Abstract

The growing population and the need to improve livability in cities have led to the development of smart cities usingemerging technologies such as the Internet of Things (IoT). Wastemanagement is a critical aspect of a smart city as it impacts thehealth of citizens and the environment. To address this issue, ourresearch focuses on using IoT to collect data and applying multi-agent deep reinforcement learning algorithms to extract meaningfulinsights from the data. The data is used to provide notifications, extract insights, and predict waste fill levels to optimize wastecollection routes. Our proposed framework is hardware-agnosticand can effectively interface with a wide variety of hardware while keeping the architecture abstracted.

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