Hybrid DDPG Approach for Vehicle Motion Planning

Árpád Fehér, Szilárd Aradi, Ferenc Hegedűs, Tamás Bécsi, Péter Gáspár

2019

Abstract

The paper presents a motion planning solution which combines classic control techniques with machine learning. For this task, a reinforcement learning environment has been created, where the quality of the fulfilment of the designed path by a classic control loop provides the reward function. System dynamics is described by a nonlinear planar single track vehicle model with dynamic wheel mode model. The goodness of the planned trajectory is evaluated by driving the vehicle along the track. The paper shows that this encapsulated problem and environment provides a one-step reinforcement learning task with continuous actions that can be handled with Deep Deterministic Policy Gradient learning agent. The solution of the problem provides a real-time neural network-based motion planner along with a tracking algorithm, and since the trained network provides a preliminary estimate on the expected reward of the current state-action pair, the system acts as a trajectory feasibility estimator as well.

Paper Citation

in Harvard Style

Fehér Á., Aradi S., Hegedűs F., Bécsi T. and Gáspár P. (2019). Hybrid DDPG Approach for Vehicle Motion Planning.In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-380-3, pages 422-429. DOI: 10.5220/0007955504220429

in Bibtex Style

@conference{icinco19,
author={Árpád Fehér and Szilárd Aradi and Ferenc Hegedűs and Tamás Bécsi and Péter Gáspár},
title={Hybrid DDPG Approach for Vehicle Motion Planning},
booktitle={Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2019},
pages={422-429},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007955504220429},
isbn={978-989-758-380-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Hybrid DDPG Approach for Vehicle Motion Planning
SN - 978-989-758-380-3
AU - Fehér Á.
AU - Aradi S.
AU - Hegedűs F.
AU - Bécsi T.
AU - Gáspár P.
PY - 2019
SP - 422
EP - 429
DO - 10.5220/0007955504220429