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Dong, T. Datema, V. Wassenaar, J. Van de Weg, C. T. Kopar and H. Suleman, \"Comprehensive Training and Evaluation on Deep Reinforcement Learning for Automated Driving in Various Simulated Driving Maneuvers,\" 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain, 2023, pp. 6165-6170, doi: 10.1109/ITSC57777.2023.10422159.  keywords: {Training;Deep learning;Roads;Reinforcement learning;Automobiles;Task analysis;Optimization}\u003cbr\u003eThe implementation is based on Python, Stable-Baselines3 (https://stable-baselines3.readthedocs.io/en/master/) and Highway_env simulation environment https://github.com/Farama-Foundation/HighwayEnv\u003cbr\u003e~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~Developing and testing automated driving models in the real world might be challenging and even dangerous, while simulation can help with this, especially for challenging manoeuvres. Deep reinforcement learning (DRL) has the potential to tackle complex decision-making and controlling tasks through learning and interacting with the environment, thus it is suitable for developing automated driving while not being explored in detail yet. This study carried out a comprehensive study by implementing, evaluating, and comparing the two DRL algorithms, Deep Q-networks (DQN) and Trust Region Policy Optimization (TRPO), for training automated driving on the highway-env simulation platform. Effective and customized reward functions were developed and the implemented algorithms were evaluated in terms of onlane accuracy (how well the car drives on the road within the lane), efficiency (how fast the car drives), safety (how likely the car is to crash into obstacles), and comfort (how much the car makes jerks, e.g., suddenly accelerates or brakes). Results show that the TRPO-based models with modified reward functions delivered the best performance in most cases. Furthermore, to train a uniform driving model that can tackle various driving manoeuvres besides the specific ones, this study expanded the highway-env and developed an extra customized training environment, namely, ComplexRoads, integrating various driving manoeuvres and multiple road scenarios together. Models trained on the designed ComplexRoads environment can adapt well to other driving manoeuvres with promising overall performance. Lastly, several functionalities were added to the highway-env to implement this work. The codes are open on GitHub at https://github.com/alaineman/drlcarsim-paper.\u003cbr\u003e","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"awardTitle":"Safe and efficient operation of AutoMated and human drivEN vehicles in mixed traffic","funderName":"Applied and Technical Sciences (TTW), a subdomain of the Dutch Institute for Scientific Research (NWO)","awardNumber":"17187"}],"xml":"<?xml version="1.0" encoding="UTF-8"?>
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    <title>Code underlying the publication: Comprehensive Training and Evaluation on Deep Reinforcement Learning for Automated Driving in Various Simulated Driving Maneuvers</title>
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    <description descriptionType="Abstract">&lt;p&gt;This is the code and data related to the publication:&lt;/p&gt;&lt;p&gt;Y. Dong, T. Datema, V. Wassenaar, J. Van de Weg, C. T. Kopar and H. Suleman, "Comprehensive Training and Evaluation on Deep Reinforcement Learning for Automated Driving in Various Simulated Driving Maneuvers," 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain, 2023, pp. 6165-6170, doi: 10.1109/ITSC57777.2023.10422159.&lt;/p&gt;&lt;p&gt;&amp;nbsp;&lt;/p&gt;&lt;p&gt;&amp;nbsp;keywords: {Training;Deep learning;Roads;Reinforcement learning;Automobiles;Task analysis;Optimization}&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;The implementation is based on Python, Stable-Baselines3 (https://stable-baselines3.readthedocs.io/en/master/) and Highway_env simulation environment https://github.com/Farama-Foundation/HighwayEnv&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~&lt;/p&gt;&lt;p&gt;Developing and testing automated driving models in the real world might be challenging and even dangerous, while simulation can help with this, especially for challenging manoeuvres. Deep reinforcement learning (DRL) has the potential to tackle complex decision-making and controlling tasks through learning and interacting with the environment, thus it is suitable for developing automated driving while not being explored in detail yet. This study carried out a comprehensive study by implementing, evaluating, and comparing the two DRL algorithms, Deep Q-networks (DQN) and Trust Region Policy Optimization (TRPO), for training automated driving on the highway-env simulation platform. Effective and customized reward functions were developed and the implemented algorithms were evaluated in terms of onlane accuracy (how well the car drives on the road within the lane), efficiency (how fast the car drives), safety (how likely the car is to crash into obstacles), and comfort (how much the car makes jerks, e.g., suddenly accelerates or brakes). Results show that the TRPO-based models with modified reward functions delivered the best performance in most cases. Furthermore, to train a uniform driving model that can tackle various driving manoeuvres besides the specific ones, this study expanded the highway-env and developed an extra customized training environment, namely, ComplexRoads, integrating various driving manoeuvres and multiple road scenarios together. Models trained on the designed ComplexRoads environment can adapt well to other driving manoeuvres with promising overall performance. Lastly, several functionalities were added to the highway-env to implement this work. The codes are open on GitHub at https://github.com/alaineman/drlcarsim-paper.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;</description>
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