Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …

Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …

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

Offline reinforcement learning (RL) aims at learning effective policies from historical data
without extra environment interactions. During our experience of applying offline RL, we …

An increasing number of studies have shown the effectiveness of using deep reinforcement
learning to learn profitable trading strategies from financial market data. However, a single …

Existing surveys on stock market prediction often focus on traditional machine learning
methods instead of deep learning methods. This motivates us to provide a structured and …

The financial market, as a complex nonlinear dynamic system frequently influenced by
various factors, such as international investment capital, is very challenging to build trading …

Due to the broad range of applications of reinforcement learning (RL), understanding the
effects of adversarial attacks against RL model is essential for the safe applications of this …

In this work, we argue for the importance of an online evaluation budget for a reliable
comparison of deep offline RL algorithms. First, we delineate that the online evaluation …

Portfolio optimization is an active management strategy that aims to maximize returns and
control risk within reasonable limits. The Proximal Policy Optimization (PPO), a robust on …