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

Deep reinforcement learning (DRL) has been envisioned to have a competitive edge in
quantitative finance. However, there is a steep development curve for quantitative traders to …

The interdisciplinary relationship between machine learning and financial markets has long
been a theme of great interest among both research communities. Recently, reinforcement …

As deep reinforcement learning (DRL) has been recognized as an effective approach in
quantitative finance, getting hands-on experiences is attractive to beginners. However, to …

Deep reinforcement learning (DRL) has achieved significant results in many machine
learning (ML) benchmarks. In this short survey, we provide an overview of DRL applied to …

Deep reinforcement learning (DRL) is an emerging artificial intelligence (AI) research field
which combines deep learning (DL) for policy optimization and reinforcement learning (RL) …

An automatic program that generates constant profit from the financial market is lucrative for
every market practitioner. Recent advance in deep reinforcement learning provides a …

Foundations of Reinforcement Learning with Applications in Finance aims to demystify
Reinforcement Learning, and to make it a practically useful tool for those studying and …

Dynamic portfolio optimization is the process of sequentially allocating wealth to a collection
of assets in some consecutive trading periods, based on investors' return-risk profile …

Prediction of financial market data with deep learning models has achieved some level of
recent success. However, historical financial data suffer from an unknowable state space …