mlsanityData to 14 Oct 2026

A Survey of Explainable Reinforcement Learning: Targets, Methods and Needs

arXiv 2507.12599 ↗First posted 16 Jul 2025cs.AI cs.LGcited by 3

On the map: Deep reinforcement learning methods → Interpretable and logic-guided RL

Abstract (arXiv)

The success of recent Artificial Intelligence (AI) models has been accompanied by the opacity of their internal mechanisms, due notably to the use of deep neural networks. In order to understand these internal mechanisms and explain the output of these AI models, a set of methods have been proposed, grouped under the domain of eXplainable AI (XAI). This paper focuses on a sub-domain of XAI, called eXplainable Reinforcement Learning (XRL), which aims to explain the actions of an agent that has learned by reinforcement learning. We propose an intuitive taxonomy based on two questions "What" and "How". The first question focuses on the target that the method explains, while the second relates to the way the explanation is provided. We use this taxonomy to provide a state-of-the-art review of over 250 papers. In addition, we present a set of domains close to XRL, which we believe should get attention from the community. Finally, we identify some needs for the field of XRL.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Interpretable and logic-guided RL, one of 15 directions in Deep reinforcement learning methods. First posted on arXiv 16 Jul 2025.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026BXRL: Behavior-Explainable Reinforcement Learningcited by 1
  2. 2026A Differentiable Atari VCS:A Complex, Fully Known Ground Truth for Explainable AIcited by 0
  3. 2026Automated Testing of LLM-Based Post Hoc Explainers Using Model Checking as an Oraclecited by 0

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  1. 2025A Survey on Explainable Deep Reinforcement Learning
  2. 2025Explainable Reinforcement Learning Agents Using World Models
  3. 2026Explaining Reinforcement Learning Agents via Inductive Logic Programming
  4. 2024XRL-Bench: A Benchmark for Evaluating and Comparing Explainable Reinforcement Learning Techniques
  5. 2022Decisions that Explain Themselves: A User-Centric Deep Reinforcement Learning Explanation System
  6. 2026Textual Explanations and Their Evaluations for Reinforcement Learning Policy
  7. 2024Towards a Research Community in Interpretable Reinforcement Learning: the InterpPol Workshop
  8. 2024Abstracted Trajectory Visualization for Explainability in Reinforcement Learning