mlsanityData to 14 Oct 2026

A Survey on Explainable Deep Reinforcement Learning

arXiv 2502.06869 ↗First posted 8 Feb 2025cs.LG cs.AIcited by 12

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

Abstract (arXiv)

Deep Reinforcement Learning (DRL) has achieved remarkable success in sequential decision-making tasks across diverse domains, yet its reliance on black-box neural architectures hinders interpretability, trust, and deployment in high-stakes applications. Explainable Deep Reinforcement Learning (XRL) addresses these challenges by enhancing transparency through feature-level, state-level, dataset-level, and model-level explanation techniques. This survey provides a comprehensive review of XRL methods, evaluates their qualitative and quantitative assessment frameworks, and explores their role in policy refinement, adversarial robustness, and security. Additionally, we examine the integration of reinforcement learning with Large Language Models (LLMs), particularly through Reinforcement Learning from Human Feedback (RLHF), which optimizes AI alignment with human preferences. We conclude by highlighting open research challenges and future directions to advance the development of interpretable, reliable, and accountable DRL systems.

Key facts. Cited by 12 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 8 Feb 2025.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 7; 2026: 5.

12 papers.

  1. 2025A Snapshot of Influence: A Local Data Attribution Framework for Online Reinforcement Learningcited by 11
  2. 2025Deep RL Needs Deep Behavior Analysis: Exploring Implicit Planning by Model-Free Agents in Open-Ended Environmentscited by 4
  3. 2025Beyond Prediction: Reinforcement Learning as the Defining Leap in Healthcare AIcited by 3
  4. 2025Behaviour Discovery and Attribution for Explainable Reinforcement Learningcited by 2
  5. 2026Uncovering Latent Phase Structures and Branching Logic in Locomotion Policies: A Case Study on HalfCheetahcited by 1
  6. 2025Verification-Guided Falsification for Safe RL via Explainable Abstraction and Risk-Aware Explorationcited by 0
  7. 2025STACHE: Local Black-Box Explanations for Reinforcement Learning Policiescited by 0
  8. 2025Trustworthy and Explainable Deep Reinforcement Learning for Safe and Energy-Efficient Process Control: A Use Case in Industrial Compressed Air Systemscited by 0
  9. 2026Deep Intrinsic Surprise-Regularized Control (DISRC): A Biologically Inspired Mechanism for Efficient Deep Q-Learning in Sparse Environmentscited by 0
  10. 2026M2-PALE: A Framework for Explaining Multi-Agent MCTS--Minimax Hybrids via Process Mining and LLMscited by 0
  11. 2026Price of Fairness in Short-Term and Long-Term Algorithmic Selectionscited by 0
  12. 2026SPOTting the Future: Lookahead Explanations for Deep Reinforcement Learningcited by 0

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  1. 2025A Survey of Explainable Reinforcement Learning: Targets, Methods and Needs
  2. 2025From Explainability to Interpretability: Interpretable Policies in Reinforcement Learning Via Model Explanation
  3. 2022Decisions that Explain Themselves: A User-Centric Deep Reinforcement Learning Explanation System
  4. 2025Explainable Reinforcement Learning Agents Using World Models
  5. 2026Explaining Reinforcement Learning Agents via Inductive Logic Programming
  6. 2024XRL-Bench: A Benchmark for Evaluating and Comparing Explainable Reinforcement Learning Techniques
  7. 2022Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning
  8. 2026Textual Explanations and Their Evaluations for Reinforcement Learning Policy