The rapid changes in the finance industry due to the increasing amount of data have
revolutionized the techniques on data processing and data analysis and brought new …

Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …

Offline (or batch) reinforcement learning (RL) algorithms seek to learn an optimal policy from
a fixed dataset without active data collection. Based on the composition of the offline dataset …

We study offline reinforcement learning (RL), which aims to learn an optimal policy based on
a dataset collected a priori. Due to the lack of further interactions with the environment …

Reinforcement learning (RL) provides a theoretical framework for continuously improving an
agent's behavior via trial and error. However, efficiently learning policies from scratch can be …

Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …

Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have
led to multiple successes in solving sequential decision-making problems in various …

Despite the great empirical success of deep reinforcement learning, its theoretical
foundation is less well understood. In this work, we make the first attempt to theoretically …

Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …

While policy-based reinforcement learning (RL) achieves tremendous successes in practice,
it is significantly less understood in theory, especially compared with value-based RL. In …