Recurrent Neural Networks for Stochastic Control Problems with Delay
This work provides a more effective computational approach for practitioners and researchers dealing with stochastic control problems that involve time delays, which are common in finance and engineering.
This paper addresses stochastic control problems with delay, which are inherently high-dimensional due to path dependence. The authors propose using recurrent neural networks (RNNs) to parameterize control policies, demonstrating improved performance and more efficient and stable training compared to feedforward networks on three benchmark problems, including a portfolio optimization problem with infinite delay.
Stochastic control problems with delay are challenging due to the path-dependent feature of the system and thus its intrinsic high dimensions. In this paper, we propose and systematically study deep neural networks-based algorithms to solve stochastic control problems with delay features. Specifically, we employ neural networks for sequence modeling (\emph{e.g.}, recurrent neural networks such as long short-term memory) to parameterize the policy and optimize the objective function. The proposed algorithms are tested on three benchmark examples: a linear-quadratic problem, optimal consumption with fixed finite delay, and portfolio optimization with complete memory. Particularly, we notice that the architecture of recurrent neural networks naturally captures the path-dependent feature with much flexibility and yields better performance with more efficient and stable training of the network compared to feedforward networks. The superiority is even evident in the case of portfolio optimization with complete memory, which features infinite delay.