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Full environment for exploring AIs in grid environments
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Full featured AI grid environment

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This is a playground for exploring AIs and search algorithms in grid environments. It has a web interface built with Python Flask and some cool modes to play around with. Enjoy! gridaiscreenshot



  1. Install the relevant Python dependencies (Flask and related). I recommend installing these in a virtual environment. For more information on virtual environments in Python, see Virtualenv

    pip install -r requirements.txt

    If working with Virtualenv, first activate the virtual environment by using

    • OSX/Linux:

      source <name-of-the-environment-folder>/bin/activate
    • Windows (with some sort of bash client):

      source <name-of-the-environment-folder>/Scripts/activate
  2. Install the relevant node dependencies for the frontend build workflow. As simple as:

    npm i

    And now you can just sit back and watch

Running the Project and Seeing Pretty Things

The server can be run from within the current directory using:


Before viewing in the browser, the webpack build process also needs to be run at least once to build the client-side assets. There are already npm scripts set up for this:

  • For running in dev (watch) mode - so that any changes to source code rebuilds new assets:

    npm run dev
  • For running in production mode - if you just want to see how it is when it's ready for publishing:

    npm run prod

Fire it up and see how you like it.


The maze is generated using a variation of Kruskal's Algorithm for generating minimum weight spanning trees of a graph.

The search algorithms are common graph search algorithms, outlined below:

  • Depth first search - a search method that prioritizes fully exploring a given branch before trying a different branch.

    Implemented here using a stack to store the fringe (and by extension the parental heirarchy of a given branch)

  • Breadth first search - a search method that extends radially outward from a starting point and successively explores each level fully before searching along the next level, where levels are defined by distance from the starting node (could be thought of as a kind of cost function, for those who know what's coming next 😉)

    Implemented here using a queue to store the fringe

  • Best first search - a search method that uses a heuristic to evaulate the worth of each cell at the edge of the fringe and selects the best cell to explore next

    Implemented using Euclidean distance from goal as heuristic and using a sorted list (though ideal would be a heap)

  • A* search - an enhancement on best first search that uses a combination of heuristic and cost function to decide the best cell to explore next

    Implemented using Euclidean distance from goal as heuristic (as in best first search above) and level (as in breadth first search) as cost function, using a sorted list for the fringe (as with best first search, ideal would be min heap)


  • Ananth Rao (@ananthamapod)


GPL, for more information, see LICENSE

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