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

The financial markets, which involve over\$90 trillion market capitals, attract the attention of
innumerable profit-seeking investors globally. Recent explosion of reinforcement learning in …

Capacitive deionization (CDI) is an alternative desalination technology that uses
electrochemical ion separation. Although several attempts have been made to maximize the …

This paper proposes DQ-RTS, a novel decentralized Multi-Agent Reinforcement Learning
algorithm designed to address challenges posed by non-ideal communication and a varying …

A major challenge in reinforcement learning is to determine which state-action pairs are
responsible for future rewards that are delayed. Reward redistribution serves as a solution to …

Finding a more efficient trading strategy has always been one of the main concerns in
financial market trading. In order to create trading strategies that lead to higher profits …

We study reinforcement learning (RL) in episodic tabular MDPs with adversarial corruptions,
where some episodes can be adversarially corrupted. When the total number of corrupted …

Machine learning techniques are playing more and more important roles in finance market
investment. However, finance quantitative modeling with conventional supervised learning …

Abstract Recent advancements in Large Language Models (LLMs) have exhibited notable
efficacy in question-answering (QA) tasks across diverse domains. Their prowess in …

There are several automated stock trading programs using reinforcement learning, one of
which is an ensemble strategy. The main idea of the ensemble strategy is to train DRL …