Source code for the MSc thesis: Multi-leader Adaptive Cruise Control Systems considering Sensor Measurement Uncertainties based on Deep Reinforcement Learning
doi:10.4121/20436090.v1
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doi: 10.4121/20436090
doi: 10.4121/20436090
Datacite citation style:
Ying-Chuan Ni (2022): Source code for the MSc thesis: Multi-leader Adaptive Cruise Control Systems considering Sensor Measurement Uncertainties based on Deep Reinforcement Learning. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/20436090.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Software
This repository contains programming codes for the training and simulation of Deep Reinforcement Learning-based Adaptive Cruise Control systems.
For more information, please see the README.txt file in the repository or the report of master thesis titled "Multi-leader Adaptive Cruise Control Systems considering Sensor Measurement Uncertainties based on Deep Reinforcement Learning", which can be found in the reference (link).
history
- 2022-08-22 first online, published, posted
publisher
4TU.ResearchData
format
Python source code/ .py
organizations
Delft University of Technology, Faculty of Civil Engineering and Geosciences, Department of Transport and Planning.
DATA
files (13)
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README.txt - 15,880 bytesMD5:
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ACC1_training.py - 15,827 bytesMD5:
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ACC2_training.py - 18,648 bytesMD5:
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LSTM_ACC1_training.py - 18,734 bytesMD5:
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LSTM_ACC2_training.py - 15,308 bytesMD5:
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one_ACC_KF_simulation.py - 11,793 bytesMD5:
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one_ACC_LSTM_simulation.py - 11,631 bytesMD5:
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one_ACC_noisy_simulation.py - 10,934 bytesMD5:
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one_ACC_simulation.py - 27,237 bytesMD5:
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two_ACC_KF_simulation.py - 20,043 bytesMD5:
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two_ACC_LSTM_simulation.py - 20,189 bytesMD5:
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two_ACC_noisy_simulation.py - 19,338 bytesMD5:
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two_ACC_simulation.py -
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