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Related Experiment Video

Updated: Nov 2, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Explainable AI and Reinforcement Learning-A Systematic Review of Current Approaches and Trends.

Lindsay Wells1, Tomasz Bednarz1,2

  • 1Expanded Perception and Interaction Center, Faculty of Art and Design, University of New South Wales, Sydney, NSW, Australia.

Frontiers in Artificial Intelligence
|June 7, 2021
PubMed
Summary

Explainable Artificial Intelligence (XAI) research is expanding into Reinforcement Learning (RL) to enhance AI transparency. Current XAI for RL methods face limitations like few user studies and toy examples, indicating a need for more practical applications.

Keywords:
artificial intelligenceexplainable AImachine learningreinforcement learningvisualization

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Area of Science:

  • Computer Science
  • Artificial Intelligence

Background:

  • Growing demand for transparency and trust in Artificial Intelligence (AI) systems.
  • Increasing AI applications in sensitive domains with societal, ethical, and safety implications.
  • Existing Explainable Artificial Intelligence (XAI) research predominantly focuses on Machine Learning (ML) for classification, decision, or action.

Approach:

  • This review systematically explores current approaches and limitations of XAI specifically within the domain of Reinforcement Learning (RL).
  • Analyzed 25 studies from 520 search results, including 5 identified through snowball sampling.
  • Identified key trends and challenges in XAI for RL.

Key Points:

  • Identified trends in XAI for RL include visualization, query-based explanations, policy summarization, human-in-the-loop collaboration, and verification.
  • Highlighted significant limitations such as a lack of user studies.
  • Noted the prevalence of simplified 'toy-examples' and difficulties in generating understandable explanations.

Conclusions:

  • The current state of XAI for RL requires further development to address identified limitations.
  • Future research should focus on areas like immersive visualization and symbolic representation for more effective XAI in RL.
  • Enhancing user studies and moving beyond toy examples are crucial for advancing practical XAI in RL.