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Model-based strategies for control are critical to obtain sample efficient learning.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
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Integrated modeling and control based on reinforcement learning and dynamic programming
R.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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Prioritized sweeping: Reinforcement learning with less data and less time
Andrew W Moore and Christopher G Atkeson · 1993
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Efficient Learning and Planning Within the Dyna Framework
Jing Peng and Ronald J Williams · 1993
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Reinforcement Learning: An Introduction
R.S. Sutton and A G Barto · 1998
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Catastrophic forgetting in connectionist networks
R M French · 1999
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Autonomous helicopter control using reinforcement learning policy search methods
J A Bagnell and J G Schneider · 2001
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Kernel-Based Reinforcement Learning
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Fast Nonparametric Conditional Density Estimation
Michael P Holmes, Alexander G Gray, and Charles L Isbell · 2007
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An analysis of model-based Interval Estimation for Markov Decision Processes
A. Strehl and M Littman · 2008
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Dyna-style planning with linear function approximation and prioritized sweeping
R Sutton, C Szepesvári, A Geramifard, and M Bowling · 2008
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Reinforcement Learning using Kernel-Based Stochastic Factorization
A Barreto, D Precup, and J Pineau · 2011
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PILCO: A model-based and data-efficient approach to policy search
M Deisenroth and C E Rasmussen · 2011
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Modelling transition dynamics in MDPs with RKHS embeddings
Steffen Grunewalder, Guy Lever, Luca Baldassarre, Massi Pontil, and Arthur Gretton · 2012
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Kernel-Based Reinforcement Learning on Representative States
B Kveton and G Theocharous · 2012
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
I J Goodfellow, M Mirza, D Xiao, A Courville, and Y Bengio · 2013
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Policy Iteration Based on Stochastic Factorization
Learning of Non-Parametric Control Policies with High-Dimensional State Features
H Van Hoof, J. Peters, and G Neumann · 2015
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A deeper look at planning as learning from replay
H van Seijen and R.S. Sutton · 2015
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Guillaume Alain, Yoshua Bengio, Li Yao, Jason Yosinski, Éric Thibodeau-Laufer, Saizheng Zhang, and Pascal Vincent · 2016
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Incremental Stochastic Factorization for Online Reinforcement Learning
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Continuous Deep Q-Learning with Model-based Acceleration
Shixiang Gu, Timothy P Lillicrap, Ilya Sutskever, and Sergey Levine · 2016
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Compressed Conditional Mean Embeddings for Model-Based Reinforcement Learning
Guy Lever, John Shawe-Taylor, Ronnie Stafford, and Csaba Szepesvári · 2016
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A Barreto, J Pineau, and D Precup · 2014
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Generative Adversarial Nets
I J Goodfellow, J Pouget-Abadie, MehMdi Mirza, B Xu, D Warde-Farley, S Ozair, A C Courville, and Y Bengio · 2014
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Efficient learning and planning with compressed predictive states
W L Hamilton, M M Fard, and J Pineau · 2014
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Julien Mairal, Piotr Koniusz, Zaid Harchaoui, and Cordelia Schmid · 2014
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Hengshuai Yao, Csaba Szepesvári, Bernardo Avila Pires, and Xinhua Zhang · 2014
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Learning Structured Output Representation using Deep Conditional Generative Models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015
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Policy Error Bounds for Model-Based Reinforcement Learning with Factored Linear Models
Bernardo Avila Pires and Csaba Szepesvári · 2016
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Prioritized Experience Replay
Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2016
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Adapting Kernel Representations Online Using Submodular Maximization
Matthew Schlegel, Yangchen Pan, Jiecao Chen, and Martha White · 2017
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Self-Correcting Models for Model-Based Reinforcement Learning
Erik Talvitie · 2017
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Unifying task specification in reinforcement learning
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