酷！可以自学玩超级马里奥游戏的程序

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酷！可以自学玩超级马里奥游戏的程序

2015/06/15• 伯小乐分享 • 游戏 •6 评论

MarI/O 是一段由神经网络和遗传算法构成的程序，可以自学玩超级马里奥游戏。这是今天 Reddit/programming 上最火的一个帖子。

作者：SethBling

（原视频在 Youtube，伯乐在线搬运到国内了）
源码在这里：http://pastebin.com/ZZmSNaHX （没梯子的童鞋，直接看下面的）

-- MarI/O by SethBling
-- Feel free to use this code, but please do not redistribute it.
-- Intended for use with the BizHawk emulator and Super Mario World or Super Mario Bros. ROM.

if gameinfo.getromname() == &quot;Super Mario World (USA)&quot; then
	Filename = &quot;DP1.state&quot;
	ButtonNames = {
		&quot;A&quot;,
		&quot;B&quot;,
		&quot;X&quot;,
		&quot;Y&quot;,
		&quot;Up&quot;,
		&quot;Down&quot;,
		&quot;Left&quot;,
		&quot;Right&quot;,
	}
elseif gameinfo.getromname() == &quot;Super Mario Bros.&quot; then
	Filename = &quot;SMB1-1.state&quot;
	ButtonNames = {
		&quot;A&quot;,
		&quot;B&quot;,
		&quot;Up&quot;,
		&quot;Down&quot;,
		&quot;Left&quot;,
		&quot;Right&quot;,
	}
end

BoxRadius = 6
InputSize = (BoxRadius*2+1)*(BoxRadius*2+1)

Inputs = InputSize+1
Outputs = #ButtonNames

Population = 300
DeltaDisjoint = 2.0
DeltaWeights = 0.4
DeltaThreshold = 1.0

StaleSpecies = 15

MutateConnectionsChance = 0.25
PerturbChance = 0.90
CrossoverChance = 0.75
LinkMutationChance = 2.0
NodeMutationChance = 0.50
BiasMutationChance = 0.40
StepSize = 0.1
DisableMutationChance = 0.4
EnableMutationChance = 0.2

TimeoutConstant = 20

MaxNodes = 1000000

function getPositions()
	if gameinfo.getromname() == &quot;Super Mario World (USA)&quot; then
		marioX = memory.read_s16_le(0x94)
		marioY = memory.read_s16_le(0x96)

		local layer1x = memory.read_s16_le(0x1A);
		local layer1y = memory.read_s16_le(0x1C);

		screenX = marioX-layer1x
		screenY = marioY-layer1y
	elseif gameinfo.getromname() == &quot;Super Mario Bros.&quot; then
		marioX = memory.readbyte(0x6D) * 0x100 + memory.readbyte(0x86)
		marioY = memory.readbyte(0x03B8)+16

		screenX = memory.readbyte(0x03AD)
		screenY = memory.readbyte(0x03B8)
	end
end

function getTile(dx, dy)
	if gameinfo.getromname() == &quot;Super Mario World (USA)&quot; then
		x = math.floor((marioX+dx+8)/16)
		y = math.floor((marioY+dy)/16)

		return memory.readbyte(0x1C800 + math.floor(x/0x10)*0x1B0 + y*0x10 + x%0x10)
	elseif gameinfo.getromname() == &quot;Super Mario Bros.&quot; then
		local x = marioX + dx + 8
		local y = marioY + dy - 16
		local page = math.floor(x/256)%2

		local subx = math.floor((x%256)/16)
		local suby = math.floor((y - 32)/16)
		local addr = 0x500 + page*13*16+suby*16+subx

		if suby &gt;= 13 or suby &lt; 0 then
			return 0
		end

		if memory.readbyte(addr) ~= 0 then
			return 1
		else
			return 0
		end
	end
end

function getSprites()
	if gameinfo.getromname() == &quot;Super Mario World (USA)&quot; then
		local sprites = {}
		for slot=0,11 do
			local status = memory.readbyte(0x14C8+slot)
			if status ~= 0 then
				spritex = memory.readbyte(0xE4+slot) + memory.readbyte(0x14E0+slot)*256
				spritey = memory.readbyte(0xD8+slot) + memory.readbyte(0x14D4+slot)*256
				sprites[#sprites+1] = {[&quot;x&quot;]=spritex, [&quot;y&quot;]=spritey}
			end
		end		

		return sprites
	elseif gameinfo.getromname() == &quot;Super Mario Bros.&quot; then
		local sprites = {}
		for slot=0,4 do
			local enemy = memory.readbyte(0xF+slot)
			if enemy ~= 0 then
				local ex = memory.readbyte(0x6E + slot)*0x100 + memory.readbyte(0x87+slot)
				local ey = memory.readbyte(0xCF + slot)+24
				sprites[#sprites+1] = {[&quot;x&quot;]=ex,[&quot;y&quot;]=ey}
			end
		end

		return sprites
	end
end

function getExtendedSprites()
	if gameinfo.getromname() == &quot;Super Mario World (USA)&quot; then
		local extended = {}
		for slot=0,11 do
			local number = memory.readbyte(0x170B+slot)
			if number ~= 0 then
				spritex = memory.readbyte(0x171F+slot) + memory.readbyte(0x1733+slot)*256
				spritey = memory.readbyte(0x1715+slot) + memory.readbyte(0x1729+slot)*256
				extended[#extended+1] = {[&quot;x&quot;]=spritex, [&quot;y&quot;]=spritey}
			end
		end		

		return extended
	elseif gameinfo.getromname() == &quot;Super Mario Bros.&quot; then
		return {}
	end
end

function getInputs()
	getPositions()

	sprites = getSprites()
	extended = getExtendedSprites()

	local inputs = {}

	for dy=-BoxRadius*16,BoxRadius*16,16 do
		for dx=-BoxRadius*16,BoxRadius*16,16 do
			inputs[#inputs+1] = 0

			tile = getTile(dx, dy)
			if tile == 1 and marioY+dy &lt; 0x1B0 then
				inputs[#inputs] = 1
			end

			for i = 1,#sprites do
				distx = math.abs(sprites[i][&quot;x&quot;] - (marioX+dx))
				disty = math.abs(sprites[i][&quot;y&quot;] - (marioY+dy))
				if distx &lt;= 8 and disty &lt;= 8 then
					inputs[#inputs] = -1
				end
			end

			for i = 1,#extended do
				distx = math.abs(extended[i][&quot;x&quot;] - (marioX+dx))
				disty = math.abs(extended[i][&quot;y&quot;] - (marioY+dy))
				if distx &lt; 8 and disty &lt; 8 then
					inputs[#inputs] = -1
				end
			end
		end
	end

	--mariovx = memory.read_s8(0x7B)
	--mariovy = memory.read_s8(0x7D)

	return inputs
end

function sigmoid(x)
	return 2/(1+math.exp(-4.9*x))-1
end

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[&quot;connections&quot;] = MutateConnectionsChance
	genome.mutationRates[&quot;link&quot;] = LinkMutationChance
	genome.mutationRates[&quot;bias&quot;] = BiasMutationChance
	genome.mutationRates[&quot;node&quot;] = NodeMutationChance
	genome.mutationRates[&quot;enable&quot;] = EnableMutationChance
	genome.mutationRates[&quot;disable&quot;] = DisableMutationChance
	genome.mutationRates[&quot;step&quot;] = 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[&quot;connections&quot;] = genome.mutationRates[&quot;connections&quot;]
	genome2.mutationRates[&quot;link&quot;] = genome.mutationRates[&quot;link&quot;]
	genome2.mutationRates[&quot;bias&quot;] = genome.mutationRates[&quot;bias&quot;]
	genome2.mutationRates[&quot;node&quot;] = genome.mutationRates[&quot;node&quot;]
	genome2.mutationRates[&quot;enable&quot;] = genome.mutationRates[&quot;enable&quot;]
	genome2.mutationRates[&quot;disable&quot;] = genome.mutationRates[&quot;disable&quot;]

	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 copyGene(gene)
	local gene2 = newGene()
	gene2.into = gene.into
	gene2.out = gene.out
	gene2.weight = gene.weight
	gene2.enabled = gene.enabled
	gene2.innovation = gene.innovation

	return gene2
end

function newNeuron()
	local neuron = {}
	neuron.incoming = {}
	neuron.value = 0.0

	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[MaxNodes+o] = newNeuron()
	end

	table.sort(genome.genes, function (a,b)
		return (a.out &lt; 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)
	table.insert(inputs, 1)
	if #inputs ~= Inputs then
		console.writeline(&quot;Incorrect number of neural network inputs.&quot;)
		return {}
	end

	for i=1,Inputs do
		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 &gt; 0 then
			neuron.value = sigmoid(sum)
		end
	end

	local outputs = {}
	for o=1,Outputs do
		local button = &quot;P1 &quot; .. ButtonNames[o]
		if network.neurons[MaxNodes+o].value &gt; 0 then
			outputs[button] = true
		else
			outputs[button] = false
		end
	end

	return outputs
end

function crossover(g1, g2)
	-- Make sure g1 is the higher fitness genome
	if g2.fitness &gt; g1.fitness then
		tempg = g1
		g1 = g2
		g2 = tempg
	end

	local child = newGenome()

	local innovations2 = {}
	for i=1,#g2.genes do
		local gene = g2.genes[i]
		innovations2[gene.innovation] = gene
	end

	for i=1,#g1.genes do
		local gene1 = g1.genes[i]
		local gene2 = innovations2[gene1.innovation]
		if gene2 ~= nil and math.random(2) == 1 and gene2.enabled then
			table.insert(child.genes, copyGene(gene2))
		else
			table.insert(child.genes, copyGene(gene1))
		end
	end

	child.maxneuron = math.max(g1.maxneuron,g2.maxneuron)

	for mutation,rate in pairs(g1.mutationRates) do
		child.mutationRates[mutation] = rate
	end

	return child
end

function randomNeuron(genes, nonInput)
	local neurons = {}
	if not nonInput then
		for i=1,Inputs do
			neurons[i] = true
		end
	end
	for o=1,Outputs do
		neurons[MaxNodes+o] = true
	end
	for i=1,#genes do
		if (not nonInput) or genes[i].into &gt; Inputs then
			neurons[genes[i].into] = true
		end
		if (not nonInput) or genes[i].out &gt; Inputs then
			neurons[genes[i].out] = true
		end
	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[&quot;step&quot;]

	for i=1,#genome.genes do
		local gene = genome.genes[i]
		if math.random() &lt; 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 &lt;= Inputs and neuron2 &lt;= Inputs then
		--Both input nodes
		return
	end
	if neuron2 &lt;= 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() &lt; genome.mutationRates[&quot;connections&quot;] then
		pointMutate(genome)
	end

	local p = genome.mutationRates[&quot;link&quot;]
	while p &gt; 0 do
		if math.random() &lt; p then
			linkMutate(genome, false)
		end
		p = p - 1
	end

	p = genome.mutationRates[&quot;bias&quot;]
	while p &gt; 0 do
		if math.random() &lt; p then
			linkMutate(genome, true)
		end
		p = p - 1
	end

	p = genome.mutationRates[&quot;node&quot;]
	while p &gt; 0 do
		if math.random() &lt; p then
			nodeMutate(genome)
		end
		p = p - 1
	end

	p = genome.mutationRates[&quot;enable&quot;]
	while p &gt; 0 do
		if math.random() &lt; p then
			enableDisableMutate(genome, true)
		end
		p = p - 1
	end

	p = genome.mutationRates[&quot;disable&quot;]
	while p &gt; 0 do
		if math.random() &lt; 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 = DeltaDisjoint*disjoint(genome1.genes, genome2.genes)
	local dw = DeltaWeights*weights(genome1.genes, genome2.genes) 
	return dd + dw &lt; 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 &lt; 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 &gt; b.fitness)
		end)

		local remaining = math.ceil(#species.genomes/2)
		if cutToOne then
			remaining = 1
		end
		while #species.genomes &gt; remaining do
			table.remove(species.genomes)
		end
	end
end

function breedChild(species)
	local child = {}
	if math.random() &lt; 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 &gt; b.fitness)
		end)

		if species.genomes[1].fitness &gt; species.topFitness then
			species.topFitness = species.genomes[1].fitness
			species.staleness = 0
		else
			species.staleness = species.staleness + 1
		end
		if species.staleness &lt; StaleSpecies or species.topFitness &gt;= 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 * Population)
		if breed &gt;= 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 * 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 &lt; 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(&quot;backup.&quot; .. pool.generation .. &quot;.&quot; .. forms.gettext(saveLoadFile))
end

function initializePool()
	pool = newPool()

	for i=1,Population do
		basic = basicGenome()
		addToSpecies(basic)
	end

	initializeRun()
end

function clearJoypad()
	controller = {}
	for b = 1,#ButtonNames do
		controller[&quot;P1 &quot; .. ButtonNames[b]] = false
	end
	joypad.set(controller)
end

function initializeRun()
	savestate.load(Filename);
	rightmost = 0
	pool.currentFrame = 0
	timeout = TimeoutConstant
	clearJoypad()

	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]

	inputs = getInputs()
	controller = evaluateNetwork(genome.network, inputs)

	if controller[&quot;P1 Left&quot;] and controller[&quot;P1 Right&quot;] then
		controller[&quot;P1 Left&quot;] = false
		controller[&quot;P1 Right&quot;] = false
	end
	if controller[&quot;P1 Up&quot;] and controller[&quot;P1 Down&quot;] then
		controller[&quot;P1 Up&quot;] = false
		controller[&quot;P1 Down&quot;] = false
	end

	joypad.set(controller)
end

if pool == nil then
	initializePool()
end

function nextGenome()
	pool.currentGenome = pool.currentGenome + 1
	if pool.currentGenome &gt; #pool.species[pool.currentSpecies].genomes then
		pool.currentGenome = 1
		pool.currentSpecies = pool.currentSpecies+1
		if pool.currentSpecies &gt; #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

function displayGenome(genome)
	local network = genome.network
	local cells = {}
	local i = 1
	local cell = {}
	for dy=-BoxRadius,BoxRadius do
		for dx=-BoxRadius,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[MaxNodes + o].value
		cells[MaxNodes+o] = cell
		local color
		if cell.value &gt; 0 then
			color = 0xFF0000FF
		else
			color = 0xFF000000
		end
		gui.drawText(223, 24+8*o, ButtonNames[o], color, 9)
	end

	for n,neuron in pairs(network.neurons) do
		cell = {}
		if n &gt; Inputs and n &lt;= 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 &gt; Inputs and gene.into &lt;= MaxNodes then
					c1.x = 0.75*c1.x + 0.25*c2.x
					if c1.x &gt;= c2.x then
						c1.x = c1.x - 40
					end
					if c1.x &lt; 90 then
						c1.x = 90
					end

					if c1.x &gt; 220 then
						c1.x = 220
					end
					c1.y = 0.75*c1.y + 0.25*c2.y

				end
				if gene.out &gt; Inputs and gene.out &lt;= MaxNodes then
					c2.x = 0.25*c1.x + 0.75*c2.x
					if c1.x &gt;= c2.x then
						c2.x = c2.x + 40
					end
					if c2.x &lt; 90 then
						c2.x = 90
					end
					if c2.x &gt; 220 then
						c2.x = 220
					end
					c2.y = 0.25*c1.y + 0.75*c2.y
				end
			end
		end
	end

	gui.drawBox(50-BoxRadius*5-3,70-BoxRadius*5-3,50+BoxRadius*5+2,70+BoxRadius*5+2,0xFF000000, 0x80808080)
	for n,cell in pairs(cells) do
		if n &gt; Inputs or cell.value ~= 0 then
			local color = math.floor((cell.value+1)/2*256)
			if color &gt; 255 then color = 255 end
			if color &lt; 0 then color = 0 end
			local opacity = 0xFF000000
			if cell.value == 0 then
				opacity = 0x50000000
			end
			color = opacity + color*0x10000 + color*0x100 + 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(sigmoid(gene.weight))*0x80)
			if gene.weight &gt; 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)
		end
	end

	gui.drawBox(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 .. &quot;: &quot; .. rate, 0xFF000000, 10)
			pos = pos + 8
		end
	end
end

function writeFile(filename)
        local file = io.open(filename, &quot;w&quot;)
	file:write(pool.generation .. &quot;\n&quot;)
	file:write(pool.maxFitness .. &quot;\n&quot;)
	file:write(#pool.species .. &quot;\n&quot;)
        for n,species in pairs(pool.species) do
		file:write(species.topFitness .. &quot;\n&quot;)
		file:write(species.staleness .. &quot;\n&quot;)
		file:write(#species.genomes .. &quot;\n&quot;)
		for m,genome in pairs(species.genomes) do
			file:write(genome.fitness .. &quot;\n&quot;)
			file:write(genome.maxneuron .. &quot;\n&quot;)
			for mutation,rate in pairs(genome.mutationRates) do
				file:write(mutation .. &quot;\n&quot;)
				file:write(rate .. &quot;\n&quot;)
			end
			file:write(&quot;done\n&quot;)

			file:write(#genome.genes .. &quot;\n&quot;)
			for l,gene in pairs(genome.genes) do
				file:write(gene.into .. &quot; &quot;)
				file:write(gene.out .. &quot; &quot;)
				file:write(gene.weight .. &quot; &quot;)
				file:write(gene.innovation .. &quot; &quot;)
				if(gene.enabled) then
					file:write(&quot;1\n&quot;)
				else
					file:write(&quot;0\n&quot;)
				end
			end
		end
        end
        file:close()
end

function savePool()
	local filename = forms.gettext(saveLoadFile)
	writeFile(filename)
end

function loadFile(filename)
        local file = io.open(filename, &quot;r&quot;)
	pool = newPool()
	pool.generation = file:read(&quot;*number&quot;)
	pool.maxFitness = file:read(&quot;*number&quot;)
	forms.settext(maxFitnessLabel, &quot;Max Fitness: &quot; .. math.floor(pool.maxFitness))
        local numSpecies = file:read(&quot;*number&quot;)
        for s=1,numSpecies do
		local species = newSpecies()
		table.insert(pool.species, species)
		species.topFitness = file:read(&quot;*number&quot;)
		species.staleness = file:read(&quot;*number&quot;)
		local numGenomes = file:read(&quot;*number&quot;)
		for g=1,numGenomes do
			local genome = newGenome()
			table.insert(species.genomes, genome)
			genome.fitness = file:read(&quot;*number&quot;)
			genome.maxneuron = file:read(&quot;*number&quot;)
			local line = file:read(&quot;*line&quot;)
			while line ~= &quot;done&quot; do
				genome.mutationRates[line] = file:read(&quot;*number&quot;)
				line = file:read(&quot;*line&quot;)
			end
			local numGenes = file:read(&quot;*number&quot;)
			for n=1,numGenes do
				local gene = newGene()
				table.insert(genome.genes, gene)
				local enabled
				gene.into, gene.out, gene.weight, gene.innovation, enabled = file:read(&quot;*number&quot;, &quot;*number&quot;, &quot;*number&quot;, &quot;*number&quot;, &quot;*number&quot;)
				if enabled == 0 then
					gene.enabled = false
				else
					gene.enabled = true
				end

			end
		end
	end
        file:close()

	while fitnessAlreadyMeasured() do
		nextGenome()
	end
	initializeRun()
	pool.currentFrame = pool.currentFrame + 1
end

function loadPool()
	local filename = forms.gettext(saveLoadFile)
	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 &gt; maxfitness then
				maxfitness = genome.fitness
				maxs = s
				maxg = g
			end
		end
	end

	pool.currentSpecies = maxs
	pool.currentGenome = maxg
	pool.maxFitness = maxfitness
	forms.settext(maxFitnessLabel, &quot;Max Fitness: &quot; .. math.floor(pool.maxFitness))
	initializeRun()
	pool.currentFrame = pool.currentFrame + 1
	return
end

function onExit()
	forms.destroy(form)
end

writeFile(&quot;temp.pool&quot;)

event.onexit(onExit)

form = forms.newform(200, 260, &quot;Fitness&quot;)
maxFitnessLabel = forms.label(form, &quot;Max Fitness: &quot; .. math.floor(pool.maxFitness), 5, 8)
showNetwork = forms.checkbox(form, &quot;Show Map&quot;, 5, 30)
showMutationRates = forms.checkbox(form, &quot;Show M-Rates&quot;, 5, 52)
restartButton = forms.button(form, &quot;Restart&quot;, initializePool, 5, 77)
saveButton = forms.button(form, &quot;Save&quot;, savePool, 5, 102)
loadButton = forms.button(form, &quot;Load&quot;, loadPool, 80, 102)
saveLoadFile = forms.textbox(form, Filename .. &quot;.pool&quot;, 170, 25, nil, 5, 148)
saveLoadLabel = forms.label(form, &quot;Save/Load:&quot;, 5, 129)
playTopButton = forms.button(form, &quot;Play Top&quot;, playTop, 5, 170)
hideBanner = forms.checkbox(form, &quot;Hide Banner&quot;, 5, 190)

while true do
	local backgroundColor = 0xD0FFFFFF
	if not forms.ischecked(hideBanner) then
		gui.drawBox(0, 0, 300, 26, backgroundColor, backgroundColor)
	end

	local species = pool.species[pool.currentSpecies]
	local genome = species.genomes[pool.currentGenome]

	if forms.ischecked(showNetwork) then
		displayGenome(genome)
	end

	if pool.currentFrame%5 == 0 then
		evaluateCurrent()
	end

	joypad.set(controller)

	getPositions()
	if marioX &gt; rightmost then
		rightmost = marioX
		timeout = TimeoutConstant
	end

	timeout = timeout - 1

	local timeoutBonus = pool.currentFrame / 4
	if timeout + timeoutBonus &lt;= 0 then
		local fitness = rightmost - pool.currentFrame / 2
		if gameinfo.getromname() == &quot;Super Mario World (USA)&quot; and rightmost &gt; 4816 then
			fitness = fitness + 1000
		end
		if gameinfo.getromname() == &quot;Super Mario Bros.&quot; and rightmost &gt; 3186 then
			fitness = fitness + 1000
		end
		if fitness == 0 then
			fitness = -1
		end
		genome.fitness = fitness

		if fitness &gt; pool.maxFitness then
			pool.maxFitness = fitness
			forms.settext(maxFitnessLabel, &quot;Max Fitness: &quot; .. math.floor(pool.maxFitness))
			writeFile(&quot;backup.&quot; .. pool.generation .. &quot;.&quot; .. forms.gettext(saveLoadFile))
		end

		console.writeline(&quot;Gen &quot; .. pool.generation .. &quot; species &quot; .. pool.currentSpecies .. &quot; genome &quot; .. pool.currentGenome .. &quot; fitness: &quot; .. fitness)
		pool.currentSpecies = 1
		pool.currentGenome = 1
		while fitnessAlreadyMeasured() do
			nextGenome()
		end
		initializeRun()
	end

	local measured = 0
	local total = 0
	for _,species in pairs(pool.species) do
		for _,genome in pairs(species.genomes) do
			total = total + 1
			if genome.fitness ~= 0 then
				measured = measured + 1
			end
		end
	end
	if not forms.ischecked(hideBanner) then
		gui.drawText(0, 0, &quot;Gen &quot; .. pool.generation .. &quot; species &quot; .. pool.currentSpecies .. &quot; genome &quot; .. pool.currentGenome .. &quot; (&quot; .. math.floor(measured/total*100) .. &quot;%)&quot;, 0xFF000000, 11)
		gui.drawText(0, 12, &quot;Fitness: &quot; .. math.floor(rightmost - (pool.currentFrame) / 2 - (timeout + timeoutBonus)*2/3), 0xFF000000, 11)
		gui.drawText(100, 12, &quot;Max Fitness: &quot; .. math.floor(pool.maxFitness), 0xFF000000, 11)
	end

	pool.currentFrame = pool.currentFrame + 1

	emu.frameadvance();
end

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-- MarI/O by SethBling

-- Feel free to use this code, but please do not redistribute it.

-- Intended for use with the BizHawk emulator and Super Mario World or Super Mario Bros. ROM.

if gameinfo.getromname() == "Super Mario World (USA)" then

Filename = "DP1.state"

ButtonNames = {

"A",

"B",

"X",

"Y",

"Up",

"Down",

"Left",

"Right",

}

elseif gameinfo.getromname() == "Super Mario Bros." then

Filename = "SMB1-1.state"

ButtonNames = {

"A",

"B",

"Up",

"Down",

"Left",

"Right",

}

end

BoxRadius = 6

InputSize = (BoxRadius*2+1)*(BoxRadius*2+1)

Inputs = InputSize+1

Outputs = #ButtonNames

Population = 300

DeltaDisjoint = 2.0

DeltaWeights = 0.4

DeltaThreshold = 1.0

StaleSpecies = 15

MutateConnectionsChance = 0.25

PerturbChance = 0.90

CrossoverChance = 0.75

LinkMutationChance = 2.0

NodeMutationChance = 0.50

BiasMutationChance = 0.40

StepSize = 0.1

DisableMutationChance = 0.4

EnableMutationChance = 0.2

TimeoutConstant = 20

MaxNodes = 1000000

function getPositions()

if gameinfo.getromname() == "Super Mario World (USA)" then

marioX = memory.read_s16_le(0x94)

marioY = memory.read_s16_le(0x96)

local layer1x = memory.read_s16_le(0x1A);

local layer1y = memory.read_s16_le(0x1C);

screenX = marioX-layer1x

screenY = marioY-layer1y

elseif gameinfo.getromname() == "Super Mario Bros." then

marioX = memory.readbyte(0x6D) * 0x100 + memory.readbyte(0x86)

marioY = memory.readbyte(0x03B8)+16

screenX = memory.readbyte(0x03AD)

screenY = memory.readbyte(0x03B8)

end

function getTile(dx, dy)

if gameinfo.getromname() == "Super Mario World (USA)" then

x = math.floor((marioX+dx+8)/16)

y = math.floor((marioY+dy)/16)

return memory.readbyte(0x1C800 + math.floor(x/0x10)*0x1B0 + y*0x10 + x%0x10)

elseif gameinfo.getromname() == "Super Mario Bros." then

local x = marioX + dx + 8

local y = marioY + dy - 16

local page = math.floor(x/256)%2

local subx = math.floor((x%256)/16)

local suby = math.floor((y - 32)/16)

local addr = 0x500 + page*13*16+suby*16+subx

if suby >= 13 or suby < 0 then

return 0

end

if memory.readbyte(addr) ~= 0 then

return 1

else

return 0

end

function getSprites()

if gameinfo.getromname() == "Super Mario World (USA)" then

local sprites = {}

for slot=0,11 do

local status = memory.readbyte(0x14C8+slot)

if status ~= 0 then

spritex = memory.readbyte(0xE4+slot) + memory.readbyte(0x14E0+slot)*256

spritey = memory.readbyte(0xD8+slot) + memory.readbyte(0x14D4+slot)*256

sprites[#sprites+1] = {["x"]=spritex, ["y"]=spritey}

end

return sprites

elseif gameinfo.getromname() == "Super Mario Bros." then

local sprites = {}

for slot=0,4 do

local enemy = memory.readbyte(0xF+slot)

if enemy ~= 0 then

local ex = memory.readbyte(0x6E + slot)*0x100 + memory.readbyte(0x87+slot)

local ey = memory.readbyte(0xCF + slot)+24

sprites[#sprites+1] = {["x"]=ex,["y"]=ey}

end

return sprites

end

function getExtendedSprites()

if gameinfo.getromname() == "Super Mario World (USA)" then

local extended = {}

for slot=0,11 do

local number = memory.readbyte(0x170B+slot)

if number ~= 0 then

spritex = memory.readbyte(0x171F+slot) + memory.readbyte(0x1733+slot)*256

spritey = memory.readbyte(0x1715+slot) + memory.readbyte(0x1729+slot)*256

extended[#extended+1] = {["x"]=spritex, ["y"]=spritey}

end

return extended

elseif gameinfo.getromname() == "Super Mario Bros." then

return {}

end

function getInputs()

getPositions()

sprites = getSprites()

extended = getExtendedSprites()

local inputs = {}

for dy=-BoxRadius*16,BoxRadius*16,16 do

for dx=-BoxRadius*16,BoxRadius*16,16 do

inputs[#inputs+1] = 0

tile = getTile(dx, dy)

if tile == 1 and marioY+dy < 0x1B0 then

inputs[#inputs] = 1

end

for i = 1,#sprites do

distx = math.abs(sprites[i]["x"] - (marioX+dx))

disty = math.abs(sprites[i]["y"] - (marioY+dy))

if distx <= 8 and disty <= 8 then

inputs[#inputs] = -1

end

for i = 1,#extended do

distx = math.abs(extended[i]["x"] - (marioX+dx))

disty = math.abs(extended[i]["y"] - (marioY+dy))

if distx < 8 and disty < 8 then

inputs[#inputs] = -1

end

--mariovx = memory.read_s8(0x7B)

--mariovy = memory.read_s8(0x7D)

return inputs

end

function sigmoid(x)

return 2/(1+math.exp(-4.9*x))-1

end

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"] = MutateConnectionsChance

genome.mutationRates["link"] = LinkMutationChance

genome.mutationRates["bias"] = BiasMutationChance

genome.mutationRates["node"] = NodeMutationChance

genome.mutationRates["enable"] = EnableMutationChance

genome.mutationRates["disable"] = DisableMutationChance

genome.mutationRates["step"] = 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 copyGene(gene)

local gene2 = newGene()

gene2.into = gene.into

gene2.out = gene.out

gene2.weight = gene.weight

gene2.enabled = gene.enabled

gene2.innovation = gene.innovation

return gene2

end

function newNeuron()

local neuron = {}

neuron.incoming = {}

neuron.value = 0.0

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[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

genome.network = network

end

function evaluateNetwork(network, inputs)

table.insert(inputs, 1)

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]

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 = sigmoid(sum)

end

local outputs = {}

for o=1,Outputs do

local button = "P1 " .. ButtonNames[o]

if network.neurons[MaxNodes+o].value > 0 then

outputs[button] = true

else

outputs[button] = false

end

return outputs

end

function crossover(g1, g2)

-- Make sure g1 is the higher fitness genome

if g2.fitness > g1.fitness then

tempg = g1

g1 = g2

g2 = tempg

end

local child = newGenome()

local innovations2 = {}

for i=1,#g2.genes do

local gene = g2.genes[i]

innovations2[gene.innovation] = gene

end

for i=1,#g1.genes do

local gene1 = g1.genes[i]

local gene2 = innovations2[gene1.innovation]

if gene2 ~= nil and math.random(2) == 1 and gene2.enabled then

table.insert(child.genes, copyGene(gene2))

else

table.insert(child.genes, copyGene(gene1))

end

child.maxneuron = math.max(g1.maxneuron,g2.maxneuron)

for mutation,rate in pairs(g1.mutationRates) do

child.mutationRates[mutation] = rate

end

return child

end

function randomNeuron(genes, nonInput)

local neurons = {}

if not nonInput then

for i=1,Inputs do

neurons[i] = true

end

for o=1,Outputs do

neurons[MaxNodes+o] = true

end

for i=1,#genes do

if (not nonInput) or genes[i].into > Inputs then

neurons[genes[i].into] = true

end

if (not nonInput) or genes[i].out > Inputs then

neurons[genes[i].out] = true

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

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

function pointMutate(genome)

local step = genome.mutationRates["step"]

for i=1,#genome.genes do

local gene = genome.genes[i]

if math.random() < PerturbChance then

gene.weight = gene.weight + math.random() * step*2 - step

else

gene.weight = math.random()*4-2

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

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

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

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

for i = 1,#genes2 do

local gene = genes2[i]

if not i1[gene.innovation] then

disjointGenes = disjointGenes+1

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

return sum / coincident

end

function sameSpecies(genome1, genome2)

local dd = DeltaDisjoint*disjoint(genome1.genes, genome2.genes)

local dw = DeltaWeights*weights(genome1.genes, genome2.genes)

return dd + dw < 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

table.sort(global, function (a,b)

return (a.fitness < b.fitness)

end)

for g=1,#global do

global[g].globalRank = g

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

function breedChild(species)

local child = {}

if math.random() < 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 < StaleSpecies or species.topFitness >= pool.maxFitness then

table.insert(survived, species)

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 * Population)

if breed >= 1 then

table.insert(survived, species)

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

if not foundSpecies then

local childSpecies = newSpecies()

table.insert(childSpecies.genomes, child)

table.insert(pool.species, childSpecies)

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 * Population) - 1

for i=1,breed do

table.insert(children, breedChild(species))

end

cullSpecies(true) -- Cull all but the top member of each species

while #children + #pool.species < 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))

end

function initializePool()

pool = newPool()

for i=1,Population do

basic = basicGenome()

addToSpecies(basic)

end

initializeRun()

end

function clearJoypad()

controller = {}

for b = 1,#ButtonNames do

controller["P1 " .. ButtonNames[b]] = false

end

joypad.set(controller)

end

function initializeRun()

savestate.load(Filename);

rightmost = 0

pool.currentFrame = 0

timeout = TimeoutConstant

clearJoypad()

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]

inputs = getInputs()

controller = evaluateNetwork(genome.network, inputs)

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

function fitnessAlreadyMeasured()

local species = pool.species[pool.currentSpecies]

local genome = species.genomes[pool.currentGenome]

return genome.fitness ~= 0

end

function displayGenome(genome)

local network = genome.network

local cells = {}

local i = 1

local cell = {}

for dy=-BoxRadius,BoxRadius do

for dx=-BoxRadius,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

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[MaxNodes + o].value

cells[MaxNodes+o] = cell

local color

if cell.value > 0 then

color = 0xFF0000FF

else

color = 0xFF000000

end

gui.drawText(223, 24+8*o, ButtonNames[o], color, 9)

end

for n,neuron in pairs(network.neurons) do

cell = {}

if n > Inputs and n <= MaxNodes then

cell.x = 140

cell.y = 40

cell.value = neuron.value

cells[n] = cell

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 <= 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 <= 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

gui.drawBox(50-BoxRadius*5-3,70-BoxRadius*5-3,50+BoxRadius*5+2,70+BoxRadius*5+2,0xFF000000, 0x80808080)

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

gui.drawBox(cell.x-2,cell.y-2,cell.x+2,cell.y+2,opacity,color)

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(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)

end

gui.drawBox(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)

pos = pos + 8

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

file:close()

end

function savePool()

local filename = forms.gettext(saveLoadFile)

writeFile(filename)

end

function loadFile(filename)

local file = io.open(filename, "r")

pool = newPool()

pool.generation = file:read("*number")

pool.maxFitness = file:read("*number")

forms.settext(maxFitnessLabel, "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()

table.insert(genome.genes, gene)

local enabled

gene.into, gene.out, gene.weight, gene.innovation, enabled = file:read("*number", "*number", "*number", "*number", "*number")

if enabled == 0 then

gene.enabled = false

else

gene.enabled = true

end

file:close()

while fitnessAlreadyMeasured() do

nextGenome()

end

initializeRun()

pool.currentFrame = pool.currentFrame + 1

end

function loadPool()

local filename = forms.gettext(saveLoadFile)

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

pool.currentSpecies = maxs

pool.currentGenome = maxg

pool.maxFitness = maxfitness

forms.settext(maxFitnessLabel, "Max Fitness: " .. math.floor(pool.maxFitness))

initializeRun()

pool.currentFrame = pool.currentFrame + 1

return

end

function onExit()

forms.destroy(form)

end

writeFile("temp.pool")

event.onexit(onExit)

form = forms.newform(200, 260, "Fitness")

maxFitnessLabel = forms.label(form, "Max Fitness: " .. math.floor(pool.maxFitness), 5, 8)

showNetwork = forms.checkbox(form, "Show Map", 5, 30)

showMutationRates = forms.checkbox(form, "Show M-Rates", 5, 52)

restartButton = forms.button(form, "Restart", initializePool, 5, 77)

saveButton = forms.button(form, "Save", savePool, 5, 102)

loadButton = forms.button(form, "Load", loadPool, 80, 102)

saveLoadFile = forms.textbox(form, Filename .. ".pool", 170, 25, nil, 5, 148)

saveLoadLabel = forms.label(form, "Save/Load:", 5, 129)

playTopButton = forms.button(form, "Play Top", playTop, 5, 170)

hideBanner = forms.checkbox(form, "Hide Banner", 5, 190)

while true do

local backgroundColor = 0xD0FFFFFF

if not forms.ischecked(hideBanner) then

gui.drawBox(0, 0, 300, 26, backgroundColor, backgroundColor)

end

local species = pool.species[pool.currentSpecies]

local genome = species.genomes[pool.currentGenome]

if forms.ischecked(showNetwork) then

displayGenome(genome)

end

if pool.currentFrame%5 == 0 then

evaluateCurrent()

end

joypad.set(controller)

getPositions()

if marioX > rightmost then

rightmost = marioX

timeout = TimeoutConstant

end

timeout = timeout - 1

local timeoutBonus = pool.currentFrame / 4

if timeout + timeoutBonus <= 0 then

local fitness = rightmost - pool.currentFrame / 2

if gameinfo.getromname() == "Super Mario World (USA)" and rightmost > 4816 then

fitness = fitness + 1000

end

if gameinfo.getromname() == "Super Mario Bros." and rightmost > 3186 then

fitness = fitness + 1000

end

if fitness == 0 then

fitness = -1

end

genome.fitness = fitness

if fitness > pool.maxFitness then

pool.maxFitness = fitness

forms.settext(maxFitnessLabel, "Max Fitness: " .. math.floor(pool.maxFitness))

writeFile("backup." .. pool.generation .. "." .. forms.gettext(saveLoadFile))

end

console.writeline("Gen " .. pool.generation .. " species " .. pool.currentSpecies .. " genome " .. pool.currentGenome .. " fitness: " .. fitness)

pool.currentSpecies = 1

pool.currentGenome = 1

while fitnessAlreadyMeasured() do

nextGenome()

end

initializeRun()

end

local measured = 0

local total = 0

for _,species in pairs(pool.species) do

for _,genome in pairs(species.genomes) do

total = total + 1

if genome.fitness ~= 0 then

measured = measured + 1

end

if not forms.ischecked(hideBanner) then

gui.drawText(0, 0, "Gen " .. pool.generation .. " species " .. pool.currentSpecies .. " genome " .. pool.currentGenome .. " (" .. math.floor(measured/total*100) .. "%)", 0xFF000000, 11)