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Two reinforcement learning algorithms (Standard SARSA Control and Tabular Dyna-Q) where an agent learns to traverse a randomly generated maze

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RL-Maze

Standard SARSA Control and Tabular Dyna-Q are used to solve for a solution for a given maze.

To run the algorithm:

  • Download the necessary packages
    • $ pip install -r requirements.txt
  • Download the Chrome WebDriver for your version of Google Chrome
    • Note: You can find your current version of Google Chrome by clicking the three dots in the top right → "Help" → "About Google Chrome"
  • Copy the path where the Chrome WebDriver was downloaded and set to a variable called PATH in the Jupyter Notebook
    • Example: PATH = 'D:\Program Files (x86)\chromedriver.exe'
  • Copy the path of the maze directory in your local copy of this repository and set to a variable called DOWNLOAD_PATH in the Jupyter Notebook
    • This will be the location where the image of the maze will be downloaded
    • Example: DOWNLOAD_PATH = 'D:\Github\Work\RL-Maze\maze'
  • Run the Jupyter Notebook

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Two reinforcement learning algorithms (Standard SARSA Control and Tabular Dyna-Q) where an agent learns to traverse a randomly generated maze

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