Hi, I think I’m very close to actually succeeding here but there’s still one thing I can’t wrap my head around. I am now trying to use your method of training a generation simultaneously, but there are sometimes errors with the sorting of the scores.
This is the code I’m using:
local PhysicsService = game:GetService("PhysicsService")
local Package = game:GetService("ReplicatedStorage").NNLibrary
local FeedforwardNetwork = require(Package.NeuralNetwork.FeedforwardNetwork)
local ParamEvo = require(Package.GeneticAlgorithm.ParamEvo)
local clock = os.clock()
local collisionGroupName = "CarCollisionsDisabled"
-- Setup CollisionGroup for cars
PhysicsService:CreateCollisionGroup(collisionGroupName)
PhysicsService:CollisionGroupSetCollidable(collisionGroupName, collisionGroupName, false)
-- Function to activate scripts inside of car
local function setupCar(car)
car.RemoteControl.Torque = 1000
car.RemoteControl.Throttle = 5
car.RemoteControl.TurnSpeed = 30
for _, item in pairs(car:GetDescendants()) do
if item:IsA("BasePart") or item:IsA("UnionOperation") then
PhysicsService:SetPartCollisionGroup(item, collisionGroupName)
end
end
-- Create ray visualizers
local ray = Instance.new("Part")
ray.CanCollide = false
ray.Massless = true
ray.CFrame = car.RaycastPart.CFrame
ray.Size = Vector3.new(1, 1, 1)
ray.Color = Color3.fromRGB(255, 0, 0)
ray.Name = "frontRay"
ray.Parent = car
local weld = Instance.new("WeldConstraint")
weld.Part0 = car.RaycastPart
weld.Part1 = ray
weld.Parent = ray
end
-- Setup car
local carSource = game:GetService("ServerStorage").Car
setupCar(carSource)
-- Function that casts rays from the car in five directions and returns the distances in a table
local function getRayDistances(car)
-- Setup RayCastParams
local rayCastParams = RaycastParams.new()
rayCastParams.IgnoreWater = true
rayCastParams.FilterType = Enum.RaycastFilterType.Whitelist
rayCastParams.FilterDescendantsInstances = {workspace.Walls}
local bumper = car.RaycastPart
local bumperPos = bumper.Position
local dist = 250
local FrontRay = workspace:Raycast(bumperPos, bumper.CFrame.RightVector * dist, rayCastParams) -- Cast ray for front
local FrontLeftRay = workspace:Raycast(bumperPos, Vector3.new(bumper.CFrame.RightVector.X, 0, bumper.CFrame.LookVector.Z) * dist, rayCastParams) -- Cast ray to frontLeft
local FrontRightRay = workspace:Raycast(bumperPos, Vector3.new(bumper.CFrame.RightVector.X, 0, -bumper.CFrame.LookVector.Z) * dist, rayCastParams) -- Cast ray to frontRight
local LeftRay = workspace:Raycast(bumperPos, bumper.CFrame.LookVector * dist, rayCastParams) -- Cast ray to left
local RightRay = workspace:Raycast(bumperPos, -bumper.CFrame.LookVector * dist, rayCastParams) -- Cast ray to right
local pos1 = 1
local pos2 = 1
local pos3 = 1
local pos4 = 1
local pos5 = 1
if FrontRay then
pos1 = ((FrontRay.Position - bumperPos).Magnitude) / dist
end
if FrontLeftRay then
pos2 = ((FrontLeftRay.Position - bumperPos).Magnitude) / dist
end
if FrontRightRay then
pos3 = ((FrontRightRay.Position - bumperPos).Magnitude) / dist
end
if LeftRay then
pos4 = ((LeftRay.Position - bumperPos).Magnitude) / dist
end
if RightRay then
pos5 = ((RightRay.Position - bumperPos).Magnitude) / dist
end
--local calc = pos1*dist
--local ray = car.frontRay
--ray.Size = Vector3.new(calc, 1, 1)
--ray.Position = Vector3.new(bumperPos.X + calc/2, ray.Position.Y, ray.Position.Z)
return {front = pos1, frontLeft = pos2, frontRight = pos3, left = pos4, right = pos5}
end
-- Settings for genetic algorithm
local geneticSetting = {
--[[ The function that, when given the network, will return it's score.
ScoreFunction = function(net)
local startTime = os.clock()
local clone = game:GetService("ServerStorage").Car:Clone()
clone.RemoteControl.MaxSpeed = 200
-- Parent to workspace and then setup Scripts of car
clone.Parent = workspace
local score = 0
local bool = true
local checkpointsHit = {}
for _, v in pairs(clone:GetDescendants()) do
if v:IsA("BasePart") and v.CanCollide == true then
v.Touched:Connect(function(hit)
if hit.Parent.Parent == workspace.Walls then -- Destroy car on hit of walls
bool = false
elseif hit.Parent == workspace.Checkpoints and not checkpointsHit[tonumber(hit.Name)] then -- Give extra points when car reaches checkpoint
local numHit = tonumber(hit.Name)
score += (numHit * 2)
checkpointsHit[numHit] = hit
end
end)
end
end
while bool do
local distances = getRayDistances(clone) -- Get Distances of rays
local output = net(distances) -- Get output of NN with input distances
-- Set steering direction to direction of NN
clone.RemoteControl.SteerFloat = output.steerDirection
-- Set speed of car
--clone.RemoteControl.MaxSpeed = math.abs(output.speed) * 300
-- Check if this simulation has been running for longer than x seconds
if os.clock() > startTime + 90 then
score -= 40 -- Punish algorithm
break
end
wait()
end
score += (os.clock() - startTime)/2 -- Increment score based on time alive (longer is better)
print("Exit score: "..math.floor(score*100)/100)
clone:Destroy()
return score
end;]]
-- The function that runs when a generation is complete. It is given the genetic algorithm as input.
PostFunction = function(geneticAlgo)
local info = geneticAlgo:GetInfo()
print("Generation " .. info.Generation .. ", Best Score: " .. info.BestScore)
end;
HigherScoreBetter = true;
PercentageToKill = 0.4;
PercentageOfKilledToRandomlySpare = 0.1;
PercentageOfBestParentToCrossover = 0.8;
PercentageToMutate = 0.8;
MutateBestNetwork = true;
PercentageOfCrossedToMutate = 0.6;
--NumberOfNodesToMutate = 3;
--ParameterMutateRange = 3;
}
local feedForwardSettings = {
HiddenActivationName = "ReLU";
OutputActivationName = "Tanh";
--Bias = 0;
--LearningRate = 0.1;
--RandomizeWeights = true;
}
-- Create a new network with 5 inputs, 2 layers with 4 nodes each and 1 output "steerDirection"
local tempNet = FeedforwardNetwork.new({"front", "frontLeft", "frontRight", "left", "right"}, 2, 4, {"steerDirection"}, feedForwardSettings)
--local tempNet = FeedforwardNetwork.newFromSave(game.ServerStorage.NetworkSave.Value)
local populationSize = 20
local geneticAlgo = ParamEvo.new(tempNet, populationSize, geneticSetting) -- Create ParamEvo with the tempNet template, population size and settings
local function roundDecimals(num, places)
places = math.pow(10, places or 0)
num = num * places
if num >= 0 then
num = math.floor(num + 0.5)
else
num = math.ceil(num - 0.5)
end
return num / places
end
local scoreTable = {}
local generations = 30 -- Number of generations to train network with
for _ = 1, generations do
for index = 1, populationSize do
local newThread = coroutine.create(function()
local startTime = os.clock()
local clone = game:GetService("ServerStorage").Car:Clone()
clone.RemoteControl.MaxSpeed = 200
-- Parent to workspace and then setup Scripts of car
clone.Parent = workspace
local score = 0
local bool = true
local checkpointsHit = {}
for _, v in pairs(clone:GetDescendants()) do
if v:IsA("BasePart") and v.CanCollide == true then
v.Touched:Connect(function(hit)
if hit.Parent.Parent == workspace.Walls then -- Destroy car on hit of walls
bool = false
elseif hit.Parent == workspace.Checkpoints and not checkpointsHit[tonumber(hit.Name)] then -- Give extra points when car reaches checkpoint
local numHit = tonumber(hit.Name)
if numHit and typeof(numHit) == "number" then
score += (numHit * 2)
checkpointsHit[numHit] = hit
end
end
end)
end
end
-- Setup Algorithm
local net = geneticAlgo:GetPopulation()[index].Network
while bool do
local distances = getRayDistances(clone) -- Get Distances of rays
local output = net(distances) -- Get output of NN with input distances
-- Set steering direction to direction of NN
clone.RemoteControl.SteerFloat = output.steerDirection
-- Set speed of car
--clone.RemoteControl.MaxSpeed = math.abs(output.speed) * 300
-- Check if this simulation has been running for longer than x seconds
if os.clock() > startTime + 90 then
score -= 40 -- Punish algorithm
break
end
wait()
end
clone:Destroy()
score = score + (os.clock() - startTime)/2 -- Increment score based on time alive (longer is better)
--print("Exit score: " .. score)
scoreTable[index] = roundDecimals(score, 2)
end)
coroutine.resume(newThread)
wait(0.5)
end
-- Wait until generation finished
while #scoreTable < populationSize do
wait(1)
end
print(scoreTable)
geneticAlgo:ProcessGeneration(scoreTable)
scoreTable = {}
print(scoreTable)
end
local save = geneticAlgo:GetBestNetwork():Save()
game.ServerStorage.NetworkSave.Value = save
print(save)
Here is the place file: Neural Network Test.rbxl (1.0 MB)
This is the error I’m getting:
ReplicatedStorage.NNLibrary.GeneticAlgorithm:195: attempt to compare nil and number - Server - GeneticAlgorithm:195
I think there is something wrong with my scoring system, but I’m not sure what. I tried debugging and found out that the ScoreTable sometimes returns “void” values? I don’t know how this is happening. Thanks.
) But, thanks for put in much effort just to make our developpement easier, it‘s great tho have members in the community like you
