Al tener la oportunidad de evaluar cualitativamente la situación en el juego en algún momento y la capacidad de simular el mundo del juego al crear un bot para una de las soluciones, solo queda esforzarse por realizar acciones que conduzcan a una mejora en esta evaluación en el futuro cercano.
Función de estimación de posición: devuelve un valor de material donde menos significa peor. A la entrada de tal función, presenté solo el vector de posición y velocidad de la pelota. Inicialmente, esta función se implementó con fórmulas bastante simples y un par de ifs. Sin embargo, esto proporcionó una buena base para hacer trampa en el conjunto de registros localrunner para el entrenamiento posterior de la red neuronal. Así que desplacé 300 juegos (18,000 ticks cada uno) localmente, que en total produjeron alrededor de 12GB de registros y más 145 de los registros de juegos principales se descargaron del servidor (5.7GB).
Luego, fue necesario aislar las muestras de entrenamiento y prueba de estos registros. Hice esto de la siguiente manera: a partir de un gol, miré el "pasado" durante 300 ticks (5 segundos) y en incrementos de 5 ticks, cada posición y velocidad de la pelota + tomó un puntaje de referencia como ejemplo.
Un punto importante: la puntuación de referencia (salida) aquí fue calculada por la fórmula
$$ display $$ O = S / exp (T / 60) $$ display $$
donde S = -1 si la pelota vuela hacia "mi" gol y 1 en caso contrario, y T es el tiempo restante en ticks antes del gol.
Otro punto menos importante, pero también importante: el campo de juego es simétrico y, en consecuencia, la puntuación de referencia también debe ser inversamente simétrica cuando se ve desde el punto de vista del oponente. Es decir si algo se evalúa desde el punto de vista "mi" como X, entonces se debe evaluar la misma posición desde el punto de vista del adversario como -X. Esto significa que si "dobla por la mitad" todo el espacio de entrada de la red neuronal por cualquier parámetro, la red aprenderá mejor, relativamente hablando, "2 veces", y lo más importante, le dará una respuesta simétrica garantizada (que es, al menos, simplemente hermosa). "Doblé" la velocidad de la pelota a lo largo del eje Z. En pocas palabras, si la pelota vuela desde "mi" objetivo, miro desde mi "propio" punto de vista, de lo contrario, desde el punto de vista del oponente. Resulta que para una red neuronal la pelota siempre vuela en una dirección positiva a lo largo de Z. Lo mismo puede hacerse para la simetría longitudinal (a lo largo del eje X), aunque en este caso seguimos mirando desde el punto de vista del mismo equipo, pero, por así decirlo, en el espejo ubicado en planos con un normal (1, 0, 0).
Entonces, aquí está el código para preparar una prueba y una muestra de entrenamiento de los registros en Python:
import json from pprint import pprint import glob import numpy as np import random xtrain = [] ytrain = [] xtest = [] ytest = [] f1 = r"F:\Home\Projects\MailRuAI\Codeball2018\LocalRunner\logs_archive\logs_01/*.txt" f2 = r"F:\Home\Projects\MailRuAI\Codeball2018\LocalRunner\logs_archive\logs_02/*.txt" f3 = r"F:\Home\Projects\MailRuAI\Codeball2018\LocalRunner\logs_archive\logs_03/*.txt" f7 = r"F:\Home\Projects\MailRuAI\Codeball2018\downloaded_games/*.txt" for file in (glob.glob(f1) + glob.glob(f2) + glob.glob(f3) + glob.glob(f7)): with open(file) as f: content = f.readlines() print(len(content)) print(file) sumofscores = 0 lastscore0 = 0 lastscore1 = 0 ticksbackward = 300 ticksbackwardinc = 5 for x in range(0, len(content)): data = json.loads(content[x]) if "scores" in data and sum(data["scores"]) > sumofscores: sumofscores = sum(data["scores"]) value = 0 if data["scores"][0] > lastscore0: lastscore0 = data["scores"][0] value = 1 if data["scores"][1] > lastscore1: lastscore1 = data["scores"][1] value = -1 for y in range(ticksbackwardinc, ticksbackward, ticksbackwardinc): dataY = json.loads(content[x - y]) if "scores" in dataY and sum(dataY["scores"]) == sumofscores - 1: sign = 1 if dataY['ball']['velocity']['z'] < 0: sign = -1 signX = 1 if dataY['ball']['velocity']['x'] * sign < 0: signX = -1 inputs = np.zeros(6) inputs[0] = dataY['ball']['velocity']['x'] * sign * signX inputs[1] = dataY['ball']['velocity']['y'] inputs[2] = dataY['ball']['velocity']['z'] * sign inputs[3] = dataY['ball']['position']['x'] * sign * signX inputs[4] = dataY['ball']['position']['y'] inputs[5] = dataY['ball']['position']['z'] * sign outputs = np.zeros(2) outputs[0] = value*sign outputs[1] = y if (random.random() > 0.2): xtrain.append(inputs) ytrain.append(outputs) else: xtest.append(inputs) ytest.append(outputs) else: print("exceeded") print(len(xtrain)) print(len(xtest)) np.save("F:/Home/Projects/MailRuAI/Codeball2018/nnet/xtrain_BR.npy", np.asarray(xtrain)) np.save("F:/Home/Projects/MailRuAI/Codeball2018/nnet/ytrain_BR.npy", np.asarray(ytrain)) np.save("F:/Home/Projects/MailRuAI/Codeball2018/nnet/xtest_BR.npy", np.asarray(xtest)) np.save("F:/Home/Projects/MailRuAI/Codeball2018/nnet/ytest_BR.npy", np.asarray(ytest))
Los más atentos probablemente ya hayan notado que las salidas contienen dos salidas y nada de lo que describí anteriormente, pero no se preocupe, esto es un rudimento y la transformación sigue antes del entrenamiento en sí:
import numpy as np from keras.datasets import boston_housing from keras.models import Model, Sequential from keras.layers import Input, Dense, Concatenate, Add import random import datetime np.set_printoptions(edgeitems=50) xtrain = np.load("F:/Home/Projects/MailRuAI/Codeball2018/nnet/xtrain_BR.npy") ytrain = np.load("F:/Home/Projects/MailRuAI/Codeball2018/nnet/ytrain_BR.npy") xtest = np.load("F:/Home/Projects/MailRuAI/Codeball2018/nnet/xtest_BR.npy") ytest = np.load("F:/Home/Projects/MailRuAI/Codeball2018/nnet/ytest_BR.npy") ytrain = np.exp(-(ytrain[:,1])/60) * ytrain[:,0] ytest = np.exp(-(ytest[:,1])/60) * ytest[:,0] inp = Input(shape=(xtrain.shape[1],)) d1 = Dense(6, activation='relu')(inp) d2 = Dense(6, activation='linear')(inp) d3 = Dense(6, activation='sigmoid')(inp) added = Concatenate()([d1, d2, d3]) d21 = Dense(3, activation='relu')(added) d22 = Dense(3, activation='linear')(added) d23 = Dense(3, activation='sigmoid')(added) added2 = Concatenate()([d21, d22, d23]) d31 = Dense(3, activation='relu')(added2) d32 = Dense(3, activation='linear')(added2) d33 = Dense(3, activation='sigmoid')(added2) added3 = Concatenate()([d31, d32, d33]) out = Dense(1)(added3) model = Model(inputs=inp, outputs=out) model.compile(optimizer='adam', loss='mse', metrics=['mae'])
¿Por qué no preguntar exactamente 3 capas internas y solo esa configuración? No me conozco. Sin embargo, la intuición y los días de experimentos condujeron precisamente a eso.
Y finalmente, la última pregunta, ¿cómo usar una red neuronal ya entrenada en Python en C # sin tener clases preparadas? ¡Crea una clase! Con una configuración tan simple de la red neuronal y considerando que solo necesitamos implementar la función de "predicción" (es decir, solo barrer de entrada a salida), es bastante simple. Aquí esta:
public enum Activation { relu, linear, sigmoid }; public class layer { public int Count = 0; public List<List<double>> weights = new List<List<double>>(); public List<double> Ps = new List<double>(); public List<Activation> funcs = new List<Activation>(); public List<double> Values = new List<double>(); public void Add(Activation aact) { Count++; weights.Add(new List<double>()); Ps.Add(0); funcs.Add(aact); Values.Add(0); } public void Add(Activation aact, int acnt) { for (int i = 0; i < acnt; i++) Add(aact); } public void Calculate(List<double> ainps) { for (int i = 0; i < Count; i++) { Values[i] = Ps[i]; for (int j = 0; j < ainps.Count; j++) Values[i] += weights[i][j] * ainps[j]; switch (funcs[i]) { case Activation.linear: break; case Activation.relu: Values[i] = System.Math.Max(0, Values[i]); break; case Activation.sigmoid: Values[i] = (double)(1.0 / (1.0 + System.Math.Exp(-Values[i]))); break; } } } } public class nnet { public int inputCount = 0; public List<layer> layers = new List<layer>(); public layer outputLayer = null; public nnet(int ainputcount, int aoutputcount) { inputCount = ainputcount; outputLayer = new layer(); outputLayer.Add(Activation.linear, aoutputcount); } public List<double> predict(List<double> ainput) { for (int i = 0; i < layers.Count + 1; i++) { List<double> inps = ainput; if (i > 0) inps = layers[i - 1].Values; layer lr = outputLayer; if (i < layers.Count) lr = layers[i]; lr.Calculate(inps); } return outputLayer.Values; } }
Solo queda apretar los pesos de la red entrenada (por cierto, cito los pesos aquí que realmente funcionan en mi última versión):
public class trained_nnet : nnet { void FillLayer(layer al, double[] atp, double[,] atw) { al.Ps.Clear(); al.Ps.AddRange(atp); al.weights.Clear(); for (int i = 0; i < atw.GetLength(0); i++) { al.weights.Add(new List<double>()); for (int j = 0; j < atw.GetLength(1); j++) { al.weights[i].Add(atw[i, j]); } } } public trained_nnet() : base(6, 1) { layer lr1 = new layer(); lr1.Add(Activation.relu, 6); lr1.Add(Activation.linear, 6); lr1.Add(Activation.sigmoid, 6); base.layers.Add(lr1); layer lr2 = new layer(); lr2.Add(Activation.relu, 3); lr2.Add(Activation.linear, 3); lr2.Add(Activation.sigmoid, 3); base.layers.Add(lr2); layer lr3 = new layer(); lr3.Add(Activation.relu, 3); lr3.Add(Activation.linear, 3); lr3.Add(Activation.sigmoid, 3); base.layers.Add(lr3); double[] t = { 3.6843767166137695, -9.454026222229004, -5.089229106903076, -2.850287437438965, -6.96286153793335, -9.751116752624512, 10.384811401367188, -4.214056968688965, 1.2072025537490845, 1.4019242525100708, -0.13174889981746674, -13.1264066696167, -4.265004634857178, 1.8926845788955688, -0.0813497006893158, -1.4616785049438477, -5.361510753631592, -1.1896661520004272 }; double[,] t2 = { { 0.1477939784526825, 0.03613739833235741, -0.09796690940856934, 1.942456841468811, -0.3508949875831604, -0.5551134347915649 }, { -0.25495094060897827, 0.049018844962120056, -0.15976546704769135, -1.881699562072754, -1.3928385972976685, 0.017490295693278313 }, { 0.314727246761322, -0.7985705733299255, -0.16902890801429749, 0.7290273308753967, -3.3613057136535645, -0.501738965511322 }, { -0.14706645905971527, 0.013889106921851635, -8.41325855255127, 0.08269797265529633, -0.8194255232810974, 0.054869525134563446 }, { -0.11769858002662659, 0.024719441309571266, -32.9736213684082, -0.06565750390291214, -0.38925793766975403, -0.30816638469696045 }, { -0.09536012262105942, -0.4411015212535858, -0.3092011511325836, 0.061532989144325256, -1.3718899488449097, -0.9904148578643799 }, { 0.03862301632761955, -0.2239271104335785, -0.3054073452949524, 0.013336590491235256, -0.0404842384159565, -0.09027290344238281 }, { -0.317527711391449, -0.14433158934116364, 0.06079907342791557, -0.4572157561779022, 0.2782846987247467, 0.17747753858566284 }, { 0.01980031281709671, 0.015361669473350048, -0.03606397658586502, 0.013219496235251427, -0.03483833745121956, -0.01729537360370159 }, { -0.003958317916840315, 0.09587077051401138, -0.08213665336370468, -0.027169639244675636, 0.032037656754255295, -0.030492693185806274 }, { -0.04885690286755562, -0.06349656730890274, 0.013905149884521961, 0.018028201535344124, 0.012719585560262203, 0.002531017642468214 }, { 0.016520477831363678, -0.00018591046682558954, -0.003657651599496603, 0.06888063997030258, -0.2127065807580948, 0.6427022218704224 }, { -0.5308891534805298, 0.13539844751358032, 0.03864796832203865, 1.5582681894302368, -1.929693341255188, -3.2511842250823975 }, { 0.032178860157728195, 1.1472656726837158, -2.020042896270752, -0.05141841620206833, -0.4635908901691437, 0.2636871039867401 }, { 0.01480827759951353, 0.33971744775772095, -0.15343432128429413, 0.03558071702718735, 3.364596366882324, -0.7852638959884644 }, { 0.0028303645085543394, 1.2297841310501099, -0.4412313997745514, 0.3644706606864929, 2.2155861854553223, -0.43303439021110535 }, { -0.3666411340236664, 0.0464097335934639, 5.143652439117432, -2.2230076789855957, 0.3511424660682678, 1.0514445304870605 }, { 0.014482858590781689, -0.4740144610404968, -1.6240901947021484, 1.7327706813812256, -1.5116417407989502, -1.6811648607254028 } }; double[] t3 = { -3.09689998626709, -1.2031112909317017, -7.121585369110107, 2.0653932094573975, -2.8601508140563965, -1.6219528913497925, 0.16301754117012024, -6.890131950378418, 3.8225107192993164 }; double[,] t4 = { { -0.6246452927589417, -0.3575346767902374, 0.6897052526473999, -2.2513232231140137, -0.23217444121837616, 0.17847181856632233, -0.3863859176635742, -0.01201619766652584, 0.050539981573820114, 0.028343766927719116, 0.0034856200218200684, 0.5547005534172058, -0.4277774691581726, -1.0249099731445312, -8.995088577270508, -3.4937169551849365, 0.7673622369766235, -1.6504380702972412 }, { -1.0006977319717407, -0.8660659790039062, -0.0415676049888134, -0.5476861000061035, -0.7828258872032166, -0.05350146442651749, 0.005586389917880297, -0.052493464201688766, 0.07955628633499146, -0.08084911853075027, 0.09794406592845917, -0.031214063987135887, -0.7785998582839966, -0.27977627515792847, -0.4096711277961731, -0.24633635580539703, -1.5932326316833496, -0.5430923104286194 }, { -0.2330777496099472, -0.07477551698684692, -1.0634428262710571, -1.772096872329712, -1.4657013416290283, 0.6256936192512512, -0.1179097518324852, 0.07645376771688461, 0.008837736211717129, 0.030952733010053635, -0.013960030861198902, 1.0339184999465942, 0.20350944995880127, -0.047291483730077744, -4.043337345123291, -0.7629795670509338, -5.41167688369751, -3.7755305767059326 }, { 0.00979659240692854, 0.11435728520154953, -0.4749748706817627, 1.5166815519332886, -5.3047380447387695, 0.9597445130348206, 0.08123911172151566, 0.039479970932006836, -0.01649349369108677, -0.04941410943865776, 0.020120851695537567, -0.16329358518123627, 0.36106961965560913, 0.5348165035247803, 0.11825983971357346, 0.2075480818748474, -1.8661850690841675, 1.4093444347381592 }, { -0.35534173250198364, 0.3471201956272125, -0.2657061517238617, -2.4178225994110107, -3.890836238861084, 0.5999298691749573, -0.10068143904209137, 0.530009388923645, 0.023632165044546127, -0.006245455238968134, 0.031124670058488846, 0.016797777265310287, 1.720144510269165, -0.3200121223926544, 0.17827671766281128, -1.0847045183181763, 0.7679504156112671, 1.1521148681640625 }, { 0.047243088483810425, -0.07313758134841919, -0.13496115803718567, -1.0498348474502563, -2.083388328552246, 0.3018227815628052, 0.019016921520233154, 0.00780009850859642, -0.02416112646460533, -0.012299800291657448, 0.019720694050192833, 0.019809948280453682, -1.637327790260315, 0.09307140856981277, 2.963168144226074, 0.515803337097168, 0.02399904653429985, -3.9851980209350586 }, { -0.6250298023223877, -0.4796958863735199, 0.4311320185661316, -1.4590528011322021, -4.861763000488281, -1.1894060373306274, 0.31154727935791016, -0.028901753947138786, 0.07241783291101456, 0.0573900043964386, -0.16387903690338135, -0.7621306777000427, 2.864539623260498, 1.126343011856079, -0.729159414768219, 15.2516450881958, -0.5845442414283752, -0.2593745291233063 }, { -0.4520488679409027, -0.37348034977912903, -0.22873088717460632, 2.816544532775879, 0.635391891002655, 1.7192658185958862, -0.042334891855716705, -0.012391769327223301, -0.00944773480296135, -0.047271229326725006, 0.045244403183460236, 1.1044175624847412, -2.682516098022461, -1.797003984451294, -5.227936744689941, 0.3994572162628174, -3.361297130584717, -0.16535422205924988 }, { 1.3437395095825195, 0.05596136301755905, -0.6534030437469482, -3.2173333168029785, -3.256056785583496, 3.164973020553589, -0.6149216294288635, 0.3425371050834656, -0.13111716508865356, -0.42127469182014465, -0.0668950155377388, 0.19484268128871918, 2.005012273788452, -3.41219425201416, -0.3146309554576874, -2.1181774139404297, 2.2965285778045654, 5.287317276000977 } }; double[] t5 = { -1.173705816268921, -1.8888208866119385, -2.566594123840332, 0.1278465986251831, 0.05948356166481972, -0.021375492215156555, -1.554726243019104, -2.2256762981414795, 1.3142614364624023 }; double[,] t6 = { { -0.023421021178364754, 0.17735084891319275, -0.1922600418329239, -0.11634820699691772, 0.05003879591822624, 0.07409390062093735, -0.131203755736351, -0.11743484437465668, -1.1311017274856567 }, { -0.6256148219108582, -0.08678799867630005, 0.08910120278596878, -0.06354714930057526, 0.05225379019975662, 0.028936704620718956, -2.069547176361084, 0.16652414202690125, 0.4840211570262909 }, { -0.9266191720962524, 0.1542767435312271, -1.511458396911621, -2.2593629360198975, 0.32768234610557556, 0.728438138961792, 1.4113644361495972, -2.9423279762268066, -1.1225157976150513 }, { -0.31864309310913086, -0.06739992648363113, 1.8643943071365356, 0.12609687447547913, 0.003282073885202408, -0.08565603941679001, 0.22951357066631317, -3.9096572399139404, -0.5148558020591736 }, { 0.0030701414216309786, 0.22653144598007202, -0.1772366166114807, 0.01472154725342989, 0.006688127294182777, 0.029435427859425545, -0.049562305212020874, -0.01126908790320158, -0.09357477724552155 }, { -0.003160204039886594, 0.004133348818868399, 0.003914407920092344, 0.013578329235315323, 0.0036796496715396643, 0.028364477679133415, 0.025828130543231964, -0.030584659427404404, -0.0449080727994442 }, { -0.15649960935115814, 0.7045242786407471, 4.971825122833252, 0.26150253415107727, 0.25615766644477844, -0.007457265630364418, 0.4002840220928192, -4.386100769042969, -0.14405106008052826 }, { -1.283564805984497, -1.0451316833496094, -9.010445594787598, -0.23629669845104218, 0.8792487978935242, 0.12951965630054474, 2.7414908409118652, -10.04093074798584, 0.08805646747350693 }, { 0.5142691731452942, 0.27933982014656067, 17.242839813232422, -0.14753387868404388, 0.35601550340652466, -0.03304799646139145, -0.3745580017566681, 3.6696081161499023, 0.18306805193424225 } }; double[] t7 = { 0.057645831257104874 }; double[,] t8 = { { 0.02502649463713169, 0.030625218525528908, -0.04921620339155197, -0.06382419914007187, -0.0018273837631568313, -0.002946096006780863, -0.3073849678039551, -0.0770358145236969, 0.44145819544792175 } }; FillLayer(lr1, t, t2); FillLayer(lr2, t3, t4); FillLayer(lr3, t5, t6); FillLayer(outputLayer, t7, t8); } }
Llamada de red neuronal:
public double StateRatingByNNet() { double result = 0; List<double> xdata = new List<double>(); double sign = 1; if (ball.velocity.Z < 0) sign = -1; double signX = 1; if (ball.velocity.X * sign < 0) signX = -1; xdata.Add(ball.velocity.X * sign * signX); xdata.Add(ball.velocity.Y); xdata.Add(ball.velocity.Z * sign); xdata.Add(ball.position.X * sign * signX); xdata.Add(ball.position.Y); xdata.Add(ball.position.Z * sign); List<double> o = nnet.predict(xdata); return result + o[0] * sign; }
Gracias por su interes!