Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning

This paper introduces a method, based on deep reinforcement learning, for automatically generating a general purpose decision making function. A Deep Q-Network agent was trained in a simulated environment to handle speed and lane change decisions for a truck-trailer combination. In a highway driving...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Proceedings (IEEE Conference on Intelligent Transportation Systems) Ročník 2018-November; s. 2148 - 2155
Hlavní autori: Hoel, Carl-Johan, Wolff, Krister, Laine, Leo
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.11.2018
Predmet:
Accelerated aging
Artificial Intelligence (cs.AI)
Decision making
Machine Learning (cs.LG)
Markov processes
Neurons
Roads
Robotics (cs.RO)
ISBN:9781728103211, 1728103215
ISSN:2153-0009, 2153-0017, 2153-0017
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:This paper introduces a method, based on deep reinforcement learning, for automatically generating a general purpose decision making function. A Deep Q-Network agent was trained in a simulated environment to handle speed and lane change decisions for a truck-trailer combination. In a highway driving case, it is shown that the method produced an agent that matched or surpassed the performance of a commonly used reference model. To demonstrate the generality of the method, the exact same algorithm was also tested by training it for an overtaking case on a road with oncoming traffic. Furthermore, a novel way of applying a convolutional neural network to high level input that represents interchangeable objects is also introduced.
ISBN:9781728103211
1728103215
ISSN:2153-0009
2153-0017
2153-0017
DOI:10.1109/ITSC.2018.8569568