# Symbolic Imitation Learning: From Black-Box to Explainable Driving Policies

arXiv: https://arxiv.org/abs/2309.16025 · HTML: https://mlsanity.com/paper/2309.16025/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving.
- Builds on 21 of its 44 references that are in the map.
- First posted on arXiv 27 Sep 2023.

## Placed in

- End-to-end autonomous driving / [Imitation-learning end-to-end planners](https://mlsanity.com/driving/imitation-learning-end-to-end-planners/)
- Offline RL and imitation learning from data / [Imitation and offline RL for driving](https://mlsanity.com/offlinerl/imitation-and-offline-rl-for-driving/)

## Builds on (most cited first, 21 of 21)

- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019)
- [Exploring the Limitations of Behavior Cloning for Autonomous Driving](https://mlsanity.com/paper/1904.08980/) (2019)
- [A Survey on Neural Network Interpretability](https://mlsanity.com/paper/2012.14261/) (2020)
- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [A Divergence Minimization Perspective on Imitation Learning Methods](https://mlsanity.com/paper/1911.02256/) (2019)
- [Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention](https://mlsanity.com/paper/1703.10631/) (2017)
- [Inductive logic programming at 30: a new introduction](https://mlsanity.com/paper/2008.07912/) (2020)
- [Query-Efficient Imitation Learning for End-to-End Autonomous Driving](https://mlsanity.com/paper/1605.06450/) (2016)
- [Learning programs by learning from failures](https://mlsanity.com/paper/2005.02259/) (2020)
- [Off-Policy Imitation Learning from Observations](https://mlsanity.com/paper/2102.13185/) (2021)
- [Neuro-Symbolic Artificial Intelligence: Current Trends](https://mlsanity.com/paper/2105.05330/) (2021)
- [Turning 30: New Ideas in Inductive Logic Programming](https://mlsanity.com/paper/2002.11002/) (2020)
- [Neuro-Symbolic Reinforcement Learning with First-Order Logic](https://mlsanity.com/paper/2110.10963/) (2021)
- [Differentiable Logic Machines](https://mlsanity.com/paper/2102.11529/) (2021)
- [Explainable Hierarchical Imitation Learning for Robotic Drink Pouring](https://mlsanity.com/paper/2105.07348/) (2021)
- [Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2307.01316/) (2023)
- [Modelling Agent Policies with Interpretable Imitation Learning](https://mlsanity.com/paper/2006.11309/) (2020)
- [Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Survey](https://mlsanity.com/paper/2304.12090/) (2023)
- [Adversarial Mixture Density Networks: Learning to Drive Safely from Collision Data](https://mlsanity.com/paper/2107.04485/) (2021)
- [Explaining Imitation Learning through Frames](https://mlsanity.com/paper/2301.01088/) (2023)

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

- [ANDRE: An Attention-based Neuro-symbolic Differentiable Rule Extractor for Inductive Logic Programming](https://mlsanity.com/paper/2605.04193/) (2026)

## Abstract (arXiv)

Current imitation learning approaches, predominantly based on deep neural networks (DNNs), offer efficient mechanisms for learning driving policies from real-world datasets. However, they suffer from inherent limitations in interpretability and generalizability--issues of critical importance in safety-critical domains such as autonomous driving. In this paper, we introduce Symbolic Imitation Learning (SIL), a novel framework that leverages Inductive Logic Programming (ILP) to derive explainable and generalizable driving policies from synthetic datasets. We evaluate SIL on real-world HighD and NGSim datasets, comparing its performance with state-of-the-art neural imitation learning methods using metrics such as collision rate, lane change efficiency, and average speed. The results indicate that SIL significantly enhances policy transparency while maintaining strong performance across varied driving conditions. These findings highlight the potential of integrating ILP into imitation learning to promote safer and more reliable autonomous systems.