Feynman-Kac Neural Network Architectures for Stochastic Control Using Second-Order FBSDE Theory

Marcus Pereira, Ziyi Wang, Tianrong Chen, Emily Reed, Evangelos Theodorou
; Proceedings of the 2nd Conference on Learning for Dynamics and Control, PMLR 120:728-738, 2020.

Abstract

We present a deep recurrent neural network architecture to solve a class of stochastic optimal control problems described by fully nonlinear Hamilton Jacobi Bellman partial differential equations. Such PDEs arise when considering stochastic dynamics characterized by uncertainties that are additive, state dependent, and control multiplicative. Stochastic models with these characteristics are important in computational neuroscience, biology, finance, and aerospace systems and provide a more accurate representation of actuation than models with only additive uncertainty. Previous literature has established the inadequacy of the linear HJB theory for such problems, so instead, methods relying on the generalized version of the Feynman-Kac lemma have been proposed resulting in a system of second-order Forward-Backward SDEs. However, so far, these methods suffer from compounding errors resulting in lack of scalability. In this paper, we propose a deep learning based algorithm that leverages the second-order FBSDE representation and LSTM-based recurrent neural networks to not only solve such stochastic optimal control problems but also overcome the problems faced by traditional approaches, including scalability. The resulting control algorithm is tested on a high-dimensional linear system and three nonlinear systems from robotics and biomechanics in simulation to demonstrate feasibility and out-performance against previous methods.

Cite this Paper


BibTeX
@InProceedings{pmlr-v120-pereira20a, title = {Feynman-Kac Neural Network Architectures for Stochastic Control Using Second-Order FBSDE Theory}, author = {Pereira, Marcus and Wang, Ziyi and Chen, Tianrong and Reed, Emily and Theodorou, Evangelos}, booktitle = {Proceedings of the 2nd Conference on Learning for Dynamics and Control}, pages = {728--738}, year = {2020}, editor = {Alexandre M. Bayen and Ali Jadbabaie and George Pappas and Pablo A. Parrilo and Benjamin Recht and Claire Tomlin and Melanie Zeilinger}, volume = {120}, series = {Proceedings of Machine Learning Research}, address = {The Cloud}, month = {10--11 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v120/pereira20a/pereira20a.pdf}, url = {http://proceedings.mlr.press/v120/pereira20a.html}, abstract = {We present a deep recurrent neural network architecture to solve a class of stochastic optimal control problems described by fully nonlinear Hamilton Jacobi Bellman partial differential equations. Such PDEs arise when considering stochastic dynamics characterized by uncertainties that are additive, state dependent, and control multiplicative. Stochastic models with these characteristics are important in computational neuroscience, biology, finance, and aerospace systems and provide a more accurate representation of actuation than models with only additive uncertainty. Previous literature has established the inadequacy of the linear HJB theory for such problems, so instead, methods relying on the generalized version of the Feynman-Kac lemma have been proposed resulting in a system of second-order Forward-Backward SDEs. However, so far, these methods suffer from compounding errors resulting in lack of scalability. In this paper, we propose a deep learning based algorithm that leverages the second-order FBSDE representation and LSTM-based recurrent neural networks to not only solve such stochastic optimal control problems but also overcome the problems faced by traditional approaches, including scalability. The resulting control algorithm is tested on a high-dimensional linear system and three nonlinear systems from robotics and biomechanics in simulation to demonstrate feasibility and out-performance against previous methods.} }
Endnote
%0 Conference Paper %T Feynman-Kac Neural Network Architectures for Stochastic Control Using Second-Order FBSDE Theory %A Marcus Pereira %A Ziyi Wang %A Tianrong Chen %A Emily Reed %A Evangelos Theodorou %B Proceedings of the 2nd Conference on Learning for Dynamics and Control %C Proceedings of Machine Learning Research %D 2020 %E Alexandre M. Bayen %E Ali Jadbabaie %E George Pappas %E Pablo A. Parrilo %E Benjamin Recht %E Claire Tomlin %E Melanie Zeilinger %F pmlr-v120-pereira20a %I PMLR %J Proceedings of Machine Learning Research %P 728--738 %U http://proceedings.mlr.press %V 120 %W PMLR %X We present a deep recurrent neural network architecture to solve a class of stochastic optimal control problems described by fully nonlinear Hamilton Jacobi Bellman partial differential equations. Such PDEs arise when considering stochastic dynamics characterized by uncertainties that are additive, state dependent, and control multiplicative. Stochastic models with these characteristics are important in computational neuroscience, biology, finance, and aerospace systems and provide a more accurate representation of actuation than models with only additive uncertainty. Previous literature has established the inadequacy of the linear HJB theory for such problems, so instead, methods relying on the generalized version of the Feynman-Kac lemma have been proposed resulting in a system of second-order Forward-Backward SDEs. However, so far, these methods suffer from compounding errors resulting in lack of scalability. In this paper, we propose a deep learning based algorithm that leverages the second-order FBSDE representation and LSTM-based recurrent neural networks to not only solve such stochastic optimal control problems but also overcome the problems faced by traditional approaches, including scalability. The resulting control algorithm is tested on a high-dimensional linear system and three nonlinear systems from robotics and biomechanics in simulation to demonstrate feasibility and out-performance against previous methods.
APA
Pereira, M., Wang, Z., Chen, T., Reed, E. & Theodorou, E.. (2020). Feynman-Kac Neural Network Architectures for Stochastic Control Using Second-Order FBSDE Theory. Proceedings of the 2nd Conference on Learning for Dynamics and Control, in PMLR 120:728-738

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