survey

Explainability in Deep Reinforcement Learning: A Review into Current Methods and Applications

Authors:

Thomas Hickling,

Abdelhafid Zenati,

Nabil Aouf, and

Phillippa SpencerAuthors Info & Claims

ACM Computing Surveys, Volume 56, Issue 5

Article No.: 125, Pages 1 - 35

https://doi.org/10.1145/3623377

Published: 08 December 2023 Publication History

Abstract

The use of Deep Reinforcement Learning (DRL) schemes has increased dramatically since their first introduction in 2015. Though uses in many different applications are being found, they still have a problem with the lack of interpretability. This has bread a lack of understanding and trust in the use of DRL solutions from researchers and the general public. To solve this problem, the field of Explainable Artificial Intelligence has emerged. This entails a variety of different methods that look to open the DRL black boxes, ranging from the use of interpretable symbolic Decision Trees to numerical methods like Shapley Values. This review looks at which methods are being used and for which applications. This is done to identify which models are the best suited to each application or if a method is being underutilised.

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Cited By

Núñez-Molina CMesejo PFernández-Olivares J(2024)A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision MakingACM Computing Surveys10.1145/366336656:11(1-36)Online publication date: 28-Jun-2024
https://dl.acm.org/doi/10.1145/3663366
Ozalp RUcar AGuzelis C(2024)Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Toward Trustworthy, Interpretable, and Explainable Artificial IntelligenceIEEE Access10.1109/ACCESS.2024.338542612(51840-51858)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3385426
Li XZhang XLi JLuo FHuang YZhang X(2024)Blocklength Allocation and Power Control in UAV-Assisted URLLC System via Multi-agent Deep Reinforcement LearningInternational Journal of Computational Intelligence Systems10.1007/s44196-024-00530-817:1Online publication date: 3-Jun-2024
https://doi.org/10.1007/s44196-024-00530-8
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Index Terms

Explainability in Deep Reinforcement Learning: A Review into Current Methods and Applications
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Reinforcement learning
    2. Machine learning algorithms
      1. Dynamic programming for Markov decision processes
        Q-learning

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 56, Issue 5

May 2024

1019 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3613598

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 08 December 2023

Online AM: 01 November 2023

Accepted: 11 October 2023

Revised: 19 June 2023

Received: 13 July 2022

Published in CSUR Volume 56, Issue 5

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Cited By

Núñez-Molina CMesejo PFernández-Olivares J(2024)A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision MakingACM Computing Surveys10.1145/366336656:11(1-36)Online publication date: 28-Jun-2024
https://dl.acm.org/doi/10.1145/3663366
Ozalp RUcar AGuzelis C(2024)Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Toward Trustworthy, Interpretable, and Explainable Artificial IntelligenceIEEE Access10.1109/ACCESS.2024.338542612(51840-51858)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3385426
Li XZhang XLi JLuo FHuang YZhang X(2024)Blocklength Allocation and Power Control in UAV-Assisted URLLC System via Multi-agent Deep Reinforcement LearningInternational Journal of Computational Intelligence Systems10.1007/s44196-024-00530-817:1Online publication date: 3-Jun-2024
https://doi.org/10.1007/s44196-024-00530-8
Engelhardt RLange MWiskott LKonen W(2024)Exploring the Reliability of SHAP Values in Reinforcement LearningExplainable Artificial Intelligence10.1007/978-3-031-63800-8_9(165-184)Online publication date: 10-Jul-2024
https://doi.org/10.1007/978-3-031-63800-8_9
KHAN MMEHAK SYASIR DANWAR SMAJEED MRAMZAN H(2023)QUANTITATIVE STUDIES OF DEEP REINFORCEMENT LEARNING IN GAMING, ROBOTICS AND REAL-WORLD CONTROL SYSTEMSBulletin of Business and Economics (BBE)10.61506/01.0001912:2(389-395)Online publication date: 29-Aug-2023
https://doi.org/10.61506/01.00019
Çetin EBarrado CPastor E(2023)Explainability of Deep Reinforcement Learning Method with Drones2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC)10.1109/DASC58513.2023.10311156(1-9)Online publication date: 1-Oct-2023
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