guided policy search under unknown dynamics
Sergey Levine and Pieter Abbeel · 2014
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Learning continuous control policies by stochastic value gradients
Nicolas Heess, Gregory Wayne, David Silver, Timothy Lillicrap, Tom Erez, and Yuval Tassa · 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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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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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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Model-based value estimation for efficient model-free reinforcement learning
Vladimir Feinberg, Alvin Wan, Ion Stoica, Michael I Jordan, Joseph E Gonzalez, and Sergey Levine · 2018
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Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke van Hoof, and David Meger · 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
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Aviv Tamar, Sergey Levine, and Pieter Abbeel · 2016
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Uncertainty-driven imagination for continuous deep reinforcement learning
Gabriel Kalweit and Joschka Boedecker · 2017
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Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning
Anusha Nagabandi, Gregory Kahn, Ronald S Fearing, and Sergey Levine · 2017
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Path integral networks: End-to-end differentiable optimal control, 2017
Masashi Okada, Luca Rigazio, and Takenobu Aoshima · 2017
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Differentiable mpc for end-to-end planning and control, 2018
Brandon Amos, Ivan Dario Jimenez Rodriguez, Jacob Sacks, Byron Boots, and J. Zico Kolter · 2018
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Sample-efficient reinforcement learning with stochastic ensemble value expansion
Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, and Honglak Lee · 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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Kendall Lowrey, Aravind Rajeswaran, Sham M. Kakade, Emanuel Todorov, and Igor Mordatch · 2018
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Mpc-inspired neural network policies for sequential decision making
Marcus Pereira, David D. Fan, Gabriel Nakajima An, and Evangelos A. Theodorou · 2018
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Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2018
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Model-based lookahead reinforcement learning
Zhang-Wei Hong, Joni Pajarinen, and Jan Peters · 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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Algorithmic framework for model-based deep reinforcement learning with theoretical guarantees
Yuping Luo, Huazhe Xu, Yuanzhi Li, Yuandong Tian, Trevor Darrell, and Tengyu Ma · 2019
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Monte carlo gradient estimation in machine learning, 2019
Shakir Mohamed, Mihaela Rosca, Michael Figurnov, and Andriy Mnih · 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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