Machine learning 在Mari/O中保存神经网络池
所以我试着用这个人工智能来训练和清理超级马里奥世界。 申请，一切都很顺利，但我在保存训练结果时遇到了麻烦——游泳池 我得到了错误Machine learning 在Mari/O中保存神经网络池,machine-learning,neural-network,save,pool,neat,Machine Learning,Neural Network,Save,Pool,Neat,所以我试着用这个人工智能来训练和清理超级马里奥世界。 申请，一切都很顺利，但我在保存训练结果时遇到了麻烦——游泳池 我得到了错误 Loading pool from C:\Download\biz\Lua\SNES\neat-mario\pool\DP1.state.pool NLua.Exceptions.LuaScriptException: [string "main"]:923: 'for' limit must be a number 这似乎是因为在保存numGe
Loading pool from C:\Download\biz\Lua\SNES\neat-mario\pool\DP1.state.pool
NLua.Exceptions.LuaScriptException: [string "main"]:923: 'for' limit must be a number
config = require "config"
spritelist = require "spritelist"
game = require "game"
mathFunctions = require "mathFunctions"
Inputs = config.InputSize+1
Outputs = #config.ButtonNames
function newInnovation()
pool.innovation = pool.innovation + 1
return pool.innovation
end
function newPool()
local pool = {}
pool.species = {}
pool.generation = 0
pool.innovation = Outputs
pool.currentSpecies = 1
pool.currentGenome = 1
pool.currentFrame = 0
pool.maxFitness = 0
return pool
end
function newSpecies()
local species = {}
species.topFitness = 0
species.staleness = 0
species.genomes = {}
species.averageFitness = 0
return species
end
function newGenome()
local genome = {}
genome.genes = {}
genome.fitness = 0
genome.adjustedFitness = 0
genome.network = {}
genome.maxneuron = 0
genome.globalRank = 0
genome.mutationRates = {}
genome.mutationRates["connections"] = config.NeatConfig.MutateConnectionsChance
genome.mutationRates["link"] = config.NeatConfig.LinkMutationChance
genome.mutationRates["bias"] = config.NeatConfig.BiasMutationChance
genome.mutationRates["node"] = config.NeatConfig.NodeMutationChance
genome.mutationRates["enable"] = config.NeatConfig.EnableMutationChance
genome.mutationRates["disable"] = config.NeatConfig.DisableMutationChance
genome.mutationRates["step"] = config.NeatConfig.StepSize
return genome
end
function copyGenome(genome)
local genome2 = newGenome()
for g=1,#genome.genes do
table.insert(genome2.genes, copyGene(genome.genes[g]))
end
genome2.maxneuron = genome.maxneuron
genome2.mutationRates["connections"] = genome.mutationRates["connections"]
genome2.mutationRates["link"] = genome.mutationRates["link"]
genome2.mutationRates["bias"] = genome.mutationRates["bias"]
genome2.mutationRates["node"] = genome.mutationRates["node"]
genome2.mutationRates["enable"] = genome.mutationRates["enable"]
genome2.mutationRates["disable"] = genome.mutationRates["disable"]
return genome2
end
function basicGenome()
local genome = newGenome()
local innovation = 1
genome.maxneuron = Inputs
mutate(genome)
return genome
end
function newGene()
local gene = {}
gene.into = 0
gene.out = 0
gene.weight = 0.0
gene.enabled = true
gene.innovation = 0
return gene
end
function newNeuron()
local neuron = {}
neuron.incoming = {}
neuron.value = 0.0
--neuron.dw = 1
return neuron
end
function generateNetwork(genome)
local network = {}
network.neurons = {}
for i=1,Inputs do
network.neurons[i] = newNeuron()
end
for o=1,Outputs do
network.neurons[config.NeatConfig.MaxNodes+o] = newNeuron()
end
table.sort(genome.genes, function (a,b)
return (a.out < b.out)
end)
for i=1,#genome.genes do
local gene = genome.genes[i]
if gene.enabled then
if network.neurons[gene.out] == nil then
network.neurons[gene.out] = newNeuron()
end
local neuron = network.neurons[gene.out]
table.insert(neuron.incoming, gene)
if network.neurons[gene.into] == nil then
network.neurons[gene.into] = newNeuron()
end
end
end
genome.network = network
end
function evaluateNetwork(network, inputs, inputDeltas)
table.insert(inputs, 1)
table.insert(inputDeltas,99)
if #inputs ~= Inputs then
console.writeline("Incorrect number of neural network inputs.")
return {}
end
for i=1,Inputs do
network.neurons[i].value = inputs[i] * inputDeltas[i]
--network.neurons[i].value = inputs[i]
end
for _,neuron in pairs(network.neurons) do
local sum = 0
for j = 1,#neuron.incoming do
local incoming = neuron.incoming[j]
local other = network.neurons[incoming.into]
sum = sum + incoming.weight * other.value
end
if #neuron.incoming > 0 then
neuron.value = mathFunctions.sigmoid(sum)
end
end
local outputs = {}
for o=1,Outputs do
local button = "P1 " .. config.ButtonNames[o]
if network.neurons[config.NeatConfig.MaxNodes+o].value > 0 then
outputs[button] = true
else
outputs[button] = false
end
end
return outputs
end
local count = 0
for _,_ in pairs(neurons) do
count = count + 1
end
local n = math.random(1, count)
for k,v in pairs(neurons) do
n = n-1
if n == 0 then
return k
end
end
return 0
end
function containsLink(genes, link)
for i=1,#genes do
local gene = genes[i]
if gene.into == link.into and gene.out == link.out then
return true
end
end
end
function pointMutate(genome)
local step = genome.mutationRates["step"]
for i=1,#genome.genes do
local gene = genome.genes[i]
if math.random() < config.NeatConfig.PerturbChance then
gene.weight = gene.weight + math.random() * step*2 - step
else
gene.weight = math.random()*4-2
end
end
end
function linkMutate(genome, forceBias)
local neuron1 = randomNeuron(genome.genes, false)
local neuron2 = randomNeuron(genome.genes, true)
local newLink = newGene()
if neuron1 <= Inputs and neuron2 <= Inputs then
--Both input nodes
return
end
if neuron2 <= Inputs then
-- Swap output and input
local temp = neuron1
neuron1 = neuron2
neuron2 = temp
end
newLink.into = neuron1
newLink.out = neuron2
if forceBias then
newLink.into = Inputs
end
if containsLink(genome.genes, newLink) then
return
end
newLink.innovation = newInnovation()
newLink.weight = math.random()*4-2
table.insert(genome.genes, newLink)
end
function nodeMutate(genome)
if #genome.genes == 0 then
return
end
genome.maxneuron = genome.maxneuron + 1
local gene = genome.genes[math.random(1,#genome.genes)]
if not gene.enabled then
return
end
gene.enabled = false
local gene1 = copyGene(gene)
gene1.out = genome.maxneuron
gene1.weight = 1.0
gene1.innovation = newInnovation()
gene1.enabled = true
table.insert(genome.genes, gene1)
local gene2 = copyGene(gene)
gene2.into = genome.maxneuron
gene2.innovation = newInnovation()
gene2.enabled = true
table.insert(genome.genes, gene2)
end
function enableDisableMutate(genome, enable)
local candidates = {}
for _,gene in pairs(genome.genes) do
if gene.enabled == not enable then
table.insert(candidates, gene)
end
end
if #candidates == 0 then
return
end
local gene = candidates[math.random(1,#candidates)]
gene.enabled = not gene.enabled
end
function mutate(genome)
for mutation,rate in pairs(genome.mutationRates) do
if math.random(1,2) == 1 then
genome.mutationRates[mutation] = 0.95*rate
else
genome.mutationRates[mutation] = 1.05263*rate
end
end
if math.random() < genome.mutationRates["connections"] then
pointMutate(genome)
end
local p = genome.mutationRates["link"]
while p > 0 do
if math.random() < p then
linkMutate(genome, false)
end
p = p - 1
end
p = genome.mutationRates["bias"]
while p > 0 do
if math.random() < p then
linkMutate(genome, true)
end
p = p - 1
end
p = genome.mutationRates["node"]
while p > 0 do
if math.random() < p then
nodeMutate(genome)
end
p = p - 1
end
p = genome.mutationRates["enable"]
while p > 0 do
if math.random() < p then
enableDisableMutate(genome, true)
end
p = p - 1
end
p = genome.mutationRates["disable"]
while p > 0 do
if math.random() < p then
enableDisableMutate(genome, false)
end
p = p - 1
end
end
function disjoint(genes1, genes2)
local i1 = {}
for i = 1,#genes1 do
local gene = genes1[i]
i1[gene.innovation] = true
end
local i2 = {}
for i = 1,#genes2 do
local gene = genes2[i]
i2[gene.innovation] = true
end
local disjointGenes = 0
for i = 1,#genes1 do
local gene = genes1[i]
if not i2[gene.innovation] then
disjointGenes = disjointGenes+1
end
end
for i = 1,#genes2 do
local gene = genes2[i]
if not i1[gene.innovation] then
disjointGenes = disjointGenes+1
end
end
local n = math.max(#genes1, #genes2)
return disjointGenes / n
end
function weights(genes1, genes2)
local i2 = {}
for i = 1,#genes2 do
local gene = genes2[i]
i2[gene.innovation] = gene
end
local sum = 0
local coincident = 0
for i = 1,#genes1 do
local gene = genes1[i]
if i2[gene.innovation] ~= nil then
local gene2 = i2[gene.innovation]
sum = sum + math.abs(gene.weight - gene2.weight)
coincident = coincident + 1
end
end
return sum / coincident
end
function sameSpecies(genome1, genome2)
local dd = config.NeatConfig.DeltaDisjoint*disjoint(genome1.genes, genome2.genes)
local dw = config.NeatConfig.DeltaWeights*weights(genome1.genes, genome2.genes)
return dd + dw < config.NeatConfig.DeltaThreshold
end
function rankGlobally()
local global = {}
for s = 1,#pool.species do
local species = pool.species[s]
for g = 1,#species.genomes do
table.insert(global, species.genomes[g])
end
end
table.sort(global, function (a,b)
return (a.fitness < b.fitness)
end)
for g=1,#global do
global[g].globalRank = g
end
end
function calculateAverageFitness(species)
local total = 0
for g=1,#species.genomes do
local genome = species.genomes[g]
total = total + genome.globalRank
end
species.averageFitness = total / #species.genomes
end
function totalAverageFitness()
local total = 0
for s = 1,#pool.species do
local species = pool.species[s]
total = total + species.averageFitness
end
return total
end
function cullSpecies(cutToOne)
for s = 1,#pool.species do
local species = pool.species[s]
table.sort(species.genomes, function (a,b)
return (a.fitness > b.fitness)
end)
local remaining = math.ceil(#species.genomes/2)
if cutToOne then
remaining = 1
end
while #species.genomes > remaining do
table.remove(species.genomes)
end
end
end
function breedChild(species)
local child = {}
if math.random() < config.NeatConfig.CrossoverChance then
g1 = species.genomes[math.random(1, #species.genomes)]
g2 = species.genomes[math.random(1, #species.genomes)]
child = crossover(g1, g2)
else
g = species.genomes[math.random(1, #species.genomes)]
child = copyGenome(g)
end
mutate(child)
return child
end
function removeStaleSpecies()
local survived = {}
for s = 1,#pool.species do
local species = pool.species[s]
table.sort(species.genomes, function (a,b)
return (a.fitness > b.fitness)
end)
if species.genomes[1].fitness > species.topFitness then
species.topFitness = species.genomes[1].fitness
species.staleness = 0
else
species.staleness = species.staleness + 1
end
if species.staleness < config.NeatConfig.StaleSpecies or species.topFitness >= pool.maxFitness then
table.insert(survived, species)
end
end
pool.species = survived
end
function removeWeakSpecies()
local survived = {}
local sum = totalAverageFitness()
for s = 1,#pool.species do
local species = pool.species[s]
breed = math.floor(species.averageFitness / sum * config.NeatConfig.Population)
if breed >= 1 then
table.insert(survived, species)
end
end
pool.species = survived
end
function addToSpecies(child)
local foundSpecies = false
for s=1,#pool.species do
local species = pool.species[s]
if not foundSpecies and sameSpecies(child, species.genomes[1]) then
table.insert(species.genomes, child)
foundSpecies = true
end
end
if not foundSpecies then
local childSpecies = newSpecies()
table.insert(childSpecies.genomes, child)
table.insert(pool.species, childSpecies)
end
end
function newGeneration()
cullSpecies(false) -- Cull the bottom half of each species
rankGlobally()
removeStaleSpecies()
rankGlobally()
for s = 1,#pool.species do
local species = pool.species[s]
calculateAverageFitness(species)
end
removeWeakSpecies()
local sum = totalAverageFitness()
local children = {}
for s = 1,#pool.species do
local species = pool.species[s]
breed = math.floor(species.averageFitness / sum * config.NeatConfig.Population) - 1
for i=1,breed do
table.insert(children, breedChild(species))
end
end
cullSpecies(true) -- Cull all but the top member of each species
while #children + #pool.species < config.NeatConfig.Population do
local species = pool.species[math.random(1, #pool.species)]
table.insert(children, breedChild(species))
end
for c=1,#children do
local child = children[c]
addToSpecies(child)
end
pool.generation = pool.generation + 1
--writeFile("backup." .. pool.generation .. "." .. forms.gettext(saveLoadFile))
writeFile(forms.gettext(saveLoadFile) .. ".gen" .. pool.generation .. ".pool")
end
function initializePool()
pool = newPool()
for i=1,config.NeatConfig.Population do
basic = basicGenome()
addToSpecies(basic)
end
initializeRun()
end
function initializeRun()
savestate.load(config.NeatConfig.Filename);
if config.StartPowerup ~= NIL then
game.writePowerup(config.StartPowerup)
end
rightmost = 0
pool.currentFrame = 0
timeout = config.NeatConfig.TimeoutConstant
game.clearJoypad()
startCoins = game.getCoins()
startScore = game.getScore()
startLives = game.getLives()
checkMarioCollision = true
marioHitCounter = 0
powerUpCounter = 0
powerUpBefore = game.getPowerup()
local species = pool.species[pool.currentSpecies]
local genome = species.genomes[pool.currentGenome]
generateNetwork(genome)
evaluateCurrent()
end
function evaluateCurrent()
local species = pool.species[pool.currentSpecies]
local genome = species.genomes[pool.currentGenome]
local inputDeltas = {}
inputs, inputDeltas = game.getInputs()
controller = evaluateNetwork(genome.network, inputs, inputDeltas)
if controller["P1 Left"] and controller["P1 Right"] then
controller["P1 Left"] = false
controller["P1 Right"] = false
end
if controller["P1 Up"] and controller["P1 Down"] then
controller["P1 Up"] = false
controller["P1 Down"] = false
end
joypad.set(controller)
end
if pool == nil then
initializePool()
end
function nextGenome()
pool.currentGenome = pool.currentGenome + 1
if pool.currentGenome > #pool.species[pool.currentSpecies].genomes then
pool.currentGenome = 1
pool.currentSpecies = pool.currentSpecies+1
if pool.currentSpecies > #pool.species then
newGeneration()
pool.currentSpecies = 1
end
end
end
function fitnessAlreadyMeasured()
local species = pool.species[pool.currentSpecies]
local genome = species.genomes[pool.currentGenome]
return genome.fitness ~= 0
end
form = forms.newform(500, 500, "Mario-Neat")
netPicture = forms.pictureBox(form, 5, 250,470, 200)
--int forms.pictureBox(int formhandle, [int? x = null], [int? y = null], [int? width = null], [int? height = null])
function displayGenome(genome)
forms.clear(netPicture,0x80808080)
local network = genome.network
local cells = {}
local i = 1
local cell = {}
for dy=-config.BoxRadius,config.BoxRadius do
for dx=-config.BoxRadius,config.BoxRadius do
cell = {}
cell.x = 50+5*dx
cell.y = 70+5*dy
cell.value = network.neurons[i].value
cells[i] = cell
i = i + 1
end
end
local biasCell = {}
biasCell.x = 80
biasCell.y = 110
biasCell.value = network.neurons[Inputs].value
cells[Inputs] = biasCell
for o = 1,Outputs do
cell = {}
cell.x = 220
cell.y = 30 + 8 * o
cell.value = network.neurons[config.NeatConfig.MaxNodes + o].value
cells[config.NeatConfig.MaxNodes+o] = cell
local color
if cell.value > 0 then
color = 0xFF0000FF
else
color = 0xFF000000
end
--gui.drawText(223, 24+8*o, config.ButtonNames[o], color, 9)
forms.drawText(netPicture,223, 24+8*o, config.ButtonNames[o], color, 9)
end
for n,neuron in pairs(network.neurons) do
cell = {}
if n > Inputs and n <= config.NeatConfig.MaxNodes then
cell.x = 140
cell.y = 40
cell.value = neuron.value
cells[n] = cell
end
end
for n=1,4 do
for _,gene in pairs(genome.genes) do
if gene.enabled then
local c1 = cells[gene.into]
local c2 = cells[gene.out]
if gene.into > Inputs and gene.into <= config.NeatConfig.MaxNodes then
c1.x = 0.75*c1.x + 0.25*c2.x
if c1.x >= c2.x then
c1.x = c1.x - 40
end
if c1.x < 90 then
c1.x = 90
end
if c1.x > 220 then
c1.x = 220
end
c1.y = 0.75*c1.y + 0.25*c2.y
end
if gene.out > Inputs and gene.out <= config.NeatConfig.MaxNodes then
c2.x = 0.25*c1.x + 0.75*c2.x
if c1.x >= c2.x then
c2.x = c2.x + 40
end
if c2.x < 90 then
c2.x = 90
end
if c2.x > 220 then
c2.x = 220
end
c2.y = 0.25*c1.y + 0.75*c2.y
end
end
end
end
--gui.drawBox(50-config.BoxRadius*5-3,70-config.BoxRadius*5-3,50+config.BoxRadius*5+2,70+config.BoxRadius*5+2,0xFF000000, 0x80808080)
forms.drawBox(netPicture, 50-config.BoxRadius*5-3,70-config.BoxRadius*5-3,50+config.BoxRadius*5+2,70+config.BoxRadius*5+2,0xFF000000, 0x80808080)
--oid forms.drawBox(int componenthandle, int x, int y, int x2, int y2, [color? line = null], [color? background = null])
for n,cell in pairs(cells) do
if n > Inputs or cell.value ~= 0 then
local color = math.floor((cell.value+1)/2*256)
if color > 255 then color = 255 end
if color < 0 then color = 0 end
local opacity = 0xFF000000
if cell.value == 0 then
opacity = 0x50000000
end
color = opacity + color*0x10000 + color*0x100 + color
forms.drawBox(netPicture,cell.x-2,cell.y-2,cell.x+2,cell.y+2,opacity,color)
--gui.drawBox(cell.x-2,cell.y-2,cell.x+2,cell.y+2,opacity,color)
end
end
for _,gene in pairs(genome.genes) do
if gene.enabled then
local c1 = cells[gene.into]
local c2 = cells[gene.out]
local opacity = 0xA0000000
if c1.value == 0 then
opacity = 0x20000000
end
local color = 0x80-math.floor(math.abs(mathFunctions.sigmoid(gene.weight))*0x80)
if gene.weight > 0 then
color = opacity + 0x8000 + 0x10000*color
else
color = opacity + 0x800000 + 0x100*color
end
--gui.drawLine(c1.x+1, c1.y, c2.x-3, c2.y, color)
forms.drawLine(netPicture,c1.x+1, c1.y, c2.x-3, c2.y, color)
end
end
--gui.drawBox(49,71,51,78,0x00000000,0x80FF0000)
forms.drawBox(netPicture, 49,71,51,78,0x00000000,0x80FF0000)
--if forms.ischecked(showMutationRates) then
local pos = 100
for mutation,rate in pairs(genome.mutationRates) do
--gui.drawText(100, pos, mutation .. ": " .. rate, 0xFF000000, 10)
forms.drawText(netPicture,100, pos, mutation .. ": " .. rate, 0xFF000000, 10)
--forms.drawText(pictureBox,400,pos, mutation .. ": " .. rate)
--void forms.drawText(int componenthandle, int x, int y, string message, [color? forecolor = null], [color? backcolor = null], [int? fontsize = null], [string fontfamily = null], [string fontstyle = null], [string horizalign = null], [string vertalign = null])
pos = pos + 8
end
--end
forms.refresh(netPicture)
end
function writeFile(filename)
local file = io.open(filename, "w")
file:write(pool.generation .. "\n")
file:write(pool.maxFitness .. "\n")
file:write(#pool.species .. "\n")
for n,species in pairs(pool.species) do
file:write(species.topFitness .. "\n")
file:write(species.staleness .. "\n")
file:write(#species.genomes .. "\n")
for m,genome in pairs(species.genomes) do
file:write(genome.fitness .. "\n")
file:write(genome.maxneuron .. "\n")
for mutation,rate in pairs(genome.mutationRates) do
file:write(mutation .. "\n")
file:write(rate .. "\n")
end
file:write("done\n")
file:write(#genome.genes .. "\n")
for l,gene in pairs(genome.genes) do
file:write(gene.into .. " ")
file:write(gene.out .. " ")
file:write(gene.weight .. " ")
file:write(gene.innovation .. " ")
if(gene.enabled) then
file:write("1\n")
else
file:write("0\n")
end
end
end
end
file:close()
end
function savePool()
local filename = forms.gettext(saveLoadFile)
print(filename)
writeFile(filename)
end
function mysplit(inputstr, sep)
if sep == nil then
sep = "%s"
end
local t={} ; i=1
for str in string.gmatch(inputstr, "([^"..sep.."]+)") do
t[i] = str
i = i + 1
end
return t
end
function loadFile(filename)
print("Loading pool from " .. filename)
local file = io.open(filename, "r")
pool = newPool()
pool.generation = file:read("*number")
pool.maxFitness = file:read("*number")
forms.settext(MaxLabel, "Max Fitness: " .. math.floor(pool.maxFitness))
local numSpecies = file:read("*number")
for s=1,numSpecies do
local species = newSpecies()
table.insert(pool.species, species)
species.topFitness = file:read("*number")
species.staleness = file:read("*number")
local numGenomes = file:read("*number")
for g=1,numGenomes do
local genome = newGenome()
table.insert(species.genomes, genome)
genome.fitness = file:read("*number")
genome.maxneuron = file:read("*number")
local line = file:read("*line")
while line ~= "done" do
genome.mutationRates[line] = file:read("*number")
line = file:read("*line")
end
local numGenes = file:read("*number")
for n=1,numGenes do
local gene = newGene()
local enabled
local geneStr = file:read("*line")
local geneArr = mysplit(geneStr)
gene.into = tonumber(geneArr[1])
gene.out = tonumber(geneArr[2])
gene.weight = tonumber(geneArr[3])
gene.innovation = tonumber(geneArr[4])
enabled = tonumber(geneArr[5])
if enabled == 0 then
gene.enabled = false
else
gene.enabled = true
end
table.insert(genome.genes, gene)
end
end
end
file:close()
while fitnessAlreadyMeasured() do
nextGenome()
end
initializeRun()
pool.currentFrame = pool.currentFrame + 1
print("Pool loaded.")
end
function flipState()
if config.Running == true then
config.Running = false
forms.settext(startButton, "Start")
else
config.Running = true
forms.settext(startButton, "Stop")
end
end
function loadPool()
filename = forms.openfile("DP1.state.pool",config.PoolDir)
--local filename = forms.gettext(saveLoadFile)
forms.settext(saveLoadFile, filename)
loadFile(filename)
end
function playTop()
local maxfitness = 0
local maxs, maxg
for s,species in pairs(pool.species) do
for g,genome in pairs(species.genomes) do
if genome.fitness > maxfitness then
maxfitness = genome.fitness
maxs = s
maxg = g
end
end
end
pool.currentSpecies = maxs
pool.currentGenome = maxg
pool.maxFitness = maxfitness
forms.settext(MaxLabel, "Max Fitness: " .. math.floor(pool.maxFitness))
initializeRun()
pool.currentFrame = pool.currentFrame + 1
return
end