# Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks

arXiv: https://arxiv.org/abs/1612.03653 · HTML: https://mlsanity.com/paper/1612.03653/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2016.

## Built on by (most cited first, 15 of 15)

- [Explainability of deep vision-based autonomous driving systems: Review and challenges](https://mlsanity.com/paper/2101.05307/) (2021)
- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and Priors](https://mlsanity.com/paper/2305.04412/) (2023)
- [Towards Theoretical Understanding of Inverse Reinforcement Learning](https://mlsanity.com/paper/2304.12966/) (2023)
- [Regularized Inverse Reinforcement Learning](https://mlsanity.com/paper/2010.03691/) (2020)
- [Reinforcement Learning Based Safe Decision Making for Highway Autonomous Driving](https://mlsanity.com/paper/2105.06517/) (2021)
- [Autonomous Driving with Deep Reinforcement Learning in CARLA Simulation](https://mlsanity.com/paper/2306.11217/) (2023)
- [Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems](https://mlsanity.com/paper/2007.03481/) (2020)
- [A Generalised Inverse Reinforcement Learning Framework](https://mlsanity.com/paper/2105.11812/) (2021)
- [Decentralized Cooperative Lane Changing at Freeway Weaving Areas Using Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2110.08124/) (2021)
- [On the Choice of Data for Efficient Training and Validation of End-to-End Driving Models](https://mlsanity.com/paper/2206.00608/) (2022)
- [Interactive Imitation Learning in Robotics based on Simulations](https://mlsanity.com/paper/2209.03900/) (2022)
- [Reinforced Imitative Trajectory Planning for Urban Automated Driving](https://mlsanity.com/paper/2410.15607/) (2024)
- [Understanding Driver Cognition and Decision-Making Behaviors in High-Risk Scenarios: A Drift Diffusion Perspective](https://mlsanity.com/paper/2503.12637/) (2025)
- [Loop-Free Inverse Reinforcement Learning via Sequential Value Recovery with Q-Score Matching](https://mlsanity.com/paper/2609.38955/) (2026)

## Abstract (arXiv)

We propose an inverse reinforcement learning (IRL) approach using Deep Q-Networks to extract the rewards in problems with large state spaces. We evaluate the performance of this approach in a simulation-based autonomous driving scenario. Our results resemble the intuitive relation between the reward function and readings of distance sensors mounted at different poses on the car. We also show that, after a few learning rounds, our simulated agent generates collision-free motions and performs human-like lane change behaviour.