Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

The role of data in building AI systems has recently been significantly magnified by the
emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model …

Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

Experience replay is central to off-policy algorithms in deep reinforcement learning (RL), but
there remain significant gaps in our understanding. We therefore present a systematic and …

Deep reinforcement learning (DRL) augments the reinforcement learning framework, which
learns a sequence of actions that maximizes the expected reward, with the representative …

Games are abstractions of the real world, where artificial agents learn to compete and
cooperate with other agents. While significant achievements have been made in various …

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 …

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 …

Experience replay is crucial for off-policy reinforcement learning (RL) methods. By
remembering and reusing the experiences from past different policies, experience replay …