Explainable reinforcement learning: A survey and comparative review
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 …
learning that has attracted considerable attention in recent years. The goal of XRL is to …
Explainable autonomous robots: a survey and perspective
T Sakai, T Nagai - Advanced Robotics, 2022 - Taylor & Francis
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 …
and humans. Thus, the development of explanatory capabilities in robots is an urgent first …
Understanding the role of individual units in a deep neural network
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 …
over large datasets. How can we humans understand these learned representations? In this …
Acquisition of chess knowledge in alphazero
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 …
chess solely by playing against itself yet becomes capable of outperforming human chess …
DARPA's explainable AI (XAI) program: A retrospective
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 …
to enable end users to better understand, trust, and effectively manage artificially intelligent …
The logical expressiveness of graph neural networks
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 …
recently characterized in terms of the Weisfeiler-Lehman (WL) test for checking graph …
The emerging landscape of explainable ai planning and decision making
T Chakraborti, S Sreedharan… - arXiv preprint arXiv …, 2020 - arxiv.org
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 …
Explainable AI Planning (XAIP) that has emerged as a focus area in the last couple of years …
A survey on interpretable reinforcement learning
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 …
for sequential decision-making problems, it is still not mature enough for high-stake domains …
Edge: Explaining deep reinforcement learning policies
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 …
increasing need to understand and interpret DRL policies. While recent research has …
A survey of explainable reinforcement learning
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 …
learning that has attracted considerable attention in recent years. The goal of XRL is to …