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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.

Item Type:	Refereed Article
Keywords:	explainable AI, reinforcement learning, artificial intelligence, visualization, machine learning
Research Division:	Information and Computing Sciences
Research Group:	Artificial intelligence
Research Field:	Autonomous agents and multiagent systems
Objective Division:	Information and Communication Services
Objective Group:	Information systems, technologies and services
Objective Field:	Artificial intelligence
UTAS Author:	Wells, L (Dr Lindsay Wells)
ID Code:	143220
Year Published:	2021
Deposited By:	Information and Communication Technology
Deposited On:	2021-03-06
Last Modified:	2021-09-08
Downloads:	7 View Download Statistics