Explainable AI and Reinforcement Learning-A Systematic Review of Current Approaches and Trends

Lindsay Wells; Tomasz Bednarz

doi:10.3389/frai.2021.550030

Explainable AI and Reinforcement Learning-A Systematic Review of Current Approaches and Trends

Front Artif Intell. 2021 May 20:4:550030. doi: 10.3389/frai.2021.550030. eCollection 2021.

Authors

Lindsay Wells¹, Tomasz Bednarz^{1

2}

Affiliations

¹ Expanded Perception and Interaction Center, Faculty of Art and Design, University of New South Wales, Sydney, NSW, Australia.
² Data61, Commonwealth Scientific and Industrial Research Organisation, Sydney, NSW, Australia.

Abstract

Research into Explainable Artificial Intelligence (XAI) has been increasing in recent years as a response to the need for increased transparency and trust in AI. This is particularly important as AI is used in sensitive domains with societal, ethical, and safety implications. Work in XAI has primarily focused on Machine Learning (ML) for classification, decision, or action, with detailed systematic reviews already undertaken. This review looks to explore current approaches and limitations for XAI in the area of Reinforcement Learning (RL). From 520 search results, 25 studies (including 5 snowball sampled) are reviewed, highlighting visualization, query-based explanations, policy summarization, human-in-the-loop collaboration, and verification as trends in this area. Limitations in the studies are presented, particularly a lack of user studies, and the prevalence of toy-examples and difficulties providing understandable explanations. Areas for future study are identified, including immersive visualization, and symbolic representation.

Keywords: artificial intelligence; explainable AI; machine learning; reinforcement learning; visualization.

Publication types

Systematic Review