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flappylearning's Introduction

Flappy Learning (Demo)

Program that learns to play Flappy Bird by machine learning (Neuroevolution)

alt tag

NeuroEvolution.js : Utilization

// Initialize
var ne = new Neuroevolution({options});

//Default options values
var options = {
    network:[1, [1], 1],    // Perceptron structure
    population:50,          // Population by generation
    elitism:0.2,            // Best networks kepts unchanged for the next generation (rate)
    randomBehaviour:0.2,    // New random networks for the next generation (rate)
    mutationRate:0.1,       // Mutation rate on the weights of synapses
    mutationRange:0.5,      // Interval of the mutation changes on the synapse weight
    historic:0,             // Latest generations saved
    lowHistoric:false,      // Only save score (not the network)
    scoreSort:-1,           // Sort order (-1 = desc, 1 = asc)
    nbChild:1               // number of child by breeding
}

//Update options at any time
ne.set({options});

// Generate first or next generation
var generation = ne.nextGeneration();

//When an network is over -> save this score
ne.networkScore(generation[x], <score = 0>);

You can see the NeuroEvolution integration in Flappy Bird in Game.js.

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francium avatar xviniette avatar

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flappylearning's Issues

New layout

Working on the new layout, any suggestions?

The idea is to have the controls panel on the right side, with some other options (such as kill current generation and others described on issues) and on top have the game info resumed. Ideas are welcome.

flappy

may be it's variable 'i'?

Neuroevolution.js>line 203:
var n = JSON.parse(JSON.stringify(this.genomes[0].network));
may be this?
var n = JSON.parse(JSON.stringify(this.genomes[i].network));
@xviniette

Work offline

The page should load and work without many problems when offline.

Deciding factor

What is the deciding factor for selecting the best breed? Is it the amount of flaps or maxscore?

If you would add the difference in height of all the pipes as deciding factor, I think it would select better breeds faster ( not sure)

If you change this, can you add the commit here please? I'm an noob in machine learning, but this has got me excited

How it works

I know you linked to the article regarding the algorithm but it would be useful to have a walk-through (high-level) of what the Neuroevolution is doing in the case of this game.

For example, talking about what the inputs are (location on screen?) and what the outputs are (go up, or down).

Thank you, great example.

Neuroevolution.js is badly indented.

The formatting seem to have messed up during the pull request/merge for #22. Lines are not correctly indented.

It appears fine on my computer. I can look into it in the next few days.

Another AI flappy bird using genetic programming (evolutionary computation)

Appreciate this excellent work. I got a lot of inspiration from this work.

I have achieved to train an AI for a more difficult version flappy bird: the horizontal distance between adjacent pipes and the gap between up and down pipes are random within a certain range rather than being fixed. Instead of neural networks,I use evolutionary strategies and Cartesian genetic programming, which attempts to build the control function (a math expression) directly using only basic arithmetic operators. With a small population of size 10, the bird can learn to fly quite well in typically less than 50 generations, which seems to be much more efficient than simple neuron evolution.

I implement this algorithm with Python and pygame. For those who are interested, please check my GitHub repository. A demo is here.

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