# Deep Reinforcement Learning for Time Optimal Velocity Control using Prior Knowledge

arXiv: https://arxiv.org/abs/1811.11615 · HTML: https://mlsanity.com/paper/1811.11615/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2018.

## Built on by (most cited first, 6 of 6)

- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles](https://mlsanity.com/paper/2105.14218/) (2021)
- [Criticality-Based Varying Step-Number Algorithm for Reinforcement Learning](https://mlsanity.com/paper/2201.05034/) (2022)
- [Meta-Reinforcement Learning Using Model Parameters](https://mlsanity.com/paper/2210.15515/) (2022)
- [Neural Network Algorithm for Intercepting Targets Moving Along Known Trajectories by a Dubins' Car](https://mlsanity.com/paper/2304.06169/) (2023)
- [A Visual Tool for Interactive Model Explanation using Sensitivity Analysis](https://mlsanity.com/paper/2508.04269/) (2025)

## Abstract (arXiv)

Autonomous navigation has recently gained great interest in the field of reinforcement learning. However, little attention was given to the time optimal velocity control problem, i.e. controlling a vehicle such that it travels at the maximal speed without becoming dynamically unstable (roll-over or sliding). Time optimal velocity control can be solved numerically using existing methods that are based on optimal control and vehicle dynamics. In this paper, we use deep reinforcement learning to generate the time optimal velocity control. Furthermore, we use the numerical solution to further improve the performance of the reinforcement learner. It is shown that the reinforcement learner outperforms the numerically derived solution, and that the hybrid approach (combining learning with the numerical solution) speeds up the training process.