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Over the last few years, sampling-based stochastic optimal control (SOC) frameworks have shown impressive performances in reinforcement learning (RL) with applications in robotics.
Exit probabilities and optimal stochastic control
W.H. Fleming · 1971
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Controlled Markov processes and viscosity solutions
W. H. Fleming and H. M. Soner · 1993
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Propagation of uncertainty in bayesian kernel models-application to multiple-step ahead forecasting
J. Quinonero Candela, A. Girard, J. Larsen, and C. E. Rasmussen · 2003
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Path integrals and symmetry breaking for optimal control theory
H. J. Kappen · 2005
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Linear theory for control of nonlinear stochastic systems
H. J. Kappen · 2005
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Gaussian processes for machine learning
C.K.I Williams and C.E. Rasmussen · 2006
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An introduction to stochastic control theory, path integrals and reinforcement learning
H. J. Kappen · 2007
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Efficient computation of optimal actions
E. Todorov · 2009
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A generalized path integral control approach to reinforcement learning
E. Theodorou, J. Buchli, and S. Schaal · 2010
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Stochastic methods
C. Gardiner · 2010
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Pilco: A model-based and data-efficient approach to policy search
M. Deisenroth and C. Rasmussen · 2011
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Iterative Path Integral Stochastic Optimal Control: Theory and Applications to Motor Control
E. Theodorou · 2011
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Relative entropy and free energy dualities: Connections to path integral and kl control
E. Theodorou and E. Todorov · 2012
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Gaussian processes for data-efficient learning in robotics and control
M. Deisenroth, D. Fox, and C. Rasmussen · 2014
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