# A Survey on Explainable Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2502.06869 · HTML: https://mlsanity.com/paper/2502.06869/

## 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.

## Placed in

- Deep reinforcement learning methods / [Interpretable and logic-guided RL](https://mlsanity.com/deeprl/interpretable-and-logic-guided-rl/)

## Built on by (most cited first, 12 of 12)

- [A Snapshot of Influence: A Local Data Attribution Framework for Online Reinforcement Learning](https://mlsanity.com/paper/2505.19281/) (2025)
- [Deep RL Needs Deep Behavior Analysis: Exploring Implicit Planning by Model-Free Agents in Open-Ended Environments](https://mlsanity.com/paper/2506.06981/) (2025)
- [Beyond Prediction: Reinforcement Learning as the Defining Leap in Healthcare AI](https://mlsanity.com/paper/2508.21101/) (2025)
- [Behaviour Discovery and Attribution for Explainable Reinforcement Learning](https://mlsanity.com/paper/2503.14973/) (2025)
- [Uncovering Latent Phase Structures and Branching Logic in Locomotion Policies: A Case Study on HalfCheetah](https://mlsanity.com/paper/2603.18084/) (2026)
- [Verification-Guided Falsification for Safe RL via Explainable Abstraction and Risk-Aware Exploration](https://mlsanity.com/paper/2506.03469/) (2025)
- [STACHE: Local Black-Box Explanations for Reinforcement Learning Policies](https://mlsanity.com/paper/2512.09909/) (2025)
- [Trustworthy and Explainable Deep Reinforcement Learning for Safe and Energy-Efficient Process Control: A Use Case in Industrial Compressed Air Systems](https://mlsanity.com/paper/2512.18317/) (2025)
- [Deep Intrinsic Surprise-Regularized Control (DISRC): A Biologically Inspired Mechanism for Efficient Deep Q-Learning in Sparse Environments](https://mlsanity.com/paper/2601.17598/) (2026)
- [M2-PALE: A Framework for Explaining Multi-Agent MCTS--Minimax Hybrids via Process Mining and LLMs](https://mlsanity.com/paper/2604.14687/) (2026)
- [Price of Fairness in Short-Term and Long-Term Algorithmic Selections](https://mlsanity.com/paper/2605.06227/) (2026)
- [SPOTting the Future: Lookahead Explanations for Deep Reinforcement Learning](https://mlsanity.com/paper/2608.09967/) (2026)

## 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.