Fetching the paper…
Reading the bibliography…
A modified Deep BSDE (backward differential equation) learning method with measurability loss, called Deep BSDE-ML method, is introduced in this paper to solve a kind of linear decoupled forward-backward stochastic differential equations (FBSDEs), which is encountered in the policy evaluation of learning the optimal feedback policies of a class of stochastic control problems.
Stochastic controls: Hamiltonian systems and HJB equations
Jiongmin Yong and Xun Yu Zhou · 1999
Earlier work this paper cites.
Adaptive dynamic programming
J.J. Murray, C.J. Cox, G.G. Lendaris, and R. Saeks · 2002
Earlier work this paper cites.
Brownian motion and stochastic calculus
Ioannis Karatzas and Steven Shreve · 2012
Earlier work this paper cites.
Policy Iteration Algorithm for Singular Controlled Diffusion Processes
Yuan-Hua Ni and Hai-Tao Fang · 2013
Earlier work this paper cites.
An Iterative Method for Nonlinear Stochastic Optimal Control Based on Path Integrals
Satoshi Satoh, Hilbert J. Kappen, and Masami Saeki · 2017
Earlier work this paper cites.
Solving high-dimensional partial differential equations using deep learning
Jiequn Han, Arnulf Jentzen, and Weinan E · 2018
Earlier work this paper cites.
Dgm: A deep learning algorithm for solving partial differential equations
Justin Sirignano and Konstantinos Spiliopoulos · 2018
Earlier work this paper cites.
Reinforcement Learning, second edition: An Introduction
Richard S. Sutton and Andrew G. Barto · 2018
Cited alongside, same era.
Reinforcement Learning and Optimal Control
Dimitri Bertsekas · 2019
Cited alongside, same era.
Learning deep stochastic optimal control policies using forward-backward sdes
Marcus A Pereira and Ziyi Wang · 2019
Cited alongside, same era.
Algorithms for solving high dimensional pdes: From nonlinear monte carlo to machine learning
Weinan E, Jiequn Han, and Arnulf Jentzen · 2020
Cited alongside, same era.
Reinforcement Learning in Continuous Time and Space: A Stochastic Control Approach
Haoran Wang, Thaleia Zariphopoulou, and Xun Yu Zhou · 2020
Cited alongside, same era.
Continuous-time mean–variance portfolio selection: A reinforcement learning framework
Backward Deep BSDE Methods and Applications to Nonlinear Problems
Yajie Yu, Bernhard Hientzsch, and Narayan Ganesan · 2020
Later among the works it cites.
Policy evaluation and temporal-difference learning in continuous time and space: A martingale approach
Yanwei Jia and Xun Yu Zhou · 2021
Later among the works it cites.
Policy iterations for reinforcement learning problems in continuous time and space—fundamental theory and methods
Jaeyoung Lee and Richard S Sutton · 2021
Later among the works it cites.
Adaptive deep learning for high-dimensional hamilton–jacobi–bellman equations
Tenavi Nakamura-Zimmerer, Qi Gong, and Wei Kang · 2021
Later among the works it cites.
A neural network approach for high-dimensional optimal control
Derek Onken, Levon Nurbekyan, Xingjian Li, Samy Wu Fung, Stanley Osher, and Lars Ruthotto · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Haoran Wang and Xun Yu Zhou · 2020
Cited alongside, same era.
Later among the works it cites.
Adaptive Dynamic Programming in the Hamiltonian-Driven Framework
Yongliang Yang, Donald C. Wunsch II, and Yixin Yin · 2021
Later among the works it cites.