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Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment.
Model predictive control: theory and practice—a survey
Carlos E Garcia, David M Prett, and Manfred Morari · 1989
Earlier work this paper cites.
Adaptive mixtures of local experts
Robert A Jacobs, Michael I Jordan, Steven J Nowlan, Geoffrey E Hinton, et al · 1991
Earlier work this paper cites.
Using options for knowledge transfer in reinforcement learning
Theodore J Perkins, Doina Precup, et al · 1999
Earlier work this paper cites.
Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
Richard S Sutton, Doina Precup, and Satinder Singh · 1999
Earlier work this paper cites.
Multivariate information bottleneck
Nir Friedman, Ori Mosenzon, Noam Slonim, and Naftali Tishby · 2001
Earlier work this paper cites.
Learning options in reinforcement learning
Martin Stolle and Doina Precup · 2002
Earlier work this paper cites.
Information projections revisited
Imre Csiszár and Frantisek Matus · 2003
Earlier work this paper cites.
Gaussian processes in machine learning
Carl Edward Rasmussen · 2003
Earlier work this paper cites.
The im algorithm: a variational approach to information maximization
David Barber Felix Agakov · 2004
Earlier work this paper cites.
Gaussian process model based predictive control
Juš Kocijan, Roderick Murray-Smith, Carl Edward Rasmussen, and Agathe Girard · 2004
Earlier work this paper cites.
Iterative linear quadratic regulator design for nonlinear biological movement systems
Weiwei Li and Emanuel Todorov · 2004
Earlier work this paper cites.
Empowerment: A universal agent-centric measure of control
Alexander S Klyubin, Daniel Polani, and Chrystopher L Nehaniv · 2005
Earlier work this paper cites.
Estimating mutual information and multi–information in large networks
Noam Slonim, Gurinder S Atwal, Gasper Tkacik, and William Bialek · 2005
Earlier work this paper cites.
Gaussian processes and reinforcement learning for identification and control of an autonomous blimp
Jonathan Ko, Daniel J Klein, Dieter Fox, and Dirk Haehnel · 2007
Earlier work this paper cites.
Intrinsic motivation systems for autonomous mental development
Pierre-Yves Oudeyer, Frdric Kaplan, and Verena V Hafner · 2007
Earlier work this paper cites.
What is intrinsic motivation? a typology of computational approaches
Pierre-Yves Oudeyer and Frederic Kaplan · 2009
Earlier work this paper cites.
Formal theory of creativity, fun, and intrinsic motivation (1990–2010)
Jürgen Schmidhuber · 2010
Earlier work this paper cites.
Pilco: A model-based and data-efficient approach to policy search
Marc Deisenroth and Carl E Rasmussen · 2011
Earlier work this paper cites.
Hierarchical relative entropy policy search
Christian Daniel, Gerhard Neumann, and Jan Peters · 2012
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
Earlier work this paper cites.
Gaussian processes for data-efficient learning in robotics and control
Marc Peter Deisenroth, Dieter Fox, and Carl Edward Rasmussen · 2013
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
Cited alongside, same era.
Deepmpc: Learning deep latent features for model predictive control
Ian Lenz, Ross A Knepper, and Ashutosh Saxena · 2015
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Variational information maximisation for intrinsically motivated reinforcement learning
Shakir Mohamed and Danilo Jimenez Rezende · 2015
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Action-conditional video prediction using deep networks in atari games
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L Lewis, and Satinder Singh · 2015
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael I Jordan, and Philipp Moritz · 2015
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap, and Sergey Levine · 2017
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Emergence of locomotion behaviours in rich environments
Nicolas Heess, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, SM Eslami, Martin Riedmiller, et al · 2017
Later among the works it cites.
Data-efficient reinforcement learning with probabilistic model predictive control
Sanket Kamthe and Marc Peter Deisenroth · 2017
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Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell · 2017
Later among the works it cites.
Deeploco: Dynamic locomotion skills using hierarchical deep reinforcement learning
Xue Bin Peng, Glen Berseth, KangKang Yin, and Michiel Van De Panne · 2017
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Incentivizing exploration in reinforcement learning with deep predictive models
Bradly C. Stadie, Sergey Levine, and Pieter Abbeel · 2015
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Embed to control: A locally linear latent dynamics model for control from raw images
Manuel Watter, Jost Springenberg, Joschka Boedecker, and Martin Riedmiller · 2015
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Unifying count-based exploration and intrinsic motivation
Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Remi Munos · 2016
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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One-shot learning of manipulation skills with online dynamics adaptation and neural network priors
Justin Fu, Sergey Levine, and Pieter Abbeel · 2016
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Improving pilco with bayesian neural network dynamics models
Yarin Gal, Rowan McAllister, and Carl Edward Rasmussen · 2016
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Karol Gregor, Danilo Jimenez Rezende, and Daan Wierstra · 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
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# exploration: A study of count-based exploration for deep reinforcement learning
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, OpenAI Xi Chen, Yan Duan, John Schulman, Filip DeTurck, and Pieter Abbeel · 2017
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Feudal networks for hierarchical reinforcement learning
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, and Koray Kavukcuoglu · 2017
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Variational option discovery algorithms
Joshua Achiam, Harrison Edwards, Dario Amodei, and Pieter Abbeel · 2018
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Therml: Thermodynamics of machine learning
Alexander A Alemi and Ian Fischer · 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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Recurrent world models facilitate policy evolution
David Ha and Jürgen Schmidhuber · 2018
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Learning an embedding space for transferable robot skills
Karol Hausman, Jost Tobias Springenberg, Ziyu Wang, Nicolas Heess, and Martin Riedmiller · 2018
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Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, et al · 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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Data-efficient hierarchical reinforcement learning
Ofir Nachum, Shixiang Shane 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
Anusha Nagabandi, Gregory Kahn, Ronald S Fearing, and Sergey Levine · 2018
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TF-Agents: A library for reinforcement learning in tensorflow
Sergio Guadarrama, Anoop Korattikara, Oscar Ramirez, Pablo Castro, Ethan Holly, Sam Fishman, Ke Wang, Ekaterina Gonina, Chris Harris, Vincent Vanhoucke, Eugene Brevdo · 2018
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Disentangling the independently controllable factors of variation by interacting with the world, 2018
Valentin Thomas, Emmanuel Bengio, William Fedus, Jules Pondard, Philippe Beaudoin, Hugo Larochelle, Joelle Pineau, Doina Precup, and Yoshua Bengio · 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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Aggressive driving with model predictive path integral control
Grady Williams, Paul Drews, Brian Goldfain, James M Rehg, and Evangelos A Theodorou · 2018
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