TOWARD LEARNING HUMAN-LIKE, SAFE AND COMFORTABLE CAR-FOLLOWING POLICIES WITH A NOVEL DEEP REINFORCEMENT LEARNING APPROACH

Toward Learning Human-Like, Safe and Comfortable Car-Following Policies With a Novel Deep Reinforcement Learning Approach

In this paper, we present an advanced adaptive cruise control (ACC) concept powered by Deep Reinforcement Learning (DRL) that generates safe, human-like, and comfortable car-following policies.Unlike the current trend in developing DRL-based ACC systems, we propose defining the action space of the DRL agent with discrete actions rather than Glue co

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Prevalence and risk factors of chlamydia infection in Hong Kong: A population-based geospatial household survey and testing.

BACKGROUND:Chlamydia causes infertility and increases risk of HIV infection, and population-based studies provide essential information for effective infection control and prevention.This study examined Chlamydia trachomatis prevalence and risk factors among a representative sample of 18-49-year-old residents in Hong Kong.METHODS:Census boundary ma

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