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Among Us meets Reinforcement Learning

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Sus-Net (Playing Among Us with RL)

In this project we explore the application of Deep Q-Learning to the popular mobile game Among Us. We create a simulation of the game containing two teams, imposters and crew members. While crew members seek to complete tasks throughout the map, imposters aim to eliminate the crew memebrs anb sabotage tasks without being voted out. Our simple 2D representation of the game features agents that are able to move, do jobs, sabotage, kill, and vote. Checkout our Presentation and Final Report for more details!



Screenshot 2024-06-05 at 12 15 12 AM

Setup

  1. Creating python virtual environment
conda env create -f environment.yml
conda activate sus_net
  1. Setup pre-commit hooks
pre-commit install
  1. Have fun!

Experimental Results

One Imposter -vs- One Crew Member

Empty Evironment

no_walls_1v1

Evironment with Walls

walls_1v1

One Imposter -vs- Two Crew Members

Empty Evironment

Not Walled

Evironment with Walls

Walled

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Among Us meets Reinforcement Learning

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  • Python 84.1%
  • Jupyter Notebook 15.7%
  • Shell 0.2%