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)
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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 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 end 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 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 end return sprites end 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 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*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 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 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["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 end 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 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 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 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 > 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 + 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() < 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 = 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 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() < 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 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 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 < 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 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 > 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 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 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 > 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 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 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 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("1\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=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 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 > 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 + 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 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*100) .. "%)", 0xFF000000, 11) gui.drawText(0, 12, "Fitness: " .. math.floor(rightmost - (pool.currentFrame) / 2 - (timeout + timeoutBonus)*2/3), 0xFF000000, 11) gui.drawText(100, 12, "Max Fitness: " .. math.floor(pool.maxFitness), 0xFF000000, 11) end pool.currentFrame = pool.currentFrame + 1 emu.frameadvance(); end |
"NEAT" Paper: http://nn.cs.utexas.edu/downloads/pap...
最新评论
Lua
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这是什么语言写的啊
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mark
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已经有收藏功能啦
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怎么运行?有木有注释
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看了代码,瞬间觉得我还是看报表去吧!!!
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