A Stochastic Gradient Descent Approach for Stochastic Optimal Control
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Florida State Univ., Tallahassee, FL (United States)
- Univ. of Central Florida, Orlando, FL (United States)
In this work, we introduce a stochastic gradient descent approach to solve the stochastic optimal control problem through stochastic maximum principle. The motivation that drives our method is the gradient of the cost functional in the stochastic optimal control problem is under expectation, and numerical calculation of such an expectation requires fully computation of a system of forward backward stochastic differential equations, which is computationally expensive. By evaluating the expectation with single-sample representation as suggested by the stochastic gradient descent type optimisation, we could save computational efforts in solving FBSDEs and only focus on the optimisation task which aims to determine the optimal control process.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC); National Science Foundation (NSF)
- Grant/Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1814258
- Journal Information:
- East Asian Journal on Applied Mathematics, Journal Name: East Asian Journal on Applied Mathematics Journal Issue: 4 Vol. 10; ISSN 2079-7362
- Publisher:
- Global Science Press (GSP)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
Similar Records
Stochastic gradient descent algorithm for stochastic optimization in solving analytic continuation problems
Linear Forward-Backward Stochastic Differential Equations