Deep Reinforcement Learning Control Strategy at Roundabout for i-CAR Autonomous Car

Muhtadin,; Meliaz, Muhammad Roychan; Dikairono, Rudy; Purnama, I Ketut Eddy; Purnomo, Mauridhi Hery

doi:10.1109/isitia59021.2023.10221077

Cited by 1 publication

(1 citation statement)

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“…In order to solve the problems of sample correlation and unstable data distribution, DQN introduces an empirical playback mechanism. This means that the user's observation sequences, behaviours taken, rewards obtained, and next states are saved into the experience pool when interacting with the user [15]. Then, batch samples are randomly selected from the experience pool for training to reduce the correlation between samples.…”

Section: Deep Q-learning (Dqn)mentioning

confidence: 99%

Machine learning algorithms in the development of recommender systems

Guan

2024

ACE

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With the continuous development and widespread use of the Internet, individuals often find themselves overwhelmed by the vast amount of information available. To address this issue and better cater to users' personalized needs, recommender systems have emerged. A recommender system is a technology that provides users with customized content or services based on their preferences and interests. Its primary goal is to predict user behavior as accurately as possible in order to recommend relevant items. Machine learning algorithms play a crucial role in the functioning of recommender systems. These algorithms not only automatically analyze and process large volumes of data but also extract valuable features from historical data, enabling accurate predictions for unknown data. The rapid progress of deep learning technology has significantly enhanced the accuracy and efficiency of recommender systems. Deep learning enables recommender systems to better grasp user behavior, interests, and preferences, leading to more precise predictions of user needs and behaviors. Furthermore, deep learning can handle unstructured data such as images, audio, and text, extracting valuable features to improve recommendations. In the future, as machine learning and artificial intelligence technologies continue to advance and gain popularity, recommender systems will find broader applications. Apart from the traditional e-commerce sector, recommender systems can also be applied to social networks, news media, online education, and other fields. Simultaneously, in the research and implementation of recommender systems, due attention must be given to user privacy protection and data security. Only by ensuring user privacy and data security can recommender systems effectively meet user needs and gain wider acceptance.

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Section: Deep Q-learning (Dqn)mentioning

confidence: 99%

Machine learning algorithms in the development of recommender systems

Guan

2024

ACE

View full text Add to dashboard Cite

show abstract

Deep Reinforcement Learning Control Strategy at Roundabout for i-CAR Autonomous Car

Cited by 1 publication

References 12 publications

Machine learning algorithms in the development of recommender systems

Machine learning algorithms in the development of recommender systems

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