H
horndude77
I know this is a bit late, but I've been trying to train my neural net.
I've never used them before and I've been wanting to learn. I created a
neural net with the input being the kalah board plus one bias node(so
15 input nodes), 20 hidden nodes, and 6 output nodes. A move is
selected by running the neural net and then picking the valid move
whose corresponding output node is highest. I'm training the net right
now using Particle Swarm Optimization to find good weights.
I trained it against dave's player and adam's remove right. It found a
pretty good solution against the remove right player, but not dave's.
The best I could get after searching for a while was losing by 12. I
might have better luck if I just take dave's solution and replace the
function it uses to rate the board's goodness with a neural net and
then train it. That might be fun for later.
Anyways sorry about the lateness, but here it is.
-----Horndude77
NeuralPlayer.rb (Sorry about the long section of weights. I had them on
one line each.):
=================================================
require 'NeuralNet'
class NeuralPlayer < Player
def initialize(name)
super(name)
@net = NeuralNet.new(15, 20, 6)
@num_weights = 15*20 + 20*6
#weights for RemoveRight
#self.weights = [-0.428456550403913, -0.909217917148082,
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0.344066370486408, 0.110887382259345, -0.112061654833158,
0.387531331474845, 0.253470699210902, 0.58235888221721,
0.306268984507146, 0.692431343604429, -0.0536485504199445,
1.25989522777867, -1.10462932704233, -0.351286944668706,
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-1.40103570953856, 0.60460561949975, -0.293757627893575,
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-0.0397911286451754, -2.61454740162978, 0.100948481882159,
0.649844494979625, -0.20679545186547, 0.956480816100499,
0.379654934947368, 0.213954601603671, 0.0528884967154502,
0.625382960895802, 0.362097750351292, -0.253497508633716,
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0.475091440306075, 0.354449966944016, -1.01613173478496,
0.676427027866194, 0.284847198728979, -0.841248187176398,
0.429200477632715, -0.326666975727536, -0.900577897928031,
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0.506840998984342, 0.103254060859946, 0.333803252464272,
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0.270777413929673, -0.0460416847164016, -0.107106555215344,
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-0.363234787452163, 0.0239440462433509, 0.906154588569019,
0.158870148658544]
#weights for Dave
self.weights = [0.595765520562772, 0.572313565308922,
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0.691711203231252, -0.142925100919743, -0.179032193873003,
0.0702627574729848, 0.050761109919964, -0.370460718365878,
0.242887469540777, 0.601316253626796, 1.18056456152599,
0.0130617818565887, 0.383274585308917, 0.678831957200485,
0.774370794216735, 0.272507059890609, -0.655792750040113,
0.769805400188241, -0.693486521873978, -0.754598879188519,
-0.074234513578388, 0.159284258659505, -0.450713615502174,
-0.390642496445363, -0.442808200102512, 0.267069977558354,
1.17025338402709, -0.214563443086823, -0.887455981648988,
-0.260310917181494, -0.00786283804287108, 0.247962909868963,
-0.362811078077127, 0.741310134065408, -0.200574130010101,
0.06615901401702, -1.44879735953932, -0.951059812759707,
0.32796539362041, 1.33352570257521, 0.202301767080624,
0.417550060347928, -0.513589660508719, 0.887169302020696,
-0.307274011792591, -0.532308937796632, -1.20959762064861,
0.45600602813945, -0.0874192202404007, 0.202709375017572,
0.17294847307896, -0.567443011220909, 0.440070114304498,
-0.0462495898214306, -1.05197180669229, 0.459254566465936,
0.311883378462867, -0.132864109735062, -0.353295045617759,
0.657360728811636, -0.00434468322735784, 0.25414344776134,
1.36859672044245, 0.537296612344187, 0.662805848878859,
-0.428961273880273, -0.510298293008916, 0.908502083520438,
0.543623178256533, 0.194510179239622, 0.150953472942602,
-0.723401233383223, 0.222253838102068, -0.252464263706322,
-0.320356082060333, 0.019418223582139, 0.904531241872239,
-0.458408804709521, -0.164916747460608, 0.504399574818756,
-0.0285722916383224, -0.666632449053057, 0.0531630039692571,
0.101723326754842, -0.768629611012825, -1.19955210039743,
-0.778246378153086, 0.0675714759482876, -0.396111692515287,
1.5613083872805, -0.494767892310519, 0.875913986718127,
0.249256338465321, -0.362941176741653, 0.937178999188274,
-1.3214093512053, -0.243068895971681, 0.179619525348131,
0.0273438374107876, 0.968372096980664, -0.209418553863336,
1.06698122835133, 0.112620291820417, 0.554996780348471,
0.34794240961505, -0.34321078192519, 0.513339264686877,
-1.24218990057805, 0.0172507054843588, 1.73545904209529,
-0.310201933026909]
end
def weights=(weights)
@net.weights = weights
end
def choose_move
#the board is the input to the neural net plus a bias node.
output = @net.run(@game.board+[1])
max_index = 0
max = -1000000
valid_moves.each do |m|
max_index, max = m, output[m] if output[m] > max
end
offset = @side == KalahGame::TOP ? 7 : 0
max_index + offset
end
def valid_moves
offset = @side == KalahGame::TOP ? 7 : 0
moves = []
6.times do |i|
moves << i if @game.legal_move?(i+offset)
end
moves
end
end
NeuralNet.rb:
==========================================
class NeuralNet
attr_reader :num_input, :num_hidden, :num_output, :weights
def initialize(num_input, num_hidden, num_output)
@num_input = num_input
@num_hidden = num_hidden
@num_output = num_output
@weights = Array.new(@num_input*@num_hidden +
@num_hidden*@num_output) { rand }
end
def weights=(weights)
@weights = weights
end
def run(inputs)
index = 0
hidden = Array.new(@num_hidden, 0)
inputs.each do |i|
@num_hidden.times do |h|
hidden[h] += @weights[index] * i
index += 1
end
end
output = Array.new(@num_output, 0)
hidden.each do |h|
@num_output.times do |o|
output[o] += @weights[index] * h
index += 1
end
end
output
end
end
I've never used them before and I've been wanting to learn. I created a
neural net with the input being the kalah board plus one bias node(so
15 input nodes), 20 hidden nodes, and 6 output nodes. A move is
selected by running the neural net and then picking the valid move
whose corresponding output node is highest. I'm training the net right
now using Particle Swarm Optimization to find good weights.
I trained it against dave's player and adam's remove right. It found a
pretty good solution against the remove right player, but not dave's.
The best I could get after searching for a while was losing by 12. I
might have better luck if I just take dave's solution and replace the
function it uses to rate the board's goodness with a neural net and
then train it. That might be fun for later.
Anyways sorry about the lateness, but here it is.
-----Horndude77
NeuralPlayer.rb (Sorry about the long section of weights. I had them on
one line each.):
=================================================
require 'NeuralNet'
class NeuralPlayer < Player
def initialize(name)
super(name)
@net = NeuralNet.new(15, 20, 6)
@num_weights = 15*20 + 20*6
#weights for RemoveRight
#self.weights = [-0.428456550403913, -0.909217917148082,
-0.468284431041651, 0.357903882475321, -0.104252998861521,
-0.460447451110633, 0.264105895124314, -0.220335064829405,
0.344066370486408, 0.110887382259345, -0.112061654833158,
0.387531331474845, 0.253470699210902, 0.58235888221721,
0.306268984507146, 0.692431343604429, -0.0536485504199445,
1.25989522777867, -1.10462932704233, -0.351286944668706,
-0.567170711584581, 1.29779740339143, 0.907906329763339,
-0.207925256569637, 0.177102138750371, 0.128961825966903,
-1.40103570953856, 0.60460561949975, -0.293757627893575,
0.519854674076599, -0.121247185426499, -0.48243591070721,
-0.5635586279583, 0.87760707848256, -0.24059588906072,
-0.0397911286451754, -2.61454740162978, 0.100948481882159,
0.649844494979625, -0.20679545186547, 0.956480816100499,
0.379654934947368, 0.213954601603671, 0.0528884967154502,
0.625382960895802, 0.362097750351292, -0.253497508633716,
-0.883702364891605, -0.101659606324204, 0.248216287934223,
-0.113052794219895, 0.634086308736, -0.302798853606374,
-0.346890845789382, -0.779510000549479, -0.0360852571092638,
0.0153516471477581, -0.171835179564683, 0.0251128237293062,
0.645490210802246, -0.193099922624216, 0.712786971652146,
-0.152956125676004, -0.616851260423588, 0.2459019745739,
-0.955383328688331, -0.364705013321598, -0.617848226340645,
0.475091440306075, 0.354449966944016, -1.01613173478496,
0.676427027866194, 0.284847198728979, -0.841248187176398,
0.429200477632715, -0.326666975727536, -0.900577897928031,
-0.151187352160431, 0.418136290801099, 0.0941511164912963,
0.506840998984342, 0.103254060859946, 0.333803252464272,
-2.06121470406907, -0.225537973154723, -0.233342939397305,
-0.102999506187284, -0.53850202703304, -0.555527143472232,
-0.671017075023332, -0.282380091302946, 0.131082542665543,
-0.723479705286975, 0.795054642785429, 0.491077298824963,
0.423059363779898, -2.29476738228994, -0.121696962009613,
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-0.891982913707634, 0.690166633881076, 2.20541381798063,
0.33841734898414, 1.34426349664742, 0.217566888046106,
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0.4399351958435, -1.07227081177901, 0.80071121984544,
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-0.100692469885383, 1.20483707939992, -0.347967500808309,
0.553050973560177, -0.0947739419530351, 0.491011977559244,
0.628874101325749, -1.95690538525791, 0.297814702564266,
-0.471007824648471, -0.414483183737145, 0.0726120056956718,
-0.933570106288705, 0.194825010200492, -0.332411723772583,
-0.454179722185871, -1.52057182606227, -0.743829261927187,
0.217180769977938, 1.23707542789918, 0.249214021285456,
0.980914083149354, 0.840109804787634, 0.0549821073482349,
0.759213641648435, 0.345571660732875, 0.642177051909433,
-0.699048329339318, -0.711124967241672, -0.198273084388503,
-0.682136345206517, 0.311305839135266, 0.631334457513039,
0.600316128422118, 0.119320030158148, -0.277536611595672,
0.152726604986164, -1.12883395672669, 0.685939108743903,
0.612340495493681, -0.470976215605829, 0.192660126498784,
-0.38101262950115, 0.692042726229392, 0.375075320693942,
-0.537093511777074, -0.75716836463867, 0.133598059764474,
-0.0990331086141189, -0.342974262988399, 0.148259446813639,
-0.307505302024212, -0.691169077958319, 0.160615435359153,
-0.473318586554398, -0.147746841313201, 0.384987460864245,
-1.71568495055048, 0.605786228599172, -0.331527288831919,
-0.742009170961669, -0.286044861721401, -0.197316839222173,
-0.652244349958852, -0.6931049458475, 0.606268100395642,
-0.816028396199571, -0.276575039831182, -0.965134498229981,
-0.758589133085266, -0.215462052762639, -1.22485716505581,
0.635854825774725, 0.0347106278236468, 0.68017020773746,
0.486510650159744, -0.950942640912604, -0.408793432853341,
0.0660212214118211, 0.439275058115052, -1.90764266527444,
0.0705340780324987, -0.579390528498766, -0.594227207465761,
-0.607485381621411, -0.745915297691384, 0.648556199264886,
0.424482268251714, 0.448565852678736, -0.212875620275073,
-1.60767121899908, 0.590280093295565, 0.220889024665176,
0.519830949320126, -1.58646652366069, 0.0490820218238098,
-0.939734916717465, 0.271622882831742, 0.587123430789783,
0.356941209321922, 0.431588738070851, -0.00363682276519776,
0.632624296908395, -0.460607536832767, -0.162344504807553,
0.244686235644648, 0.365983697367206, -0.556207060994067,
-0.247592686197143, -0.256153222456516, 1.42851969844773,
0.150973382582707, 0.217898142551229, 0.0327769935098895,
0.0301879317982763, 0.0504474882971441, 0.927135059762964,
-1.32001110030053, -0.0908289933296031, -0.649306861640546,
-0.409394034911994, 0.548916567397019, -0.816455346515919,
-0.23857172691294, 0.124546229435521, -0.557501169427238,
0.0367883579939846, 0.217641257520411, -0.370946458590141,
0.665435343143148, 0.795439121655607, -0.328009918280298,
-0.889705313261023, 0.528441781909589, -0.521804444678478,
-0.257741432376917, 0.28334033531291, -0.647816542299153,
-0.11188517833591, -0.382643376445593, -0.50836534761093,
-0.881302835589924, 0.451928929683854, 0.322905810792575,
-0.295616304054877, -0.0490996038261706, -0.419065683767976,
0.0889212390738239, 0.354441291440674, 0.533502165411728,
0.39775482176289, 0.522613557593291, 0.56763651037131,
-0.553985587928485, -0.0892770054346463, 0.417242403170033,
-0.0622231365237445, -0.536468658574459, -0.625611293373744,
0.397897575429255, -0.164522809437456, -0.76074160128093,
-0.338860069316338, 0.108424735798555, 0.72183978231058,
0.270777413929673, -0.0460416847164016, -0.107106555215344,
-1.16391449205179, 0.0287701615910848, 0.338030888059846,
0.916736090240755, -0.0946791015986226, -0.337775858656124,
-0.184221985273276, 0.7317875788664, -0.246207369460492,
0.627625049453152, -0.265781090911406, 0.250113034586153,
-0.203373686280343, -0.632653811240432, 0.96838789592563,
0.19063294685656, 1.17332675801791, -0.516224856380255,
-0.264875062177442, 0.50505882714235, -0.491935430100226,
0.4549665963441, -0.414740791398929, -0.571609921257171,
-0.265250663950144, -0.475686272333388, -1.07131641800336,
-0.168325437973922, -0.17234499168695, 1.2020474172808,
0.337248901596775, -0.634358591648102, -2.94504361685402,
-0.0474679493484615, -0.46289571515561, -0.149910278399775,
-0.56911680359925, 0.856479648671053, 0.0222252254438126,
-0.0789053965464129, -0.865175832880598, 0.813113040749056,
1.40052017545579, 0.247265188021256, -0.440075796418733,
0.917240725211066, 0.726954195590911, 0.573646173145207,
-0.336511408821544, -0.222735979722609, 0.879239872079174,
-0.733586265855175, -0.41900569195846, -0.293545971690515,
-0.632169580731648, -0.0856443553620738, -0.223316902854729,
0.403983130234464, -0.0986907442550533, 0.505726677775571,
0.544979171664306, -0.92863401861874, -0.583791809420349,
-0.682365608929785, -0.620485109687089, -0.0729610489595295,
1.67633914332691, 0.557908335099985, -0.654472279868246,
1.696774835487, 0.0502110469681493, 1.18420531528306,
-0.479767058178744, -1.14370493230831, 0.0504510683261292,
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-0.593650654967001, -1.10475004597524, -0.705061651554581,
0.560147802074617, 0.112770824037605, 0.308635801604607,
-0.139788519302014, -0.171711481459321, 0.0548073868984597,
0.622975084294081, -0.887766178428485, -0.0826250645225019,
-0.294980224893042, 0.324832701146147, -0.632388433547754,
-0.0743648174407521, 0.256966886000103, -0.348550409126788,
0.86243829650827, 0.110484229604374, -0.105573500449927,
-0.584517432238993, 0.119208851966649, 0.993143683984971,
-0.358145459385369, -0.0579403357513844, -0.965421816301397,
0.512560827725041, -1.42246421454656, 0.863912738871492,
1.04780140334956, -0.0646477189132773, -0.543296500968879,
0.271989197407765, -0.959413629549363, 0.280181611937799,
-0.363234787452163, 0.0239440462433509, 0.906154588569019,
0.158870148658544]
#weights for Dave
self.weights = [0.595765520562772, 0.572313565308922,
-1.01593258550904, -1.63154027608213, -0.242177969065067,
-1.44498150429411, -0.611346758262896, -0.714767503318283,
0.133920007475619, -0.0280544755220122, 0.805544561666266,
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end
def weights=(weights)
@net.weights = weights
end
def choose_move
#the board is the input to the neural net plus a bias node.
output = @net.run(@game.board+[1])
max_index = 0
max = -1000000
valid_moves.each do |m|
max_index, max = m, output[m] if output[m] > max
end
offset = @side == KalahGame::TOP ? 7 : 0
max_index + offset
end
def valid_moves
offset = @side == KalahGame::TOP ? 7 : 0
moves = []
6.times do |i|
moves << i if @game.legal_move?(i+offset)
end
moves
end
end
NeuralNet.rb:
==========================================
class NeuralNet
attr_reader :num_input, :num_hidden, :num_output, :weights
def initialize(num_input, num_hidden, num_output)
@num_input = num_input
@num_hidden = num_hidden
@num_output = num_output
@weights = Array.new(@num_input*@num_hidden +
@num_hidden*@num_output) { rand }
end
def weights=(weights)
@weights = weights
end
def run(inputs)
index = 0
hidden = Array.new(@num_hidden, 0)
inputs.each do |i|
@num_hidden.times do |h|
hidden[h] += @weights[index] * i
index += 1
end
end
output = Array.new(@num_output, 0)
hidden.each do |h|
@num_output.times do |o|
output[o] += @weights[index] * h
index += 1
end
end
output
end
end