Explainable deep reinforcement learning: state of the art and challenges
GA Vouros - ACM Computing Surveys, 2022 - dl.acm.org
Interpretability, explainability, and transparency are key issues to introducing artificial
intelligence methods in many critical domains. This is important due to ethical concerns and …
intelligence methods in many critical domains. This is important due to ethical concerns and …
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 …
Interpretable machine learning: Fundamental principles and 10 grand challenges
Interpretability in machine learning (ML) is crucial for high stakes decisions and
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …
What do we want from Explainable Artificial Intelligence (XAI)?–A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research
Abstract Previous research in Explainable Artificial Intelligence (XAI) suggests that a main
aim of explainability approaches is to satisfy specific interests, goals, expectations, needs …
aim of explainability approaches is to satisfy specific interests, goals, expectations, needs …
A practical guide to multi-objective reinforcement learning and planning
Real-world sequential decision-making tasks are generally complex, requiring trade-offs
between multiple, often conflicting, objectives. Despite this, the majority of research in …
between multiple, often conflicting, objectives. Despite this, the majority of research in …
Explainability in deep reinforcement learning
A Heuillet, F Couthouis, N Díaz-Rodríguez - Knowledge-Based Systems, 2021 - Elsevier
A large set of the explainable Artificial Intelligence (XAI) literature is emerging on feature
relevance techniques to explain a deep neural network (DNN) output or explaining models …
relevance techniques to explain a deep neural network (DNN) output or explaining models …
Explainable reinforcement learning: A survey
E Puiutta, EMSP Veith - … cross-domain conference for machine learning …, 2020 - Springer
Abstract Explainable Artificial Intelligence (XAI), ie, the development of more transparent and
interpretable AI models, has gained increased traction over the last few years. This is due to …
interpretable AI models, has gained increased traction over the last few years. This is due to …
Explainable reinforcement learning through a causal lens
Prominent theories in cognitive science propose that humans understand and represent the
knowledge of the world through causal relationships. In making sense of the world, we build …
knowledge of the world through causal relationships. In making sense of the world, we build …
State2explanation: Concept-based explanations to benefit agent learning and user understanding
As more non-AI experts use complex AI systems for daily tasks, there has been an
increasing effort to develop methods that produce explanations of AI decision making that …
increasing effort to develop methods that produce explanations of AI decision making that …
Explainable Deep Reinforcement Learning for UAV autonomous path planning
Autonomous navigation in unknown environment is still a hard problem for small Unmanned
Aerial Vehicles (UAVs). Recently, some neural network-based methods are proposed to …
Aerial Vehicles (UAVs). Recently, some neural network-based methods are proposed to …