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about	A deep RL system demo for modeling indoor behavior and covid-19 transmissions	/behavior_modeling

Simulating Indoor Human Behavior and Virus Transmission with Deep RL

I developed a deep reinforcement learning (RL) system from scratch to simulate how people move about in indoor environments like offices or classrooms. The agent models realistic human behaviors such as walking to meetings, taking breaks, or interacting with others—and learns them through trial and error using deep RL techniques.

These simulations are to be used to study how viruses like COVID-19 might spread as people go about their routines. The model enables us to test the impact of different public health interventions (e.g., staggered schedules, mask-wearing, or room ventilation) in a data-driven way.

Code available on request.