📦 Be sure to check out the NuGet pacakge: https://www.nuget.org/packages/NTDLS.Determinet
Determinet is versatile multilayer perception neural network designed for extendibility and genetic-style mutations to allow forward propagation of the network variants.
Below is a simple example of using the network to navigate a maze or other obstacles for a simple simulation. You can find more advanced examples as well as working models of this in the AIVolution project: https://github.com/NTDLS/AIVolution
If you are in a fighting mood, you can also battle it out against some of these trained models in the Space Flight Shooter Game: https://github.com/NTDLS/NebulaSiege
public enum AIInputs
{
DistanceFromObstacle,
AngleToObstacleInDecimalDegrees
}
public enum AIOutputs
{
MoveAway,
AdjustSpeed
}
static void Main()
{
TrainAndSaveModel("./TestHarness.json");
//Note that if you want to use the model in different threads, you will need to
// make a clone since the values of the input nodes are altered when making decisions.
// Fortunately, this can be easily accomplished with a call to Clone();
var network = LoadSavedModel("./TestHarness.json");
var decidingFactors = GatherInputs(); //Get decision inputs.
var decisions = network.FeedForward(decidingFactors); //Make decisions.
//Handle the speed decisions.
var shouldAdjustSpeed = decisions.Get(AIOutputs.AdjustSpeed);
if (shouldAdjustSpeed > 0.8)
{
//Adjust speed up.
}
else if (shouldAdjustSpeed < 0.2)
{
//Adjust speed down.
}
//Handle the heading direction.
var shouldMoveAway = decisions.Get(AIOutputs.MoveAway);
if (shouldMoveAway > 0.9)
{
//Change heading. Maybe just turn around completely?
}
}
/// <summary>
/// Get the input values we need to make a decision.
/// </summary>
/// <returns></returns>
static DniNamedInterfaceParameters GatherInputs()
{
//Here we are just using some dummy values, in this hypothetical situation
// we would be getting the distance from a wall and the angle to it.
double idealMaxDistance = 1000;
double distanceFromObstacle = 500;
double percentageOfCloseness = (distanceFromObstacle / idealMaxDistance);
double angleToObstacleInDecimalDegrees = 0.8;
var aiParams = new DniNamedInterfaceParameters();
aiParams.Set(AIInputs.DistanceFromObstacle, percentageOfCloseness);
aiParams.Set(AIInputs.AngleToObstacleInDecimalDegrees, angleToObstacleInDecimalDegrees);
return aiParams;
}
static DniNeuralNetwork LoadSavedModel(string fileName)
{
var network = DniNeuralNetwork.LoadFromFile(fileName);
if (network == null)
{
throw new Exception("Failed to load the network from file.");
}
return network;
}
static void TrainAndSaveModel(string fileName)
{
var Network = new DniNeuralNetwork
{
LearningRate = 0.01
};
//Add input layer
Network.Layers.AddInput(ActivationType.LeakyReLU,
new object[] {
AIInputs.DistanceFromObstacle,
AIInputs.AngleToObstacleInDecimalDegrees
});
//Add a intermediate "hidden" layer. You can add more if you like.
Network.Layers.AddIntermediate(ActivationType.Sigmoid, 8);
//Add the output layer.
Network.Layers.AddOutput(
new object[] {
AIOutputs.MoveAway,
AIOutputs.AdjustSpeed
});
//Train the model with some input scenarios. Look at TrainingScenerio() and TrainingDecision()
// to see that these ominous looking numbers are actualy just named inouts. Its pretty simple really.
for (int epoch = 0; epoch < 5000; epoch++)
{
//Very close to observed object, slow way down and get away
Network.BackPropagate(TrainingScenerio(0, 0), TrainingDecision(1, 0));
Network.BackPropagate(TrainingScenerio(0, -1), TrainingDecision(1, 0));
Network.BackPropagate(TrainingScenerio(0, 1), TrainingDecision(1, 0));
Network.BackPropagate(TrainingScenerio(0, 0.5), TrainingDecision(1, 0));
Network.BackPropagate(TrainingScenerio(0, -0.5), TrainingDecision(1, 0));
//Pretty close to observed object, slow down a bit and get away.
Network.BackPropagate(TrainingScenerio(0.25, 0), TrainingDecision(1, 0.2));
Network.BackPropagate(TrainingScenerio(0.25, -1), TrainingDecision(1, 0.2));
Network.BackPropagate(TrainingScenerio(0.25, 1), TrainingDecision(1, 0.2));
Network.BackPropagate(TrainingScenerio(0.25, 0.5), TrainingDecision(1, 0.2));
Network.BackPropagate(TrainingScenerio(0.25, -0.5), TrainingDecision(1, 0.2));
//Very far from observed object, speed up and maintain heading.
Network.BackPropagate(TrainingScenerio(1, 0), TrainingDecision(0, 1));
Network.BackPropagate(TrainingScenerio(1, -1), TrainingDecision(0, 1));
Network.BackPropagate(TrainingScenerio(1, 1), TrainingDecision(0, 1));
Network.BackPropagate(TrainingScenerio(1, 0.5), TrainingDecision(0, 1));
Network.BackPropagate(TrainingScenerio(1, -0.5), TrainingDecision(0, 1));
//Pretty far from observed object, maintain heading but don't change speed.
Network.BackPropagate(TrainingScenerio(0.75, 0), TrainingDecision(0, 0.5));
Network.BackPropagate(TrainingScenerio(0.75, -1), TrainingDecision(0, 0.5));
Network.BackPropagate(TrainingScenerio(0.75, 1), TrainingDecision(0, 0.5));
Network.BackPropagate(TrainingScenerio(0.75, 0.5), TrainingDecision(0, 0.5));
Network.BackPropagate(TrainingScenerio(0.75, -0.5), TrainingDecision(0, 0.5));
}
static DniNamedInterfaceParameters TrainingScenerio(double distanceFromObstacle, double angleToObstacleInDecimalDegrees)
{
var param = new DniNamedInterfaceParameters();
param.Set(AIInputs.DistanceFromObstacle, distanceFromObstacle);
param.Set(AIInputs.AngleToObstacleInDecimalDegrees, angleToObstacleInDecimalDegrees);
return param;
}
static DniNamedInterfaceParameters TrainingDecision(double moveAway, double adjustSpeed)
{
var param = new DniNamedInterfaceParameters();
param.Set(AIOutputs.MoveAway, moveAway);
param.Set(AIOutputs.AdjustSpeed, adjustSpeed);
return param;
}
//Save the network to a file. This is only done here for examples sake.
Network.Save(fileName);
}