A stochastic optimal control problem deals with uncertainties when making decisions to maximize or minimize an objective function. With a given objective function, decision makers need to determine a strategy, which is the stochastic control, to optimize the objective function in a random environment. The decision-making problem is the so-called stochastic control problem. One powerful tool to study the stochastic control problems is the dynamic programming principle and the associated Hamilton-Jacobi-Bellman (HJB) equation. Optimal controls are obtained by solving the HJB equation.

Since the development of dynamic programming techniques, there has been significant research in stochastic control theory and its application to a wide range of areas, from engineering to economics. Although many problems have been investigated under various stochastic models, there are still a lot of emerging and challenging problems such as constraints on control variables and the complexity of models that, in order to investigate, need the development of new theoretical and numerical methodologies. In particular, the recent fast development of machine learning methods provides a new way to study stochastic control problems.

This Special Issue aims to collect high-quality research papers on theoretical and numerical methods for solving stochastic optimal control problems. The research results will advance our knowledge in decision-making processes. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Dynamic programming and its applications in engineering
• Stochastic maximum principle and its applications in engineering
• Complex stochastic systems in engineering
• Stability analysis of stochastic control
• Machine learning methods for solving control problems
• Data-driven control strategies
• Computational methods in stochastic systems
• Stochastic games
• Optimal insurance strategy and valuation of life insurance and annuity contracts