Self-learning and adaptive algorithms (Reinforcement learning)

A list of my implementations of homework tasks for 'Self-learning and adaptive algorithms' course.

Running a certain homework task

Clone the repository using command

git clone https://github.com/usernamenenad/Reinforcement-Learning-Course.git

and position yourself in a homework folder of interest.

Then, it is necessary to make a python virtual environment. I recommend using poetry, but you can go with the native virtual environment provided by python itself. Using python's default venv, execute

python -m venv .venv

and activate it based on the OS you're currently on. If that's Windows, execute in PowerShell

.\.venv\Scripts\activate.ps1

If you're on Linux, activate the virtual environment by executing in bash

source ./.venv/bin/activate

A list of dependencies is located in requirements.txt and it is necessary to install required packages by executing

pip install -r requirements.txt

If you're using PyCharm IDE, it will automatically install required packages for you.

Homeworks are constructed in such a way as to run them as test files. After setting up your virtual environment, pytest is automatically installed, so you position yourself in a desired homework folder and run

pytest -s

This will run all tests. If you want to run a specific test, execute

pytest -s ./tests/`TESTNAME`