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 …

Advanced communication protocols are critical for the coexistence of autonomous robots
and humans. Thus, the development of explanatory capabilities in robots is an urgent first …

Deep neural networks excel at finding hierarchical representations that solve complex tasks
over large datasets. How can we humans understand these learned representations? In this …

We analyze the knowledge acquired by AlphaZero, a neural network engine that learns
chess solely by playing against itself yet becomes capable of outperforming human chess …

DARPA formulated the Explainable Artificial Intelligence (XAI) program in 2015 with the goal
to enable end users to better understand, trust, and effectively manage artificially intelligent …

The ability of graph neural networks (GNNs) for distinguishing nodes in graphs has been
recently characterized in terms of the Weisfeiler-Lehman (WL) test for checking graph …

In this paper, we provide a comprehensive outline of the different threads of work in
Explainable AI Planning (XAIP) that has emerged as a focus area in the last couple of years …

Although deep reinforcement learning has become a promising machine learning approach
for sequential decision-making problems, it is still not mature enough for high-stake domains …

With the rapid development of deep reinforcement learning (DRL) techniques, there is an
increasing need to understand and interpret DRL policies. While recent research has …

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 …