# XDQN: Inherently Interpretable DQN through Mimicking

arXiv: https://arxiv.org/abs/2301.03043 · HTML: https://mlsanity.com/paper/2301.03043/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 37 references that are in the map.
- First posted on arXiv 8 Jan 2023.

## Builds on (most cited first, 22 of 22)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Do Deep Nets Really Need to be Deep?](https://mlsanity.com/paper/1312.6184/) (2013)
- [Policy Distillation](https://mlsanity.com/paper/1511.06295/) (2015)
- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates](https://mlsanity.com/paper/1610.00633/) (2016)
- [Interpretable machine learning: definitions, methods, and applications](https://mlsanity.com/paper/1901.04592/) (2019)
- [Distilling a Neural Network Into a Soft Decision Tree](https://mlsanity.com/paper/1711.09784/) (2017)
- [Verifiable Reinforcement Learning via Policy Extraction](https://mlsanity.com/paper/1805.08328/) (2018)
- [Explainable Reinforcement Learning Through a Causal Lens](https://mlsanity.com/paper/1905.10958/) (2019)
- [Principles and Practice of Explainable Machine Learning](https://mlsanity.com/paper/2009.11698/) (2020)
- [Towards Interpretable Reinforcement Learning Using Attention Augmented Agents](https://mlsanity.com/paper/1906.02500/) (2019)
- [Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees](https://mlsanity.com/paper/1807.05887/) (2018)
- [DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems](https://mlsanity.com/paper/1909.03602/) (2019)
- [Generation of Policy-Level Explanations for Reinforcement Learning](https://mlsanity.com/paper/1905.12044/) (2019)
- [Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy](https://mlsanity.com/paper/1907.01180/) (2019)
- [Towards Better Interpretability in Deep Q-Networks](https://mlsanity.com/paper/1809.05630/) (2018)
- [Resolving Congestions in the Air Traffic Management Domain via Multiagent Reinforcement Learning Methods](https://mlsanity.com/paper/1912.06860/) (2019)
- [Tree-based Focused Web Crawling with Reinforcement Learning](https://mlsanity.com/paper/2112.07620/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Differentiable Atari VCS:A Complex, Fully Known Ground Truth for Explainable AI](https://mlsanity.com/paper/2606.22447/) (2026)

## Abstract (arXiv)

Although deep reinforcement learning (DRL) methods have been successfully applied in challenging tasks, their application in real-world operational settings is challenged by methods' limited ability to provide explanations. Among the paradigms for explainability in DRL is the interpretable box design paradigm, where interpretable models substitute inner constituent models of the DRL method, thus making the DRL method "inherently" interpretable. In this paper we explore this paradigm and we propose XDQN, an explainable variation of DQN, which uses an interpretable policy model trained through mimicking. XDQN is challenged in a complex, real-world operational multi-agent problem, where agents are independent learners solving congestion problems. Specifically, XDQN is evaluated in three MARL scenarios, pertaining to the demand-capacity balancing problem of air traffic management. XDQN achieves performance similar to that of DQN, while its abilities to provide global models' interpretations and interpretations of local decisions are demonstrated.