Inspiration

Current times and how we dealt with the situation

What it does

It uses PyTorch to give an optimal outcome to the virus The optimal outcome being no more people getting infected

How we built it

I used unity to build the environment, used C# for scripts, and Python for machine learning

Challenges we ran into

Lots of issues - physics engine, kool-aid man(people going through walls), Time constraint, Environment being too large for mass learning

Accomplishments that we're proud of

The House setting works great It's my first time doing ML, so I feel proud of that

What we learned

Unity MLAGENTS library ML time-consuming. Reinforcement learning. Imitation learning.

What's next for Virus Simulator

I would like to get the store, work, and park environment working for a better simulation, added an apartment setting, add options for the user to add objects or people. goals for different types of people

Share this project:

Updates