Deep Reinforcement Learning for Time Optimal Velocity Control using Prior Knowledge
This work addresses a specific problem in autonomous navigation for vehicle control, but it is incremental as it builds on existing numerical methods.
The paper tackled the time optimal velocity control problem for autonomous vehicles by using deep reinforcement learning combined with a numerical solution, resulting in the reinforcement learner outperforming the numerical solution and speeding up training.
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.