# ssusnic/Machine-Learning-Flappy-Bird

Machine Learning for Flappy Bird using Neural Network and Genetic Algorithm
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# Machine Learning for Flappy Bird using Neural Network and Genetic Algorithm

Here is the source code for a HTML5 project that implements a machine learning algorithm in the Flappy Bird video game using neural networks and a genetic algorithm. The program teaches a little bird how to flap optimally in order to fly safely through barriers as long as possible.

The complete tutorial with much more details and demo you can find here:
http://www.askforgametask.com/tutorial/machine-learning-algorithm-flappy-bird

Here you can also watch a short video with a simple presentation of the algorithm:
https://www.youtube.com/watch?v=aeWmdojEJf0

All code is written in HTML5 using Phaser framework and Synaptic Neural Network library for neural network implementation.

## Neural Network Architecture

To play the game, each unit (bird) has its own neural network consisted of the next 3 layers:

1. an input layer with 2 neurons presenting what a bird sees:

``````1) horizontal distance between the bird and the closest gap
2) height difference between the bird and the closest gap
``````
2. a hidden layer with 6 neurons

3. an output layer with 1 neuron used to provide an action as follows:

``````if output > 0.5 then flap else do nothing
``````

There is used Synaptic Neural Network library to implement entire artificial neural network instead of making a new one from the scratch.

## The Main Concept of Machine Learning

The main concept of machine learning implemented in this program is based on the neuro-evolution form. It uses evolutionary algorithms such as a genetic algorithm to train artificial neural networks. Here are the main steps:

1. create a new population of 10 units (birds) with a random neural network

2. let all units play the game simultaneously by using their own neural networks

3. for each unit calculate its fitness function to measure its quality as:

``````fitness = total travelled distance - distance to the closest gap
``````

4. when all units are killed, evaluate the current population to the next one using genetic algorithm operators (selection, crossover and mutation) as follows:

``````1. sort the units of the current population in decreasing order by their fitness ranking
2. select the top 4 units and mark them as the winners of the current population
3. the 4 winners are directly passed on to the next population
4. to fill the rest of the next population, create 6 offsprings as follows:
- 1 offspring is made by a crossover of two best winners
- 3 offsprings are made by a crossover of two random winners
- 2 offsprings are direct copy of two random winners
5. to add some variations, apply random mutations on each offspring.
``````
5. go back to the step 2

## Implementation

### Requirements

Since the program is written in HTML5 using Phaser framework and Synaptic Neural Network library you need these files:

• phaser.min.js
• synaptic.min.js

### gameplay.js

The entire game logic is implemented in gameplay.js file. It consists of the following classes:

• `App.Main`, the main routine with the following essential functions:

• preload() to preload all assets
• create() to create all objects and initialize a new genetic algorithm object
• update() to run the main loop in which the Flappy Bird game is played by using AI neural networks and the population is evolved by using genetic algorithm
• drawStatus() to display information of all units
• `TreeGroup Class`, extended Phaser Group class to represent a moving barrier. This group contains a top and a bottom Tree sprite.

• `Tree Class`, extended Phaser Sprite class to represent a Tree sprite.

• `Bird Class`, extended Phaser Sprite class to represent a Bird sprite.

• `Text Class`, extended Phaser BitmapText class used for drawing text.

### genetic.js

The genetic algorithm is implemented in genetic.js file which consists of the following class:

• `GeneticAlgorithm Class`, the main class to handle all genetic algorithm operations. It needs two parameters: max_units to set a total number of units in population and top_units to set a number of top units (winners) used for evolving population. Here are its essential functions:

• reset() to reset genetic algorithm parameters
• createPopulation() to create a new population
• activateBrain() to activate the AI neural network of an unit and get its output action according to the inputs
• evolvePopulation() to evolve the population by using genetic operators (selection, crossover and mutations)
• selection() to select the best units from the current population
• crossOver() to perform a single point crossover between two parents
• mutation() to perform random mutations on an offspring