First, the goal of this post is to get you working with a pathfinding neural network as quickly as possible. The best way to learn is to start doing and by the end of this tutorial you WILL have a working pathfinder! While this example uses PHP as the coding language, FANN is available for over 20 languages so you should have little trouble adapting this tutorial to work with your favorite language.

If you are following along with this tutorial, there are a couple of obligatory disclaimers… 😛

  1. You will need to setup a test environment with PHP and the FANN library. I have a tutorial on that here: Getting started with Neural Networks
    Note: The Official PHP FANN repo on Gitub agreed to include this as an official example so when you download or clone the PHP FANN repo the Pathfinder example will already be included for you in the examples folder.

     

  2. This will not be the most robust pathfinding neural network and it’s not meant to be. This is not Google Maps and is not capable of navigating for Tesla’s cars. It will however provide you with the basic tools and knowledge to create your own pathfinding neural networks for games, research or anything else you can think of!

Since most of my posts tend to get a little long I am going to make this a multi part post as usual. I intend for this post to provide the code and cover the basics and in the next post in the series we will dig into the details.

Meet Pathfinder

pathfinder
Pathfinder has 25 input neurons (green), 3 hidden layers with 70 hidden neurons on each layer (red) and 25 output neurons (blue).

Pathfinder is an example of a neural network that is capable of plotting an 8 direction step path from a starting position in a 5×5 grid to an ending position in that grid.

8 Direction Stepping

8-direction-step-path
8 Direction Stepping means that movement can be expressed in not just up, down, left and right but also diagonal steps as well.

The Grid

5x5-grid
Pathfinder’s view of the world.

To keep the Pathfinder example simple it is not trained to deal with walls or non-traversable terrain however it would be very easy to add that by adding additional training, which I encourage you to do after following this tutorial!

Don’t worry, yes I will offer some advice on what you might do to improve upon it.

😉

Pathfinding

Pathfinding, is the act of finding a path. A path can be defined as a collective series of steps you would take to travel between two or more points.

Just for now we will keep things simple so we will define our map in strict binary terms: 1 or 0.

If you are wondering why we are excluding -1, Great question! We’re going to ignore it for now but we will use it later.

If you are now wondering why we don’t use floating point numbers to create a more dynamic grid full of gradient step paths that allow you to pick the best path provided rather than relying on Pathfinder to give you a single path… well aren’t you just the smarty pants, this is a tutorial and i’m trying to keep it simple! 😛

We will use 0 to represent “open” or “traversable” spaces.

We will use 1 to denote “steps” in our path as well as the “start” and “end” positions.

Example input

pathfinder-example-1
Traversable spaces with a start and stop location.

It’s important to note that because both start and stop are 1, either space can be interpreted as the start and stop positions and we are saying in this case that start is the bottom left (green) cell and stop is the top right cell (red). This neural network is only concerned with finding a path that connects these two points. If you were to use a pathfinder neural network that worked like this you would have to keep track of the start and stop positions outside of the neural network in X & Y variables in your code somewhere.

In terms of 2D vector space (x,y), where x is the row and y is the column, the position of the start can be interpreted as (4, 0). The position of the end can be interpreted as (0, 4).

Based on these start and stop positions we want pathfinder to return the grid with the equivalent step path of these vectors:

(4,0), (3,1), (2,2), (1,3), (0, 4)

Example Output

pathfinder-example-2

 

5x5-vectorgrid-1

So now that we have looked at what we want Pathfinder to do, lets look at how we make it happen.

It’s actually really easy!

We prepare a grid for evaluation by the neural network simply by rearranging the rows into a single line.

Example:

colorgrid

colorline

 

Once we have rearranged the data as illustrated we are ready to pass it to the neural network for processing.

As you can see in the Pathfinder Neural Network illustration above, Pathfinder has 25 input neurons and our rearranged grid has 25 values, a perfect match!

The Code

So now we can start looking at code, we will cover more detail in my next Pathfinder post however you can use this code to get started.

Remember you will need to setup a test environment with the FANN library. I have a tutorial on that here: Getting started with Neural Networks

pathfinder.data

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0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0
0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0
0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0

pathfinder_train.php

<?php 
$num_input = 25; 
$num_output = 25; 
$num_layers = 3; 
$num_neurons_hidden = 70; 
$desired_error = 0.001; 
$max_epochs = 5000000; 
$epochs_between_reports = 1000; 
$pathfinder_ann = fann_create_standard($num_layers, $num_input, $num_neurons_hidden, $num_output); 

if ($pathfinder_ann) { 
    fann_set_activation_function_hidden($pathfinder_ann, FANN_SIGMOID_SYMMETRIC); 
    fann_set_activation_function_output($pathfinder_ann, FANN_SIGMOID_SYMMETRIC); 

    $filename = dirname(__FILE__) . "/pathfinder.data"; 
    if (fann_train_on_file($pathfinder_ann, $filename, $max_epochs, $epochs_between_reports, $desired_error)) 
        fann_save($pathfinder_ann, dirname(__FILE__) . "/pathfinder_float.net"); fann_destroy($pathfinder_ann); 
    
    echo "<h1 class='green'>Training Complete!</h1><a href='pathfinder_test.php'>Test Pathfinder</a>"; 
}
?>

pathfinder_test.php

<html>
<head>
<style>
 .blue{color:blue;}
 .red{color:red;}
</style>
</head>
<body>
<?php
function Pathfinder($ann, $array) {
     $calc_out = fann_run($ann, $array);
     echo "<h1 class='blue'>Testing Pathfinder:</h1>";
     // Display Input Map
     for ($i = 0; $i <= 24; $i++){
         if(abs(round($array[$i]) == 1)) { 
             echo "<span class='red'>" . abs(round($array[$i])) . "</span>"; 
         }else {
            echo abs(round($array[$i]));
         }
         if($i > 0 && ($i % 5) - 4 == 0){
             echo "<br>" . PHP_EOL;
         }
    }
    echo "<h1 class='blue'>Results:</h1>";
    // Display Output Map
    for ($i = 0; $i <= 24; $i++){
       if(abs(round($calc_out[$i]) == 1)) { 
           echo "<span class='red'>" . abs(round($calc_out[$i])) . "</span>";
       }else {
           echo abs(round($calc_out[$i]));
       }
       if($i > 0 && ($i % 5) - 4 == 0){
         echo "<br>" . PHP_EOL;
       }
    }
}
$train_file = (dirname(__FILE__) . "/pathfinder_float.net");
if (!is_file($train_file)) {
     die('<span class="red">The file pathfinder_float.net has not been created! Please run <a href="pathfinder_train.php">pathfinder_train.php</a> to generate it</span>' . PHP_EOL);
}
$pathfinder_ann = fann_create_from_file($train_file);
if ($pathfinder_ann) {
   Pathfinder($pathfinder_ann, array(0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0));
   Pathfinder($pathfinder_ann, array(0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0));
   Pathfinder($pathfinder_ann, array(0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1));
   Pathfinder($pathfinder_ann, array(0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1));
   Pathfinder($pathfinder_ann, array(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1));
   Pathfinder($pathfinder_ann, array(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0));
   Pathfinder($pathfinder_ann, array(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0));
   Pathfinder($pathfinder_ann, array(0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0));
   Pathfinder($pathfinder_ann, array(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0));
   Pathfinder($pathfinder_ann, array(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0));
   Pathfinder($pathfinder_ann, array(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0));
   Pathfinder($pathfinder_ann, array(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0));
   Pathfinder($pathfinder_ann, array(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0));
   Pathfinder($pathfinder_ann, array(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0));
   Pathfinder($pathfinder_ann, array(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1));
   Pathfinder($pathfinder_ann, array(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0));
   Pathfinder($pathfinder_ann, array(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0));
   Pathfinder($pathfinder_ann, array(0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0));
   Pathfinder($pathfinder_ann, array(0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0));
   Pathfinder($pathfinder_ann, array(0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0));
   Pathfinder($pathfinder_ann, array(0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0));
   Pathfinder($pathfinder_ann, array(0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0));
   Pathfinder($pathfinder_ann, array(0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0));
   Pathfinder($pathfinder_ann, array(0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1));
   Pathfinder($pathfinder_ann, array(0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0));
   Pathfinder($pathfinder_ann, array(0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0));
   Pathfinder($pathfinder_ann, array(0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0));
   Pathfinder($pathfinder_ann, array(0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0));
   Pathfinder($pathfinder_ann, array(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0));
   Pathfinder($pathfinder_ann, array(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0));
   Pathfinder($pathfinder_ann, array(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0));
   fann_destroy($pathfinder_ann);
} else {
     die('<span class="red">Unable to open Pathfinder.</span>' . PHP_EOL);
}
?>
</body>
</html>

If you run this code you will have a working pathfinding neural network under your control! 🙂

First run pathfinder_train.php then run pathfinder_test.php

In my next post in this series we will breakdown why this works and cover some ways we can make it even better!

As always, thanks for reading! If this post helps you or you found it enjoyable or just have some thoughts about all of this, please leave a comment below.

~Joy

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