Code underlying the publication: Comprehensive Training and Evaluation on Deep Reinforcement Learning for Automated Driving in Various Simulated Driving Maneuvers
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This is the code and data related to the publication: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. keywords: {Training;Deep learning;Roads;Reinforcement learning;Automobiles;Task analysis;Optimization} 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 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~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 …
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