This is a simple asset that train a neural networks using genetic algorithm in unity to make a bot that can play a game or just interact with the envoriment
First of anything I will explain the concept of Genetic algorithm: This is a algorithm based on the process of natural selection that works crossovering certains individuals and mutate them with the goal of get a more ideal individuals for a work.
Now the neural network is just a matematical function that can imitate or aproximate to any other matematical function, is the universal approximator. One of this function can be the "Being the most enjoyable final boss" or "Being the most human like chatbot" or "Being the most difficult enemy", so usually the neural network is training using the backpropagation algorithm. But this is a pretty complex algorithm to do, and it does not works when you don't really know the function that you want
Here the Genetic algorithm can help us, just crossover a lot of neural networks and choose the most adapted one
I wanted to sell this asset on the store, but Unity ML attacked and I couldn't, so I release it, It's a pretty good and simple experiment, to learn neural networks without backpropagation.
So this was made for my youtube, and twitch channels
Youtube channel
https://www.youtube.com/c/HectorAndresPulidoPalmar
Twitch Channel
https://www.twitch.tv/hector_pulido_
- More examples
- Cleaner interface to set the fitness
Open it on unity 2018 or greater (sorry about that >-< ), is also recomended to set the scripting runtime version in .Net 4.X, you can make your own envoriment and set the fitness function, but I recomend, to look at the examples.
You need two things a mendel machine (a trainer if you like the concept), to use it you need to iherit from the class mendel machine and set up, things like the startpoints or the behaviour when the generation is over.
using EvolutionaryPerceptron.MendelMachine;
public class ExampleMendelMachine : MendelMachine {
int index = 0; //Just one way to change the generation
//Init all variables
protected override void Start()
{
individualsPerGeneration = somenumber; //You can set an individuals per generation here
base.Start();
StartCoroutine(InstantiateBotCoroutine());
}
//When a bot die
public override void NeuralBotDestroyed(Brain neuralBot)
{
//Consolidate the fitness
base.NeuralBotDestroyed(neuralBot);
//Doo some cool stuff, read the examples
Destroy(neuralBot.gameObject); //Don't forget to destroy the gameObject
index--;
if (index <= 0)
{
Save(); //don't forget to save when you change the generation
population = Mendelization();
generation++;
StartCoroutine(InstantiateBotCoroutine());
}
}
//You can instantiate one, two, what you want
IEnumerator InstantiateBotCoroutine()
{
//Instantiate bots
index = individualsPerGeneration;
for (int i = 0 ; i < individualsPerGeneration ; i++)
{
var b = InstantiateBot(population[i], lifeTime, someTransform, i); // A way to instantiate
}
}
}And you also need a interpreter of the neural bot class this class will act like a Senses and Actuators from the body (the individual), I recomend that the sensors where raycast (o raycast2D) or any lineal information, this class also can change the fitness of the neuralbot.
public class NeuralExample : BotHandler
{
MyControllerClass cs;
//Init all variables
protected override void Start()
{
base.Start();
cs = GetComponent<MyControllerClass>();
}
void Update()
{
var i = new double[1, 5] { { n1, n2, n3, n4, n5 } }; // Sensor info
var output = nb.SetInput(i); //Feed forward
cs.speed = output[0, 0]; // Linear something
if (output[0, 1] > 0.5) // Trigger something
{
cs.jumpRequest = true;
}
nb.AddFitness(Time.deltaTime); // You can reward the lifetime
}
private void OnTriggerEnter2D(Collider2D collision)
{
//Example of destroy
if (collision.CompareTag("Obstacle"))
{
nb.Destroy();
}
}
}Now you can select the bot you want to save, and press the save button, that will generate a .nn file, that file is compatible with IMITATION LEARNING, just desactivate the Learning Phase boolean
In this moments there are 4 examples
An automatic Car in unity, it can be trained on just 10 generations, the sensors are Raycast and the actuator is the Unity Standard Asset Vehicle Car
The fitnes function is, how many checkpoints it touch and the Die function is the collision with the tag "Obstacle"
This is a flappy bird game bot made with this library, the sensors are 3 raycast, the position, and the center of the obstacles
This is a google chrome clone, it have 7 raycast as sensor aditional to the 7 raycast of the last frame, and 2 buttons as outputs also the fitness function is the lifetime
The fitness function is the time, and the Die function is the collision with the tag "Obstacle"
Assets from
This is an implementation of the algorithm for the Asteroid game; The sensors are a lot of raycast that detect asteroids, and the die function is when ship collide with an asteroid, and there are 3 output, one for shoot, one for turn, other for accelerate
Lifetime without shoot is a positive reward
Shooting time is a negative reward
https://github.com/HectorPulido/Asteroids-like-game
Assets Licence: MIT
This is an implementation of the algorithm in the Survival shooter project from Unity Tec.
The sensors are a lot of raycast that detect shootables, and enemies, and the die function is when the life gets 0
A useful shoot give 2 points
A fail shoot give -5 points
When the bot move a lot give 1 point
When health is lost give -1 points
https://github.com/HectorPulido/Evolutionary-Neural-Bots-On-Survival-Shooter
Assets Licence: Apache 2.0
This is an implementation for the pong game, the sensors are the position and velocity of the ball, the the position and the velocity of the enemy racket and the position of the racket (All the position must be locals)
Pro point add one of fitness point
Contra point remove one of fitness point
This is an open source project that uses neural networks and backpropagation in C#, and train it via stochastic gradient descend using the human behaviour as base
https://github.com/HectorPulido/Imitation-learning-in-unity
Those are three Genetics Algorithm using unity, The First one is a simple algorithm that Looks for the minimun of a function, The Second one is a solution for the Travelling Salesman Problem, The Third one is a Automata machine
https://github.com/HectorPulido/Three-Genetics-Algorithm-Using-Unity
This is a simple MultiLayer perceptron made with Simple Linear Algebra for C# , is a neural network based on This Algorithm but generalized. This neural network can calcule logic doors like Xor Xnor And Or via Stochastic gradient descent backpropagation with Sigmoid as Activation function, but can be used to more complex problems.
https://github.com/HectorPulido/Vectorized-multilayer-neural-network
- On my Youtube channel (spanish) are a lot of information about Machine learning and Neural networks
- https://www.youtube.com/channel/UCS_iMeH0P0nsIDPvBaJckOw
- This convolutional neural network (Pretty hardcore)
- https://github.com/HectorPulido/Convolutional-Neural-Network-From-Scratch
- This example of Genetics Algorithm on unity
- https://github.com/HectorPulido/Three-Genetics-Algorithm-Using-Unity
- You can also look at the Multilayer perceptron
- https://github.com/HectorPulido/Vectorized-multilayer-neural-network
- Or The monolayer Example (Simpler)
- https://github.com/HectorPulido/Simple-vectorized-mono-layer-perceptron
- Or Look at a Non Vectorized multilayer perceptronExample
- https://github.com/HectorPulido/Multi-layer-perceptron
