We survey the nascent literature on machine learning in the study of financial markets. We
highlight the best examples of what this line of research has to offer and recommend …

Deep Reinforcement Learning (DRL) is an essential subfield of Artificial Intelligence (AI),
where agents interact with environments to learn policies for solving complex tasks. In recent …

Stochastic optimal control and games have a wide range of applications, from finance and
economics to social sciences, robotics, and energy management. Many real-world …

Finance is a particularly challenging playground for deep reinforcement learning. However,
establishing high-quality market environments and benchmarks for financial reinforcement …

Quantitative trading (QT), which refers to the usage of mathematical models and data-driven
techniques in analyzing the financial market, has been a popular topic in both academia and …

We mitigate vortex-induced vibrations of a square cylinder at a Reynolds number of 100
using deep reinforcement learning (DRL)-based active flow control (AFC). The proposed …

Current constrained reinforcement learning (RL) methods guarantee constraint satisfaction
only in expectation, which is inadequate for safety-critical decision problems. Since a …

The financial market is a particularly challenging playground for deep reinforcement
learning due to its unique feature of dynamic datasets. Building high-quality market …

Modeling and control are primary domains in bridge wind engineering. The natural wind
field characteristics (eg, non-stationary, non-uniform, spatial-temporal changing …

Portfolio management is a fundamental problem in finance. It involves periodic reallocations
of assets to maximize the expected returns within an appropriate level of risk exposure …