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...

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Vydáno v:Proceedings (IEEE Conference on Intelligent Transportation Systems) Ročník 2018-November; s. 2148 - 2155
Hlavní autoři: Hoel, Carl-Johan, Wolff, Krister, Laine, Leo
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.11.2018
Témata:
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
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Abstract 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.
AbstractList 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.
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. https://arxiv.org/abs/1803.10056
Author Wolff, Krister
Laine, Leo
Hoel, Carl-Johan
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  email: leo.laine@chalmers.se
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Snippet This paper introduces a method, based on deep reinforcement learning, for automatically generating a general purpose decision making function. A Deep Q-Network...
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StartPage 2148
SubjectTerms Accelerated aging
Artificial Intelligence (cs.AI)
Decision making
Machine Learning (cs.LG)
Markov processes
Neurons
Roads
Robotics (cs.RO)
Title Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning
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https://research.chalmers.se/publication/508724
Volume 2018-November
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