-- MarI/O by SethBling rewritten for use with FCEUX by Akisame (used 3DI70R's version as a reference to save time but I have been told by him that he used juvester's script as a reference https://github.com/juvester/mari-o-fceux.)
-- I would like to stress that we tried to keep the algorithm as close to the version of SethBling as possible.
-- Pressing N will show the Neural net, pressing M will show the mutation rates, pressing L will load the last saved generation and pressing T will display the genome with the top fitness
-- This file includes all the fixes from my bizhawk version (https://pastebin.com/dKYFKg2E) and an extra fix to hopefully prevent the formation of a monospecies when we encounter a major bottleneck
-- If you want to start your own stream with this script I would appreciate it if you would include this file as well as SethBling's original version in the description
-- To use this script just download FCEUX and create a savestate in state 1 for smb. click file -> lua -> 'new lua script window' and load the script.
-- Have fun!
--FCEUX doesn't allow for user string input but if you press 'L' on your keyboard it should load the latest generation that was saved
--for loading specific generations replace nil with the filename as shown in the example below
savedpool = nil
--savedpool = 'backups/backup.429.SMB1-1.state.pool'
--savedpool = "backups/backup.5.SMB1-1.state.pool"
-- HUD options. These can be changed on the fly by pressing 'M' for the mutation rates and 'N' for the network
mutation_rates_disp = false
neural_net_disp = true
--set to false if you want warning popups before loading anything that might cause you to lose your current progress
nopopup = true
savestate_slot = 1 -- Load the level from FCEUX savestate slot
SAVE_LOAD_FILE = "SMB1-1.state.pool" --filename to be used
os.execute("mkdir backups") --create the folder to save the pools in
SavestateObj = savestate.object(savestate_slot)
ButtonNames = {
"A",
"B",
"up",
"down",
"left",
"right",
}
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
keyflag = false --used for turning the neural network on and off by pressin 'n'
StaleSpecies = 25 --increased slightly to allow for longer evolution
mariohole = false --flag for mario falling into a hole
mariopipe = false --flag for mario exiting a pipe
marioPipeEnter = false --new flag for entering a pipe
offset = 0 --offset value for when mario exits a pipe
timebonus = 0 --timebonus to prevent the hard drops in fitness when exiting a pipe
maxcounter = 0 --species that reach the max fitness
currentTracker = 0 --tracks location on screen
loop = false --flag for when a loop is detected
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 = 90 --set to a higher value but see for yourself what is acceptable
MaxNodes = 1000000
function getPositions()
marioX = memory.readbyte(0x6D) * 0x100 + memory.readbyte(0x86)
marioY = memory.readbyte(0x03B8)+16
mariostate = memory.readbyte(0x0E) --get mario's state. (entering exiting pipes, dying, picking up a mushroom etc etc)
currentscreen = memory.readbyte(0x071A) --finds current screen for loop detection
nextscreen = memory.readbyte(0x071B) --finds next screen
CurrentWorld = memory.readbyte(0x075F) --finds the current world (value + 1 is world)
CurrentLevel = memory.readbyte(0x0760) --finds the current level (0=1 2=2 3=3 4=4)
screenX = memory.readbyte(0x03AD)
screenY = memory.readbyte(0x03B8)
end
function getTile(dx, dy)
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
function getSprites()
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)+36 --changed this to stop sprites from floating in mid air on the neural network
sprites[#sprites+1] = {["x"]=ex,["y"]=ey}
end
end
return sprites
end
function getInputs()
getPositions()
sprites = getSprites()
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+16))
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
pool.maxcounter = 0 --counter for %max species reached
return pool
end
function newSpecies()
local species = {}
species.topFitness = 0
species.staleness = 0
species.genomes = {}
species.averageFitness = 0
return species
end
function getCurrentGenome()
local species = pool.species[pool.currentSpecies]
return species.genomes[pool.currentGenome]
end
function toRGBA(ARGB)
return bit.lshift(ARGB, 8) + bit.rshift(ARGB, 24)
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
emu.print("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 = 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 secondbestspecies() --added this to help prevent the formation of monospecies when MarI/O encounters a major bottleneck
local bestfitness = 0
local secondbestfitness = 0
for s = 1,#pool.species do
local species = pool.species[s]
if species.topFitness > bestfitness then
secondbestfitness = bestfitness
bestfitness = species.topFitness
elseif species.topFitness > secondbestfitness then
secondbestfitness = species.topFitness
end
end
return secondbestfitness
end
function removeStaleSpecies()
local survived = {}
local secondbest = secondbestspecies()
emu.print("Max Fitness: ".. pool.maxFitness ..". second best: ".. secondbest)
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 >= secondbest then --originally species.topFitness >= pool.maxFitness
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
pool.maxcounter = 0 --reset max fitness counter
writeFile("backups/backup." .. pool.generation .. "." .. SAVE_LOAD_FILE)
writelatestgen() --tracker for the latest generation
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[ButtonNames[b]] = false
end
joypad.set(1, controller)
end
function initializeRun()
savestate.load(SavestateObj);
rightmost = 0
pool.currentFrame = 0
timeout = TimeoutConstant
clearJoypad()
currentTracker = memory.readbyte(0x071A) --sets the current tracker to the current screen
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["left"] and controller["right"] then
controller["left"] = false
controller["right"] = false
end
if controller["up"] and controller["down"] then
controller["up"] = false
controller["down"] = false
end
joypad.set(1, 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)
gui.opacity(0.53)
if not genome then return end
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
gui.drawbox(215,32,245,82,toRGBA(0x80808080),toRGBA(0x00000000))
gui.opacity(0.85)
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 = 0xFF777777-- original color = 0xFF000000
end
gui.drawtext(225, 26+8*o, ButtonNames[o], toRGBA(color), 0x0) --moved slightly for better alignment
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, toRGBA(0x80808080), toRGBA(0xFF000000))
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,toRGBA(color), toRGBA(opacity))
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 = 0xF0000000
if pool.generation > 50 then
opacity = 0x80000000
end
if c1.value == 0 then
if pool.generation <10 then --slowly reducing the visibility of non active nodes
opacity = 0x80000000
elseif pool.generation < 15 then
opacity = 0x60000000
elseif pool.generation < 25 then
opacity = 0x40000000
end
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
if c1.value ~= 0 or pool.generation < 50 then -- remove non active nodes from generation 50 onwards
gui.drawline(c1.x+1, c1.y, c2.x-3, c2.y, toRGBA(color))
end
end
end
gui.drawbox(49,71,51,78,toRGBA(0x80FF0000),toRGBA(0x00000000))
if mutation_rates_disp then
local pos = 120
for mutation,rate in pairs(genome.mutationRates) do
gui.drawtext(16, pos, mutation .. ": " .. rate, toRGBA(0xFF000000), 0x0)
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() --used to save when a new max fitness is reached
local filename = SAVE_LOAD_FILE
writeFile(filename)
emu.print("saved pool due to new max fitness") --suggestion by DeltaLeeds
end
function loadFile(filename)
local file = io.open(filename, "r")
pool = newPool()
pool.generation = file:read("*number")
pool.maxFitness = file:read("*number")
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
--I am able to revert back to the original loading script with FCEUX
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 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
initializeRun()
pool.currentFrame = pool.currentFrame + 1
return
end
function writelatestgen() --used to write the generation tracker to a file
local file = io.open('backups/latestgen', "w")
file:write(pool.generation)
file:close()
end
function readlatestgen() --used to retrieve the latest generation from a file
local file = io.open('backups/latestgen', "r")
local latestgen = file:read()
file:close()
local name = 'backups/backup.'.. latestgen .. '.' .. SAVE_LOAD_FILE
print('loaded: '.. name)
return name
end
function keyboardinput()
local keyboard = input.get()
if keyboard['N'] and keyflag == false then
if neural_net_disp then
neural_net_disp = false
else
neural_net_disp = true
end
keyflag = true
end
if keyboard['M'] and keyflag == false then
if mutation_rates_disp then
mutation_rates_disp = false
else
mutation_rates_disp = true
end
keyflag = true
end
if keyboard['L'] and keyflag == false then
if not nopopup then
local loadyn = input.popup('Are you sure you want to load the last saved generation?')
end
if loadyn == 'yes' or nopopup then
name = readlatestgen()
loadFile(name)
end
keyflag = true
end
if keyboard['T'] and keyflag == false then
if not nopopup then
local loadyn = input.popup('Are you sure you want to show the topFitness genome?')
end
if loadyn == 'yes' or nopopup then
playTop()
end
end
if not (keyboard['N'] or keyboard['L'] or keyboard['M']) and keyflag == true then
keyflag = false
end
end
if savedpool then
loadFile(savedpool)
end
writeFile("temp.pool")
while true do
keyboardinput()
local backgroundColor = toRGBA(0xD0FFFFFF)
gui.drawbox(0, 0, 200, 30, backgroundColor, backgroundColor)
if pool.maxcounter == nil then
pool.maxcounter = 0
emu.print("caught missing variable")
end
local species = pool.species[pool.currentSpecies]
local genome = species.genomes[pool.currentGenome]
if neural_net_disp then
displayGenome(genome)
end
if pool.currentFrame%5 == 0 then
evaluateCurrent()
end
joypad.set(1, controller)
getPositions()
if mariostate == 2 or mariostate == 3 then --detects when mario enters a pipe
marioPipeEnter = true
end
if (CurrentWorld == 3 and CurrentLevel == 4) or (CurrentWorld == 6 and CurrentLevel == 4) or (CurrentWorld == 7 and CurrentLevel == 3) then
if currentTracker == currentscreen or currentTracker == nextscreen and not mariopipe then --follows the progress of mario
currentTracker = nextscreen
elseif currentTracker > nextscreen and not mariopipe and not marioPipeEnter and mariostate ~= 7 then --if mario suddenly goes back trigger loop detection
loop = true
elseif currentscreen == 0 and nextscreen == 0 and mariopipe and mariostate ~= 7 then -- if mario enters a piperoom negate loop detection
loop = false
currentTracker = nextscreen
elseif nextscreen >= (currentTracker - 1) and mariopipe and mariostate ~= 7 then -- if mario goes forward in the level by entering a pipe negate loop detection
loop = false
currentTracker = nextscreen
pipeexit = true
else --if nothing is true then we are in a loop
if not pipeexit then --prevents the extra one frame flicker of the tracker
loop = true
else
pipeexit = false
end
end
end
if marioPipeEnter == true and mariostate == 7 then --set mariopipe when exiting the pipe
mariopipe = true
end
if mariopipe == true and mariostate ~= 7 and rightmost > marioX and not mariohole then --calculate offset
offset = rightmost - marioX
mariopipe = false
marioPipeEnter = false
elseif mariopipe == true and mariostate ~= 7 and rightmost <= marioX and not mariohole then --if the exit coordinates are higher than rightmost after exiting a pipe set offset to 0
offset = 0
mariopipe = false
marioPipeEnter = false
end
if marioY + memory.readbyte(0xB5)*255 > 512 then --added the fallen into a hole variable that will activate when mario goes below screen
mariohole = true
end
if marioX + offset > rightmost and mariohole == false and loop == false and marioPipeEnter == false then
rightmost = marioX + offset
if TimeoutConstant - timeout > 20 then --this is required for 4-4 so walking left will not give a big penalty
timebonus = timebonus + (TimeoutConstant - timeout) * 13/20
end
timeout = TimeoutConstant
end
if marioPipeEnter == true then --freeze fitness and timer when mario enters a pipe
timeout = timeout + 1
timebonus = timebonus + 2/3
end
timeout = timeout - 1
local timeoutBonus = pool.currentFrame / 4
if timeout + timeoutBonus <= 0 then
local fitness = rightmost - pool.currentFrame / 2 +20 + timebonus
if fitness == 0 then
fitness = -1
end
genome.fitness = fitness
if fitness > pool.maxFitness then
pool.maxFitness = fitness
savePool()
end
if fitness > pool.maxFitness - 30 then
pool.maxcounter = pool.maxcounter + 1
end
emu.print("Gen " .. pool.generation .. " species " .. pool.currentSpecies .. " genome " .. pool.currentGenome .. " fitness: " .. fitness)
pool.currentSpecies = 1
pool.currentGenome = 1
while fitnessAlreadyMeasured() do
offset = 0 --offset in x coordinates for pipe
timebonus = 0 --used to freeze fitness
mariohole = false --reset the fallen into hole variable
mariopipe = false --is mario exiting a pipe or just teleporting
loop = false --is this a loop?
marioPipeEnter = false --has mario entered a pipe
currentTracker = 1 --tracker for the loop
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
gui.opacity(1) --made the top banner opaque. you can change this if you want a slightly transparent banner
gui.drawtext(5, 11, "Gen " .. pool.generation .. " species " .. pool.currentSpecies .. " genome " .. pool.currentGenome .. " (" .. math.floor(measured/total*100) .. "%)", toRGBA(0xFF000000), 0x0)
gui.drawtext(5, 20, "Fitness: " .. math.floor(rightmost - (pool.currentFrame) / 2 + - (timeout + timeoutBonus)*2/3 +20 + timebonus), toRGBA(0xFF000000), 0x0) --the (timeout + timeoutBonus)*2/3 makes sure that the displayed fitness remains stable for the viewers pleasure
gui.drawtext(80, 20, "Max Fitness:" .. math.floor(pool.maxFitness).. " ".. "(" .. math.ceil(pool.maxcounter/Population*100) .. "%)", toRGBA(0xFF000000), 0x0)
pool.currentFrame = pool.currentFrame + 1
emu.frameadvance();
end
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