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As we aim to control complex systems, use of a simulator in model-based reinforcement learning is becoming more common.
Differential dynamic programming
D. H. Jacobson and D. Q. Mayne · 1970
Earlier work this paper cites.
Identification and control of dynamical systems using neural networks
K. S. Narendra and K. Parthasarathy · 1990
Earlier work this paper cites.
The h-infinity control problem: A state space approach
A. A. Stoorvogel · 1993
Earlier work this paper cites.
Locally weighted learning
C. G. Atkeson, A. W. Moorey, and S. Schaalz · 1997
Earlier work this paper cites.
Back to reality: Crossing the reality gap in evolutionary robotics
J. C. Zagal, J. Ruiz-del Solar, and P. Vallejos · 2004
Earlier work this paper cites.
A generalized iterative lqg method for locally-optimal feedback control of constrained nonlinear stochastic systems
E. Todorov and W. Li · 2005
Earlier work this paper cites.
Using inaccurate models in reinforcement learning
P. Abbeel, M. Quigley, and A. Y. Ng · 2006
Earlier work this paper cites.
Gaussian processes for machine learning
C. E. Rasmussen · 2006
Earlier work this paper cites.
Sparse gaussian processes using pseudo-inputs
E. Snelson and Z. Ghahramani · 2006
Earlier work this paper cites.
Real-time motor control using recurrent neural networks
D. Huh and E. Todorov · 2009
Cited alongside, same era.
Adaptive optimal feedback control with learned internal dynamics models
D. Mitrovic, S. Klanke, and S. Vijayakumar · 2010
Cited alongside, same era.
Sparse spectrum gaussian process regression
J. Quiñonero-Candela, C. E. Rasmussen, A. R. Figueiras-Vidal, et al · 2010
Cited alongside, same era.
Gaussian processes for machine learning (gpml) toolbox
C. E. Rasmussen and H. Nickisch · 2010
Cited alongside, same era.
Optimizing walking controllers for uncertain inputs and environments
J. M. Wang, D. J. Fleet, and A. Hertzmann · 2010
Cited alongside, same era.
Pilco: A model-based and data-efficient approach to policy search
M. Deisenroth and C. E. Rasmussen · 2011
Cited alongside, same era.
Probabilistic differential dynamic programming
Y. Pan and E. Theodorou · 2014
Later among the works it cites.
Data-driven differential dynamic programming using gaussian processes
Y. Pan and E. A. Theodorou · 2015
Later among the works it cites.
Scalable reinforcement learning via trajectory optimization and approximate gaussian process regression
Y. Pan, X. Yan, E. Theodorou, and B. Boots · 2015
Later among the works it cites.
Differential dynamic programming with temporally decomposed dynamics
A. Yamaguchi and C. G. Atkeson · 2015
Later among the works it cites.
Combining model-based policy search with online model learning for control of physical humanoids
I. Mordatch, N. Mishra, C. Eppner, and P. Abbeel · 2016
Later among the works it cites.
Simulation-based design of dynamic controllers for humanoid balancing
J. Tan, Z. Xie, B. Boots, and C. K. Liu · 2016
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Leveraging multiple simulators for crossing the reality gap
A. Boeing and T. Bräunl · 2012
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Reinforcement learning in robotics: A survey
J. Kober, J. A. Bagnell, and J. Peters · 2013
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Guided policy search
S. Levine and V. Koltun · 2013
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Iterative linear quadratic regulator design for nonlinear biological movement systems
W. Li and E. Todorov
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Extended lqr: Locally-optimal feedback control for systems with non-linear dynamics and non-quadratic cost
J. van den Berg · 2016
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Neural networks and differential dynamic programming for reinforcement learning problems
A. Yamaguchi and C. G. Atkeson · 2016
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Learning deep control policies for autonomous aerial vehicles with mpc-guided policy search
T. Zhang, G. Kahn, S. Levine, and P. Abbeel · 2016
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