MSOE Senior Design - Adversarial Strategy VEX Robot Program

Adversarial strategy and game playing project for the MSOE Senior Design Team focused on bringing AI to the MSOE VEX U Team's robots.

Get started

After installing packages and activating the virtual env, run

python src/training.py

To view all training benchmarks in tensorboard:

python -m tensorboard.main --logdir logs/tensorboard/

Interpret results here

Installing Through Pip

This package can be installed through a pip package, using the following command:

pip install git+https://github.com/msoe-vex/senior-design-adversarial-strategy

To install a pip package pointing to a specific branch, append @[BRANCH] to the end of the command above, replacing [BRANCH] with your branch name.

Running Locally

Running this project locally requires installing some Pip packages to get all dependencies sorted. We highly recommend utilizing a Python virtual environment, which can be set up with one of the following command sets running in the project root:

Executing in Bash

python -m venv venv
source venv/Scripts/activate
pip install -r requirements.txt

Executing in Powershell

python -m venv venv
.\venv\Scripts\Activate.ps1
pip install -r requirements.txt

Unit Testing

This code utilizes the python unittest library for running unit tests. This can be done by running the following at the root of the project:

python -m unittest discover

Optionally, you can utilize the built-in VSCode tasks to run unit tests, located in the top menu (Terminal > Run Task > Run Unit Tests). This requires adding a settings.json file to your local .vscode folder, with the python.pythonPath variable being set (either to your virtual environment python interpreter, or your system python interpreter).

An example configuration, with a virtual environment called venv is shown below:

{
    "python.pythonPath": "venv/Scripts/python.exe"
}

Reinforcement Learning Components

After installing packages and activating the virtual env, run

python src/training.py

To view all training benchmarks in tensorboard:

python -m tensorboard.main --logdir logs/tensorboard/

Interpret results here

Contributing

Some libraries are currently used by this repository to help boost code quality and functionality. To download them, run the following line from the project root directory:

pip install -r requirements.txt

NOTE: We recommend a Python virtual environment is used for this.

The libraries included are discussed below as needed.

Black: Python Formatting Utility

Black is a python formatting utility which is included in this project to help with formatting. It can be run using the following command, which will format all files within a specified directory:

black *.py

This will automatically format all files within a specific directory. Currently, no recursive option exists for this formatting.