mlsanityData to 7 Oct 2026

Explainability in Deep Reinforcement Learning, a Review into Current Methods and Applications

arXiv 2207.01911 ↗First posted 5 Jul 2022cs.LG cs.AIcited by 6

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Abstract (arXiv)

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 (XAI) 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 (DT) 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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 26 of its 74 references that are in the map. First posted on arXiv 5 Jul 2022.

Builds on

26 of 74 references.

References · 26

  1. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 723
  2. 2020Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Surveycited by 252
  3. 2018SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planningcited by 77
  4. 2018Toward Interpretable Deep Reinforcement Learning with Linear Model U-Treescited by 65
  5. 2016Learning to Perform Physics Experiments via Deep Reinforcement Learningcited by 47
  6. 2021DRIVE: Deep Reinforced Accident Anticipation with Visual Explanationcited by 46
  7. 2021Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley Valuescited by 24
  8. 2019Explaining Reinforcement Learning to Mere Mortals: An Empirical Studycited by 21
  9. 2020Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenariocited by 19
  10. 2020Machine versus Human Attention in Deep Reinforcement Learning Taskscited by 16
  11. 2021Why? Why not? When? Visual Explanations of Agent Behavior in Reinforcement Learningcited by 16
  12. 2020CDT: Cascading Decision Trees for Explainable Reinforcement Learningcited by 15
14 more
  1. 2019Mo"ET: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learningcited by 12
  2. 2019DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learningcited by 12
  3. 2020Towards Interpretable-AI Policies Induction using Evolutionary Nonlinear Decision Trees for Discrete Action Systemscited by 12
  4. 2021Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Programcited by 11
  5. 2021Explaining Deep Reinforcement Learning Agents In The Atari Domain through a Surrogate Modelcited by 9
  6. 2021Interpretable Model-based Hierarchical Reinforcement Learning using Inductive Logic Programmingcited by 8
  7. 2021Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systemscited by 7
  8. 2021Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agentscited by 5
  9. 2020Moody Learners -- Explaining Competitive Behaviour of Reinforcement Learning Agentscited by 4
  10. 2021Robotic Lever Manipulation using Hindsight Experience Replay and Shapley Additive Explanationscited by 4
  11. 2021Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learningcited by 4
  12. 2021XAI-N: Sensor-based Robot Navigation using Expert Policies and Decision Treescited by 3
  13. 2020Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Casecited by 2
  14. 2021A Novel Approach to Curiosity and Explainable Reinforcement Learning via Interpretable Sub-Goalscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 2; 2026: 1.

6 papers.

  1. 2024A Causal Explainable Guardrails for Large Language Modelscited by 18
  2. 2025A Comprehensive Survey on Self-Interpretable Neural Networkscited by 18
  3. 2025Multimodal LLM-assisted Evolutionary Search for Programmatic Control Policiescited by 8
  4. 2023A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Makingcited by 2
  5. 2026Uncovering Latent Phase Structures and Branching Logic in Locomotion Policies: A Case Study on HalfCheetahcited by 1
  6. 2024Why the Agent Made that Decision: Contrastive Explanation Learning for Reinforcement Learningcited by 0