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- W4387496502 abstract "The popularity of autonomous vehicles (AVs) has raised interest in creating sophisticated control algorithms to raise the effectiveness and safety of these vehicles. In-depth analysis of current advancements in reinforcement learning (RL) for steering control in AVs is the goal of this overview article. RL has emerged as a potential method for steering control in AVs. RL is a sort of machine learning that concentrates on teaching agents to make decisions based on trial and error. The first section of this study gives an overview of the many RL algorithms, including Q-learning, deep Q-networks, double deep Q-networks, and duelling double deep Q-networks, that have been suggested for steering control in AVs. Overall, this survey article offers a thorough analysis of the contrast between RL-based steering control algorithms with the goal of serving as a useful tool for academics, engineers, and professionals engaged in the field of autonomous driving." @default.
- W4387496502 created "2023-10-11" @default.
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- W4387496502 date "2023-01-01" @default.
- W4387496502 modified "2023-10-12" @default.
- W4387496502 title "Controlling the Steering Wheel Using Deep Reinforcement Learning: A Survey" @default.
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- W4387496502 doi "https://doi.org/10.1007/978-981-99-4626-6_65" @default.
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