Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine
learning that has attracted considerable attention in recent years. The goal of XRL is to …

In recent years, reinforcement learning (RL) systems have shown impressive performance
and remarkable achievements. Many achievements can be attributed to combining RL with …

强化学习能够在动态复杂环境中实现自主学习, 这使其在法律, 医学, 金融等领域有着广泛应用.
但强化学习仍面临着全局状态空间不可观测, 对奖励函数强依赖和因果关系不确定等诸多问题 …

Reinforcement Learning (RL) systems can be complex and non-interpretable, making it
challenging for non-AI experts to understand or intervene in their decisions. This is due in …

A lot of infrastructure upgrade and algorithms have been developed for the information
technology driven smart grids over the past twenty years, especially with increasing interest …

This paper introduces Personalized Path Recourse, a novel method that generates recourse
paths for an agent. The objective is to achieve desired goals (eg, better outcomes compared …

Despite the increased attention to explainable AI, explainability methods for understanding
reinforcement learning (RL) agents have not been extensively studied. Failing to understand …

Abstract Machine learning is progressing at an astounding rate. The past decade has seen
an explosion in the amount of machine learning research, including deep learning …

As AI systems become widely employed this technology will profoundly impact society. To
ensure this impact is positive it is essential to align these systems with the values and …