Authors:
Árpád Fehér
1
;
Szilárd Aradi
1
;
Ferenc Hegedűs
1
;
Tamás Bécsi
1
and
Péter Gáspár
2
Affiliations:
1
Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, Budapest and Hungary
;
2
Computer and Automation Research Institute, Hungarian Academy of Sciences, Budapest and Hungary
Keyword(s):
Reinforcement Learning, Motion Planning, Autonomous Vehicles, Automotive Control.
Related
Ontology
Subjects/Areas/Topics:
Hybrid Learning Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Machine Learning in Control Applications
;
Mobile Robots and Autonomous Systems
;
Neural Networks Based Control Systems
;
Robotics and Automation
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 a
s well.
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