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Distribution and sample models are two popular model choices in model-based reinforcement learning (MBRL).
Integrated modeling and control based on reinforcement learning and dynamic programming
R.S. Sutton · 1991
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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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Residual algorithms: Reinforcement learning with function approximation
Leemon Baird · 1995
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Generalization in reinforcement learning: Successful examples using sparse coarse coding
Richard S Sutton · 1996
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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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Efficient selectivity and backup operators in monte-carlo tree search
Rémi Coulom · 2006
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An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning
Ronald Parr, Lihong Li, Gavin Taylor, Christopher Painter-Wakefield, and Michael L Littman · 2008
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Fast gradient-descent methods for temporal-difference learning with linear function approximation
Richard S Sutton, Hamid Reza Maei, Doina Precup, Shalabh Bhatnagar, David Silver, Csaba Szepesvári, and Eric Wiewiora · 2009
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Convergence of least squares temporal difference methods under general conditions
Huizhen Yu · 2010
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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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Value function approximation in reinforcement learning using the fourier basis
George Konidaris, Sarah Osentoski, and Philip Thomas · 2011
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Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction
Richard S Sutton, Joseph Modayil, Michael Delp, Thomas Degris, Patrick M Pilarski, Adam White, and Doina Precup · 2011
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Dyna-style planning with linear function approximation and prioritized sweeping
Richard S Sutton, Csaba Szepesvári, Alborz Geramifard, and Michael P Bowling · 2012
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Reinforcement learning with unsupervised auxiliary tasks
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z Leibo, David Silver, and Koray Kavukcuoglu · 2016
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A deep learning approach for joint video frame and reward prediction in atari games
Felix Leibfried, Nate Kushman, and Katja Hofmann · 2016
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Learning sparse representations in reinforcement learning with sparse coding
Lei Le, Raksha Kumaraswamy, and Martha White · 2017
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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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Two-timescale networks for nonlinear value function approximation
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Multi-timescale nexting in a reinforcement learning robot
Joseph Modayil, Adam White, and Richard S Sutton · 2014
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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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Developing a predictive approach to knowledge
Adam White et al · 2015
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Wesley Chung, Somjit Nath, Ajin Joseph, and Martha White · 2018
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Sina Ghiassian, Andrew Patterson, Martha White, Richard S. Sutton, and Adam White · 2018
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Yangchen Pan, Muhammad Zaheer, Adam White, Andrew Patterson, and Martha White · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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