【转载】 NeuroEvolution with MarI/O —— 使用人工智能来通关超级玛丽

-- 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.
-- For SMW, make sure you have a save state named "DP一.state" at the beginning of a level,
-- and put a copy in both the Lua folder and the root directory of BizHawk.
 
if gameinfo.getromname() == "Super Mario World (USA)" then
    Filename = "DP一.state"
    ButtonNames = {
        "A",
        "B",
        "X",
        "Y",
        "Up",
        "Down",
        "Left",
        "Right",
    }
elseif gameinfo.getromname() == "Super Mario Bros." then
    Filename = "SMB一⑴.state"
    ButtonNames = {
        "A",
        "B",
        "Up",
        "Down",
        "Left",
        "Right",
    }
end
 
BoxRadius = 六
InputSize = (BoxRadius*二+一)*(BoxRadius*二+一)
 
Inputs = InputSize+一
Outputs = #ButtonNames
 
Population = 三00
DeltaDisjoint = 二.0
DeltaWeights = 0.四
DeltaThreshold = 一.0
 
StaleSpecies = 一五
 
MutateConnectionsChance = 0.二五
PerturbChance = 0.九0
CrossoverChance = 0.七五
LinkMutationChance = 二.0
NodeMutationChance = 0.五0
BiasMutationChance = 0.四0
StepSize = 0.一
DisableMutationChance = 0.四
EnableMutationChance = 0.二
 
TimeoutConstant = 二0
 
MaxNodes = 一000000
 
function getPositions()
    if gameinfo.getromname() == "Super Mario World (USA)" then
        marioX = memory.read_s一六_le(0x九四)
        marioY = memory.read_s一六_le(0x九六)
 
        local layer一x = memory.read_s一六_le(0x一A);
        local layer一y = memory.read_s一六_le(0x一C);
 
        screenX = marioX-layer一x
        screenY = marioY-layer一y
    elseif gameinfo.getromname() == "Super Mario Bros." then
        marioX = memory.readbyte(0x六D) * 0x一00 + memory.readbyte(0x八六)
        marioY = memory.readbyte(0x0三B八)+一六
 
        screenX = memory.readbyte(0x0三AD)
        screenY = memory.readbyte(0x0三B八)
    end
end
 
function getTile(dx, dy)
    if gameinfo.getromname() == "Super Mario World (USA)" then
        x = math.floor((marioX+dx+八)/一六)
        y = math.floor((marioY+dy)/一六)
 
        return memory.readbyte(0x一C八00 + math.floor(x/0x一0)*0x一B0 + y*0x一0 + x%0x一0)
    elseif gameinfo.getromname() == "Super Mario Bros." then
        local x = marioX + dx + 八
        local y = marioY + dy - 一六
        local page = math.floor(x/二五六)%二
 
        local subx = math.floor((x%二五六)/一六)
        local suby = math.floor((y - 三二)/一六)
        local addr = 0x五00 + page*一三*一六+suby*一六+subx
 
        if suby >= 一三 or suby < 0 then
            return 0
        end
 
        if memory.readbyte(addr) ~= 0 then
            return 一
        else
            return 0
        end
    end
end
 
function getSprites()
    if gameinfo.getromname() == "Super Mario World (USA)" then
        local sprites = {}
        for slot=0,一一 do
            local status = memory.readbyte(0x一四C八+slot)
            if status ~= 0 then
                spritex = memory.readbyte(0xE四+slot) + memory.readbyte(0x一四E0+slot)*二五六
                spritey = memory.readbyte(0xD八+slot) + memory.readbyte(0x一四D四+slot)*二五六
                sprites[#sprites+一] = {["x"]=spritex, ["y"]=spritey}
            end
        end        
 
        return sprites
    elseif gameinfo.getromname() == "Super Mario Bros." then
        local sprites = {}
        for slot=0,四 do
            local enemy = memory.readbyte(0xF+slot)
            if enemy ~= 0 then
                local ex = memory.readbyte(0x六E + slot)*0x一00 + memory.readbyte(0x八七+slot)
                local ey = memory.readbyte(0xCF + slot)+二四
                sprites[#sprites+一] = {["x"]=ex,["y"]=ey}
            end
        end
 
        return sprites
    end
end
 
function getExtendedSprites()
    if gameinfo.getromname() == "Super Mario World (USA)" then
        local extended = {}
        for slot=0,一一 do
            local number = memory.readbyte(0x一七0B+slot)
            if number ~= 0 then
                spritex = memory.readbyte(0x一七一F+slot) + memory.readbyte(0x一七三三+slot)*二五六
                spritey = memory.readbyte(0x一七一五+slot) + memory.readbyte(0x一七二九+slot)*二五六
                extended[#extended+一] = {["x"]=spritex, ["y"]=spritey}
            end
        end        
 
        return extended
    elseif gameinfo.getromname() == "Super Mario Bros." then
        return {}
    end
end
 
function getInputs()
    getPositions()
 
    sprites = getSprites()
    extended = getExtendedSprites()
 
    local inputs = {}
 
    for dy=-BoxRadius*一六,BoxRadius*一六,一六 do
        for dx=-BoxRadius*一六,BoxRadius*一六,一六 do
            inputs[#inputs+一] = 0
 
            tile = getTile(dx, dy)
            if tile == 一 and marioY+dy < 0x一B0 then
                inputs[#inputs] = 一
            end
 
            for i = 一,#sprites do
                distx = math.abs(sprites[i]["x"] - (marioX+dx))
                disty = math.abs(sprites[i]["y"] - (marioY+dy))
                if distx <= 八 and disty <= 八 then
                    inputs[#inputs] = -一
                end
            end
 
            for i = 一,#extended do
                distx = math.abs(extended[i]["x"] - (marioX+dx))
                disty = math.abs(extended[i]["y"] - (marioY+dy))
                if distx < 八 and disty < 八 then
                    inputs[#inputs] = -一
                end
            end
        end
    end
 
    --mariovx = memory.read_s八(0x七B)
    --mariovy = memory.read_s八(0x七D)
 
    return inputs
end
 
function sigmoid(x)
    return 二/(一+math.exp(-四.九*x))-一
end
 
function newInnovation()
    pool.innovation = pool.innovation + 一
    return pool.innovation
end
 
function newPool()
    local pool = {}
    pool.species = {}
    pool.generation = 0
    pool.innovation = Outputs
    pool.currentSpecies = 一
    pool.currentGenome = 一
    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 genome二 = newGenome()
    for g=一,#genome.genes do
        table.insert(genome二.genes, copyGene(genome.genes[g]))
    end
    genome二.maxneuron = genome.maxneuron
    genome二.mutationRates["connections"] = genome.mutationRates["connections"]
    genome二.mutationRates["link"] = genome.mutationRates["link"]
    genome二.mutationRates["bias"] = genome.mutationRates["bias"]
    genome二.mutationRates["node"] = genome.mutationRates["node"]
    genome二.mutationRates["enable"] = genome.mutationRates["enable"]
    genome二.mutationRates["disable"] = genome.mutationRates["disable"]
 
    return genome二
end
 
function basicGenome()
    local genome = newGenome()
    local innovation = 一
 
    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 gene二 = newGene()
    gene二.into = gene.into
    gene二.out = gene.out
    gene二.weight = gene.weight
    gene二.enabled = gene.enabled
    gene二.innovation = gene.innovation
 
    return gene二
end
 
function newNeuron()
    local neuron = {}
    neuron.incoming = {}
    neuron.value = 0.0
 
    return neuron
end
 
function generateNetwork(genome)
    local network = {}
    network.neurons = {}
 
    for i=一,Inputs do
        network.neurons[i] = newNeuron()
    end
 
    for o=一,Outputs do
        network.neurons[MaxNodes+o] = newNeuron()
    end
 
    table.sort(genome.genes, function (a,b)
        return (a.out < b.out)
    end)
    for i=一,#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, 一)
    if #inputs ~= Inputs then
        console.writeline("Incorrect number of neural network inputs.")
        return {}
    end
 
    for i=一,Inputs do
        network.neurons[i].value = inputs[i]
    end
 
    for _,neuron in pairs(network.neurons) do
        local sum = 0
        for j = 一,#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
    end
 
    local outputs = {}
    for o=一,Outputs do
        local button = "P一 " .. ButtonNames[o]
        if network.neurons[MaxNodes+o].value > 0 then
            outputs[button] = true
        else
            outputs[button] = false
        end
    end
 
    return outputs
end
 
function crossover(g一, g二)
    -- Make sure g一 is the higher fitness genome
    if g二.fitness > g一.fitness then
        tempg = g一
        g一 = g二
        g二 = tempg
    end
 
    local child = newGenome()
 
    local innovations二 = {}
    for i=一,#g二.genes do
        local gene = g二.genes[i]
        innovations二[gene.innovation] = gene
    end
 
    for i=一,#g一.genes do
        local gene一 = g一.genes[i]
        local gene二 = innovations二[gene一.innovation]
        if gene二 ~= nil and math.random(二) == 一 and gene二.enabled then
            table.insert(child.genes, copyGene(gene二))
        else
            table.insert(child.genes, copyGene(gene一))
        end
    end
 
    child.maxneuron = math.max(g一.maxneuron,g二.maxneuron)
 
    for mutation,rate in pairs(g一.mutationRates) do
        child.mutationRates[mutation] = rate
    end
 
    return child
end
 
function randomNeuron(genes, nonInput)
    local neurons = {}
    if not nonInput then
        for i=一,Inputs do
            neurons[i] = true
        end
    end
    for o=一,Outputs do
        neurons[MaxNodes+o] = true
    end
    for i=一,#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
    end
 
    local count = 0
    for _,_ in pairs(neurons) do
        count = count + 一
    end
    local n = math.random(一, count)
 
    for k,v in pairs(neurons) do
        n = n-一
        if n == 0 then
            return k
        end
    end
 
    return 0
end
 
function containsLink(genes, link)
    for i=一,#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=一,#genome.genes do
        local gene = genome.genes[i]
        if math.random() < PerturbChance then
            gene.weight = gene.weight + math.random() * step*二 - step
        else
            gene.weight = math.random()*四-二
        end
    end
end
 
function linkMutate(genome, forceBias)
    local neuron一 = randomNeuron(genome.genes, false)
    local neuron二 = randomNeuron(genome.genes, true)
 
    local newLink = newGene()
    if neuron一 <= Inputs and neuron二 <= Inputs then
        --Both input nodes
        return
    end
    if neuron二 <= Inputs then
        -- Swap output and input
        local temp = neuron一
        neuron一 = neuron二
        neuron二 = temp
    end
 
    newLink.into = neuron一
    newLink.out = neuron二
    if forceBias then
        newLink.into = Inputs
    end
 
    if containsLink(genome.genes, newLink) then
        return
    end
    newLink.innovation = newInnovation()
    newLink.weight = math.random()*四-二
 
    table.insert(genome.genes, newLink)
end
 
function nodeMutate(genome)
    if #genome.genes == 0 then
        return
    end
 
    genome.maxneuron = genome.maxneuron + 一
 
    local gene = genome.genes[math.random(一,#genome.genes)]
    if not gene.enabled then
        return
    end
    gene.enabled = false
 
    local gene一 = copyGene(gene)
    gene一.out = genome.maxneuron
    gene一.weight = 一.0
    gene一.innovation = newInnovation()
    gene一.enabled = true
    table.insert(genome.genes, gene一)
 
    local gene二 = copyGene(gene)
    gene二.into = genome.maxneuron
    gene二.innovation = newInnovation()
    gene二.enabled = true
    table.insert(genome.genes, gene二)
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(一,#candidates)]
    gene.enabled = not gene.enabled
end
 
function mutate(genome)
    for mutation,rate in pairs(genome.mutationRates) do
        if math.random(一,二) == 一 then
            genome.mutationRates[mutation] = 0.九五*rate
        else
            genome.mutationRates[mutation] = 一.0五二六三*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 - 一
    end
 
    p = genome.mutationRates["bias"]
    while p > 0 do
        if math.random() < p then
            linkMutate(genome, true)
        end
        p = p - 一
    end
 
    p = genome.mutationRates["node"]
    while p > 0 do
        if math.random() < p then
            nodeMutate(genome)
        end
        p = p - 一
    end
 
    p = genome.mutationRates["enable"]
    while p > 0 do
        if math.random() < p then
            enableDisableMutate(genome, true)
        end
        p = p - 一
    end
 
    p = genome.mutationRates["disable"]
    while p > 0 do
        if math.random() < p then
            enableDisableMutate(genome, false)
        end
        p = p - 一
    end
end
 
function disjoint(genes一, genes二)
    local i一 = {}
    for i = 一,#genes一 do
        local gene = genes一[i]
        i一[gene.innovation] = true
    end
 
    local i二 = {}
    for i = 一,#genes二 do
        local gene = genes二[i]
        i二[gene.innovation] = true
    end
 
    local disjointGenes = 0
    for i = 一,#genes一 do
        local gene = genes一[i]
        if not i二[gene.innovation] then
            disjointGenes = disjointGenes+一
        end
    end
 
    for i = 一,#genes二 do
        local gene = genes二[i]
        if not i一[gene.innovation] then
            disjointGenes = disjointGenes+一
        end
    end
 
    local n = math.max(#genes一, #genes二)
 
    return disjointGenes / n
end
 
function weights(genes一, genes二)
    local i二 = {}
    for i = 一,#genes二 do
        local gene = genes二[i]
        i二[gene.innovation] = gene
    end
 
    local sum = 0
    local coincident = 0
    for i = 一,#genes一 do
        local gene = genes一[i]
        if i二[gene.innovation] ~= nil then
            local gene二 = i二[gene.innovation]
            sum = sum + math.abs(gene.weight - gene二.weight)
            coincident = coincident + 一
        end
    end
 
    return sum / coincident
end
 
function sameSpecies(genome一, genome二)
    local dd = DeltaDisjoint*disjoint(genome一.genes, genome二.genes)
    local dw = DeltaWeights*weights(genome一.genes, genome二.genes) 
    return dd + dw < DeltaThreshold
end
 
function rankGlobally()
    local global = {}
    for s = 一,#pool.species do
        local species = pool.species[s]
        for g = 一,#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=一,#global do
        global[g].globalRank = g
    end
end
 
function calculateAverageFitness(species)
    local total = 0
 
    for g=一,#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 = 一,#pool.species do
        local species = pool.species[s]
        total = total + species.averageFitness
    end
 
    return total
end
 
function cullSpecies(cutToOne)
    for s = 一,#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/二)
        if cutToOne then
            remaining = 一
        end
        while #species.genomes > remaining do
            table.remove(species.genomes)
        end
    end
end
 
function breedChild(species)
    local child = {}
    if math.random() < CrossoverChance then
        g一 = species.genomes[math.random(一, #species.genomes)]
        g二 = species.genomes[math.random(一, #species.genomes)]
        child = crossover(g一, g二)
    else
        g = species.genomes[math.random(一, #species.genomes)]
        child = copyGenome(g)
    end
 
    mutate(child)
 
    return child
end
 
function removeStaleSpecies()
    local survived = {}
 
    for s = 一,#pool.species do
        local species = pool.species[s]
 
        table.sort(species.genomes, function (a,b)
            return (a.fitness > b.fitness)
        end)
 
        if species.genomes[一].fitness > species.topFitness then
            species.topFitness = species.genomes[一].fitness
            species.staleness = 0
        else
            species.staleness = species.staleness + 一
        end
        if species.staleness < 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 = 一,#pool.species do
        local species = pool.species[s]
        breed = math.floor(species.averageFitness / sum * Population)
        if breed >= 一 then
            table.insert(survived, species)
        end
    end
 
    pool.species = survived
end
 
 
function addToSpecies(child)
    local foundSpecies = false
    for s=一,#pool.species do
        local species = pool.species[s]
        if not foundSpecies and sameSpecies(child, species.genomes[一]) 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 = 一,#pool.species do
        local species = pool.species[s]
        calculateAverageFitness(species)
    end
    removeWeakSpecies()
    local sum = totalAverageFitness()
    local children = {}
    for s = 一,#pool.species do
        local species = pool.species[s]
        breed = math.floor(species.averageFitness / sum * Population) - 一
        for i=一,breed do
            table.insert(children, breedChild(species))
        end
    end
    cullSpecies(true) -- Cull all but the top member of each species
    while #children + #pool.species < Population do
        local species = pool.species[math.random(一, #pool.species)]
        table.insert(children, breedChild(species))
    end
    for c=一,#children do
        local child = children[c]
        addToSpecies(child)
    end
 
    pool.generation = pool.generation + 一
 
    writeFile("backup." .. pool.generation .. "." .. forms.gettext(saveLoadFile))
end
 
function initializePool()
    pool = newPool()
 
    for i=一,Population do
        basic = basicGenome()
        addToSpecies(basic)
    end
 
    initializeRun()
end
 
function clearJoypad()
    controller = {}
    for b = 一,#ButtonNames do
        controller["P一 " .. 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["P一 Left"] and controller["P一 Right"] then
        controller["P一 Left"] = false
        controller["P一 Right"] = false
    end
    if controller["P一 Up"] and controller["P一 Down"] then
        controller["P一 Up"] = false
        controller["P一 Down"] = false
    end
 
    joypad.set(controller)
end
 
if pool == nil then
    initializePool()
end
 
 
function nextGenome()
    pool.currentGenome = pool.currentGenome + 一
    if pool.currentGenome > #pool.species[pool.currentSpecies].genomes then
        pool.currentGenome = 一
        pool.currentSpecies = pool.currentSpecies+一
        if pool.currentSpecies > #pool.species then
            newGeneration()
            pool.currentSpecies = 一
        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 = 一
    local cell = {}
    for dy=-BoxRadius,BoxRadius do
        for dx=-BoxRadius,BoxRadius do
            cell = {}
            cell.x = 五0+五*dx
            cell.y = 七0+五*dy
            cell.value = network.neurons[i].value
            cells[i] = cell
            i = i + 一
        end
    end
    local biasCell = {}
    biasCell.x = 八0
    biasCell.y = 一一0
    biasCell.value = network.neurons[Inputs].value
    cells[Inputs] = biasCell
 
    for o = 一,Outputs do
        cell = {}
        cell.x = 二二0
        cell.y = 三0 + 八 * 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(二二三, 二四+八*o, ButtonNames[o], color, 九)
    end
 
    for n,neuron in pairs(network.neurons) do
        cell = {}
        if n > Inputs and n <= MaxNodes then
            cell.x = 一四0
            cell.y = 四0
            cell.value = neuron.value
            cells[n] = cell
        end
    end
 
    for n=一,四 do
        for _,gene in pairs(genome.genes) do
            if gene.enabled then
                local c一 = cells[gene.into]
                local c二 = cells[gene.out]
                if gene.into > Inputs and gene.into <= MaxNodes then
                    c一.x = 0.七五*c一.x + 0.二五*c二.x
                    if c一.x >= c二.x then
                        c一.x = c一.x - 四0
                    end
                    if c一.x < 九0 then
                        c一.x = 九0
                    end
 
                    if c一.x > 二二0 then
                        c一.x = 二二0
                    end
                    c一.y = 0.七五*c一.y + 0.二五*c二.y
 
                end
                if gene.out > Inputs and gene.out <= MaxNodes then
                    c二.x = 0.二五*c一.x + 0.七五*c二.x
                    if c一.x >= c二.x then
                        c二.x = c二.x + 四0
                    end
                    if c二.x < 九0 then
                        c二.x = 九0
                    end
                    if c二.x > 二二0 then
                        c二.x = 二二0
                    end
                    c二.y = 0.二五*c一.y + 0.七五*c二.y
                end
            end
        end
    end
 
    gui.drawBox(五0-BoxRadius*五-三,七0-BoxRadius*五-三,五0+BoxRadius*五+二,七0+BoxRadius*五+二,0xFF000000, 0x八0八0八0八0)
    for n,cell in pairs(cells) do
        if n > Inputs or cell.value ~= 0 then
            local color = math.floor((cell.value+一)/二*二五六)
            if color > 二五五 then color = 二五五 end
            if color < 0 then color = 0 end
            local opacity = 0xFF000000
            if cell.value == 0 then
                opacity = 0x五0000000
            end
            color = opacity + color*0x一0000 + color*0x一00 + color
            gui.drawBox(cell.x-二,cell.y-二,cell.x+二,cell.y+二,opacity,color)
        end
    end
    for _,gene in pairs(genome.genes) do
        if gene.enabled then
            local c一 = cells[gene.into]
            local c二 = cells[gene.out]
            local opacity = 0xA0000000
            if c一.value == 0 then
                opacity = 0x二0000000
            end
 
            local color = 0x八0-math.floor(math.abs(sigmoid(gene.weight))*0x八0)
            if gene.weight > 0 then 
                color = opacity + 0x八000 + 0x一0000*color
            else
                color = opacity + 0x八00000 + 0x一00*color
            end
            gui.drawLine(c一.x+一, c一.y, c二.x-三, c二.y, color)
        end
    end
 
    gui.drawBox(四九,七一,五一,七八,0x00000000,0x八0FF0000)
 
    if forms.ischecked(showMutationRates) then
        local pos = 一00
        for mutation,rate in pairs(genome.mutationRates) do
            gui.drawText(一00, pos, mutation .. ": " .. rate, 0xFF000000, 一0)
            pos = pos + 八
        end
    end
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("一\n")
                else
                    file:write("0\n")
                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, "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=一,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=一,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=一,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
 
            end
        end
    end
        file:close()
 
    while fitnessAlreadyMeasured() do
        nextGenome()
    end
    initializeRun()
    pool.currentFrame = pool.currentFrame + 一
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
        end
    end
 
    pool.currentSpecies = maxs
    pool.currentGenome = maxg
    pool.maxFitness = maxfitness
    forms.settext(maxFitnessLabel, "Max Fitness: " .. math.floor(pool.maxFitness))
    initializeRun()
    pool.currentFrame = pool.currentFrame + 一
    return
end
 
function onExit()
    forms.destroy(form)
end
 
writeFile("temp.pool")
 
event.onexit(onExit)
 
form = forms.newform(二00, 二六0, "Fitness")
maxFitnessLabel = forms.label(form, "Max Fitness: " .. math.floor(pool.maxFitness), 五, 八)
showNetwork = forms.checkbox(form, "Show Map", 五, 三0)
showMutationRates = forms.checkbox(form, "Show M-Rates", 五, 五二)
restartButton = forms.button(form, "Restart", initializePool, 五, 七七)
saveButton = forms.button(form, "Save", savePool, 五, 一0二)
loadButton = forms.button(form, "Load", loadPool, 八0, 一0二)
saveLoadFile = forms.textbox(form, Filename .. ".pool", 一七0, 二五, nil, 五, 一四八)
saveLoadLabel = forms.label(form, "Save/Load:", 五, 一二九)
playTopButton = forms.button(form, "Play Top", playTop, 五, 一七0)
hideBanner = forms.checkbox(form, "Hide Banner", 五, 一九0)
 
 
while true do
    local backgroundColor = 0xD0FFFFFF
    if not forms.ischecked(hideBanner) then
        gui.drawBox(0, 0, 三00, 二六, 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%五 == 0 then
        evaluateCurrent()
    end
 
    joypad.set(controller)
 
    getPositions()
    if marioX > rightmost then
        rightmost = marioX
        timeout = TimeoutConstant
    end
 
    timeout = timeout - 一
 
 
    local timeoutBonus = pool.currentFrame / 四
    if timeout + timeoutBonus <= 0 then
        local fitness = rightmost - pool.currentFrame / 二
        if gameinfo.getromname() == "Super Mario World (USA)" and rightmost > 四八一六 then
            fitness = fitness + 一000
        end
        if gameinfo.getromname() == "Super Mario Bros." and rightmost > 三一八六 then
            fitness = fitness + 一000
        end
        if fitness == 0 then
            fitness = -一
        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 = 一
        pool.currentGenome = 一
        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 + 一
            if genome.fitness ~= 0 then
                measured = measured + 一
            end
        end
    end
    if not forms.ischecked(hideBanner) then
        gui.drawText(0, 0, "Gen " .. pool.generation .. " species " .. pool.currentSpecies .. " genome " .. pool.currentGenome .. " (" .. math.floor(measured/total*一00) .. "%)", 0xFF000000, 一一)
        gui.drawText(0, 一二, "Fitness: " .. math.floor(rightmost - (pool.currentFrame) / 二 - (timeout + timeoutBonus)*二/三), 0xFF000000, 一一)
        gui.drawText(一00, 一二, "Max Fitness: " .. math.floor(pool.maxFitness), 0xFF000000, 一一)
    end
 
    pool.currentFrame = pool.currentFrame + 一
 
    emu.frameadvance();
end