def AI_loop():
#Release keys
ai.thrust(0)
ai.turnLeft(0)
ai.turnRight(0)
ai.setTurnSpeed(45)
sendData = getSendData()
output = getOutput(sendData, 12, 5, 2, weight)
turn, thrust = "N", "N"
if output[0] >= .55:
ai.turnRight(1)
turn = "R"
elif output[0] < .45:
ai.turnLeft(1)
turn = "L"
ai.setTurnSpeed(abs(output[0]-.5)*100)
if output[1] > random():
ai.thrust(1)
thrust = "Y"
if ai.selfAlive():
print (turn +" "+ str(round(output[0],3)) +" | "+ thrust +" "+ str(round(output[1],3)))
def AI_loop():
#Release keys
ai.thrust(0)
ai.turnLeft(0)
ai.turnRight(0)
ai.setTurnSpeed(45)
#noshoot simple2 v1 - possibly broken
#weight = [[0.7493857316521443, 0.13368056995306654, -0.47767010506248414, 0.36303490376111747, 0.3699402939846066, -0.24513421555956436, 0.6049213422682447, -1.002771350490762, 0.04035249957935825, -0.28434589370049973, 0.06045481719428707, -0.7059773487995405], [0.4597751718875, 0.48527265677711917, 1.1633270644302531, -0.2617787944640836, 0.43969814249154665, 0.39105202732983013, -0.4842763268769806, 0.0139642821717396, 0.6316767202549712, -0.7261062652316246, 0.21262210805420517, 0.5641099350636366], [-0.3874172140692371, 0.7651295146994167, 1.0395098788413182, 1.186226846675026, -0.025235379424306762, 0.2946735626872294, 0.8189904988668917, -0.3112836835374087, 0.4561011502092862, -1.117805735032487, -0.43961810490175446, -0.4119472393430895], [0.22275511634133816, -0.6648437205568158, -0.8414740640064268, 0.8056440111521608, 0.015267716015499746, 0.41334996924272127, 0.4081963555038903, -0.1419511839287509, 0.45361416054410414, -0.8794849582102071, -0.3468746833944422, 0.5401415708615188], [0.5074970117306209, 0.5819675141877677, 0.6517899284876814, 0.46691400605473293, 0.32604510520447944, 0.5968926122763236, 0.5310482664503053, -0.680392116654488, 0.8673231237747582, 0.33285892719317295, 0.8399077545012879, 0.6164215093257798], [-0.372562013299741, -0.19652620269354013, 0.1437357080878078, 0.4658598892474184, 0.2622911587442708, -0.0978653491300881], [-0.8758502232674676, -0.2171515591378693, -2.993752695121232, -0.35299785185297505, 0.23130479682141922, -0.3366867315248932]]
#same
#weight = [[-0.5615458044552419, 0.18357035281974562, 0.03349259503126993, 0.7384951638825633, -0.30798810007905386, 0.30573186272807085, 0.854959406907431, -0.7415861859492735, -0.7738387875893121, 0.02855354677310282, -0.851478966174285, -0.4098017778685181], [-0.207370684933253, -0.013364767376613134, -0.9163625758710611, 0.7147484720369136, 0.4332939609435104, 0.08179173241171495, -0.7872472562071717, -0.028481692524635203, 0.9018021433254303, 0.44174414866442663, -0.9254417448428363, -0.8782722048252558], [0.33830929947647237, 0.3662059060118147, 0.3811711776833437, 0.24042392561994674, 0.5277499126295441, -0.0004866655690337176, 0.25640579185352463, -0.7633986561071578, -0.612156341670209, 0.8230711072150679, 0.760953671186351, -0.2573796666700781], [-0.504034389497162, -0.5579761993837166, -0.5804468446739354, 0.20559197382639358, 0.2633102441141902, 0.6523171620389138, -0.024442707325193276, 0.20991650483980787, 0.8242995112997102, -0.310268650118638, 0.6134846337134139, -0.1333594123873779], [-0.1482061081648374, -0.8156028525698128, 0.5620698705963493, 0.3369535386843364, 0.4950101349517008, 0.632041030208815, 0.2282786987810209, 0.5886322102158713, 0.1479714498784525, -0.35843514023521145, 0.7822417167093876, -0.6863908492221918], [0.19475966937630487, 0.3329038845401951, -0.4194515125286082, -1.0059783572483236, 0.4401534681618835, -0.14131035905437314], [-0.1489506388852606, -0.7142411448348961, 0.19491176823686687, -0.7258125795197734, -0.5016315182547347, 1.187683861246764]]
#noshoot simple2 v1
#weight = [[4.023914862289385, -11.939688383204079, -15.825568188856662, -8.91142185823263, 2.641203914119205, -0.5129014819329881, 0.9220419943089314, 0.3895906088166904, -3.314207628630927, 1.467458787811278, 1.0256723985314264, 6.790853759075572], [0.46927642890835647, 0.4119664313516612, 1.0805195819839035, -22.587191583019706, -2.0569865767257087, 1.0122406403957074, 0.7655020473448081, 0.5363534804959299, -0.09153880599832058, 1.742638802899449, 0.604362216981221, -5.437549122082703], [-7.860553953097792, 8.381751807757572, 16.129268910929408, 0.8192147369369929, 12.621334155351937, 1.1871517887014937, 1.3270695309760896, 1.0735505581082165, -2.1305919492192547, 0.8033262515099523, 3.3317152348735313, -1.1046970930734303], [0.4398449547641411, 1.912802187200376, 1.5190820053279013, 0.05054235950852018, 20.89903152269866, -5.003395008994748, -2.6880139668653324, -7.882075146152547, 7.312459204984362, 0.7725581247271933, -3.2005214579831134, 8.556187322708842], [-1.293677418426306, 1.0762585514923537, 2.9472553959491394, -2.8811018920854745, 20.39410027291124, 3.5575387211971545, -4.928681592073799, 3.91012465174139, -0.6466184042394076, 8.419255646279453, -8.094268776239682, 19.5220882165644], [0.3806641134895386, -0.054247151061047154, 0.35553915476967757, -0.16909223457447353, 0.6749672736601655, -0.19785778477211138], [6.21846263437043, 13.27919055895352, 5.556186398879765, 1.9758063219353386, 3.685290720905768, 11.4029046390971]]
#weight = [[0.5903138120766798, 0.3877986755669686, -0.9141386987535192, -0.1976691845132495, 0.20203743452617146, -3.8075577538969068, 13.355367631879064, -4.451492883287705, -4.077459994350015, -2.8670604965816433, -4.7237141988262215, 4.196577955775335], [0.05788957126040142, 0.7553202993193877, -0.41293776536874083, 0.9699420358471046, -0.39940905986885467, -0.7501531314002139, -3.649664715012045, -4.480417131687637, 12.40189634710716, -3.5479446525980323, -5.617634895580113, 4.614443478920718], [-0.44964748196431087, -0.21623652235598786, 0.5866855307562749, 2.6966017037464547, -0.2198767358187771, -15.121568447637916, 5.394174355851121, 3.3357101263144284, 5.142941080162113, 4.396587735322672, 6.27965132359125, -1.7132992179558508], [-0.8420976477874832, 3.6695944609982, 3.985187073998181, 11.839206173737303, -5.338358329529615, 12.584411691032383, -3.6258238911963225, -9.100219517619708, 0.09757837890948427, 0.26347032883307564, -2.317810633312963, 3.488522703736898], [0.7765991306851521, 0.28898396896441564, 0.7418514532805458, 7.79799228304993, -0.19002509820555794, -0.4950254716867189, 1.674108868938578, -2.550479398095525, -0.4777881148214889, -17.68616848936312, 17.798127474785755, 3.564677234214063], [-7.8104601458246075, -2.0636289766378315, -8.171829518484577, -0.3002860633762538, -0.8003364861508999, -8.88545431866241], [-9.931271155016605, -8.304191180682096, -4.721651527085654, -11.413401995134363, -6.807105641677094, -4.2517323859898255]]
#take 3
#weight = [[3.2209677211590364, 3.197844521358916, 1.0178700774531555, 2.6850832852265705, -0.1142410857208976, -7.424760693950065, 2.4741068398654735, -2.2455337118424423, 0.7975567187947812, -7.14879174218826, 1.1236926484785208, 1.0893799362829708], [0.8039483127966134, 2.0360025213584287, 0.01727196345640146, 13.743242184336747, -3.1131725731697797, 3.551772323183245, -1.912134715951023, 6.3466547648380365, 2.7583972445682843, -8.543348277242536, 1.0695972197298327, 1.0968548560479523], [-1.9711628616688408, -2.915258703528061, -0.6856763056229077, 5.775022292258987, -0.9242292134264676, -5.2290301586614545, 1.554497111395412, -8.663422012897401, 3.434867742464676, -1.4626063376098368, 3.1916811250980226, -3.1266957996982083], [-2.963581011499582, -1.8333571215071727, 0.19431283257511642, 12.685505979633772, 5.629563357825744, 2.269402859201827, -0.5394798674782302, 0.5415330617135145, -1.3492223641876873, -2.3782036784139104, -1.6868806998589756, -2.566848157895665], [-2.1971772106385568, -3.046613937692231, -1.91992660803775, 6.63802500331367, -13.090973918413972, 2.0019783380453955, 1.5786044885623458, -1.1226639661286304, -0.8816140948702889, -0.6041236731069092, 0.4128577493527515, -9.236718652388456], [-0.37898780078879885, 0.02278416261917311, -0.7370299412447531, 0.4580826382451188, 0.024909816638153417, -0.2986982689652905], [-6.204456817434451, -7.054321617074455, -3.8731103597961956, -7.0447149929917146, -6.223052160580929, -15.546591666508577]]
#weight = [[1.1620356523045294, 1.2293119820243932, -1.0680281171990968, -5.292742962549776, 3.004567388617913, 0.1416865776270463, 3.291787739904783, 3.311779449153159, 0.14350525985680226, 1.864065320111765, 0.41531949262324336, -0.7533626568752921], [2.6058003821118527, 0.9844458916296726, -2.031523030007777, -3.4422006288675315, 4.5638626155289055, -2.7046080365173837, 1.5356155037759958, 2.143460607268979, 3.9199241782825687, 0.12721742246873802, -0.8673469875291382, -1.8507505784965346], [-0.6131784294183935, 0.4639547932071712, -0.2666315961058117, 7.5292821094228755, -1.087211170794136, -0.7293843967199962, 0.4755491742014118, 0.4206888297192853, -0.8152826105125003, 1.988359326013337, -5.637090150274117, 0.5993755933965147], [0.27691971567025647, 1.037850843325554, -0.8250576728298599, -2.000417570912899, -0.006391527390813737, 2.0837950209487466, -8.24238052083867, 2.073287983133908, 3.2529633929820907, 2.1453759622986524, 3.7417070714472893, -2.9435663281508724], [-0.5037569895779601, -0.09031006667126633, -0.5311072081881074, 0.9440264182616986, 0.783774066571815, 6.828375559954358, -2.841644500387658, 0.24901014690965492, -4.849174297326825, -1.5945524577293186, -4.823283932380879, 1.1962232130136108], [1.4042452333993327, -0.09263191061453735, 0.8155734671747938, 5.256825832477101, 3.7371321136701705, 6.574206973322576], [13.323880256265307, -2.429025764741311, -10.985707613602697, 5.331771733353978, -0.12615902379461702, 15.191420141027539]]
#take 4
#weight = [[0.1906964794417142, -1.3230251087714693, -1.8568527275491726, -10.570510924730712, 0.3754935945207968, -4.767279359714083, 0.11809759894653031, -6.033443845654109, -4.862563766686397, -1.5992520992754746, 7.726616433374799, -3.552685892017083], [0.3653010148791735, 1.6736547762277736, 0.4531478751025384, -16.54229219042565, -3.440356011229119, 1.0104044032989574, -1.5155008934004035, 0.10642496061430406, -0.31316504641192483, 3.6828030284027515, -0.7489883459127186, -1.5244542545724582], [3.764701498917189, 0.4439295275324288, -2.9864503359860985, -3.9316071635513206, 5.798674529890128, -5.74883908980071, 5.398546661400034, -2.51467872126794, 10.108154232633654, -2.18382803218839, 2.5521301844943816, 3.311614056240679], [3.540372052106104, 4.504838909273797, 0.9723232714577832, -8.437301256316747, 14.998025152577217, -0.31994218366769667, -2.907161494453055, 3.893824258473095, -0.7579119727276817, 5.473585535436292, -3.2397908440984766, 15.192742537984023], [-6.248680428157617, -6.451873887564568, 1.6916836827123973, -4.655942296742202, 16.84212812043791, 5.734711342713387, 0.2955864473988734, 1.791898461633518, -3.4668510416701595, -1.358433980522392, -1.1057313703313203, 0.6888252016526282], [-0.43458701659431115, -0.0012780178353029815, -0.3084730481531892, 0.45069433382083834, 0.11685910089310178, -0.38449878977835567], [3.853866205452628, 12.037185197898191, 1.8699413530857774, 5.001790072421261, 4.513316028443307, 9.851232302101327]]
#weight = [[1.2911306905979911, -0.24861004977707912, -0.3569265073483646, 6.428936726785303, -1.3066626434454807, 0.25686627368220305, 0.9109367109212936, 0.16801829318526124, -0.20947845384297986, 1.2064241958740287, 0.036856251428802896, -3.359023278463905], [0.8036278040339718, 0.08012145204557049, 0.6845458738830329, 5.665809973883479, -0.9210048722891027, 1.3932208121673026, 1.046083232807299, 0.7941648248360582, 0.9365855847155237, 1.459603497851585, -0.5332056328831426, -2.534773939928277], [0.13301078275476066, 0.3429847838792201, -1.0338159784473917, 4.850766016537791, 0.7802072613774678, 1.2836937010475253, -0.8584360890917163, 1.7672832265369043, 0.10738236989054603, 3.4517636983002387, 0.22658851691351473, -1.0683529525811006], [1.6450210175622832, -0.28557720210974497, -0.2443112896936013, 6.8747070212198045, -0.8271033896420884, 0.2800592224187591, 0.5652744631888489, 0.08792418368229415, 0.9155105124974535, 0.46138821886651926, -0.3837164120204661, -3.494842813175506], [-0.8165377903719206, 1.958062852731677, 2.000566970645578, 5.919031622286524, 0.5506236423988042, 0.5494849931384184, 1.7160531008225108, 0.5260681781001207, 2.06495028663279, -0.07306752095214097, -1.204154609328308, -4.01239045823013], [-0.6146224030404228, 0.3233086944120275, 5.3979041539672545, -4.401300546048576, -0.42678298976091805, 1.0758830778504247], [-5.977941738072086, 0.019369137899827416, -0.5522710341025945, -7.13480943116727, -4.873784872189164, -16.38430927507949]]
#over break training
weight = [[
0.3662869049519842, -0.8570465997089849, -1.0415622777532179,
0.4420706041485171, 0.009019607340448946, 10.71120549250932,
-12.382299178044326, 10.244684059761504, -4.7714116184482425,
10.703496354982395, -0.8400740885526794, 2.1209753284753745
],
[
2.5366260663128686, 1.068830514524183, -2.0352964619508715,
-0.4999108962144593, 20.533875500405504, -1.301731900907607,
0.3103574877473953, -0.6500134665269202, 0.5084981306498488,
-0.8059254394827501, 1.31308616755389, 15.873718577684429
],
[
0.08005904547677181, -0.007564753721625019,
-0.10037179685269433, -30.286524359136706,
1.0348747725041256, -0.9129196441653706, 0.3754957498625551,
-1.305163204786704, -0.30909410903499906, 1.3574890310465415,
-0.2886464224612783, -5.911566724871949
],
[
4.655481170315177, -1.2991521769114085, -4.454652256174474,
4.274677448402512, 16.502421922725226, 12.710289689086315,
-9.738471684679293, 9.865851212762863, -8.873570414630043,
-0.1285739247431209, -14.804016099227272, 10.638993200840254
],
[
-13.975667825847953, 13.618115550695093, 27.871087273931305,
1.7052627216183622, -16.717674225226972, 0.8131962686148689,
0.6430161349620291, -0.6832651795038519, 1.6123037795473343,
3.7701435389411695, -2.729257195826183, -2.0449519807080896
],
[
0.6784559764050078, -0.30818410563793897,
-0.12048266318505906, 0.7386778766766025,
0.18813592750492103, 0.9656707078851317
],
[
7.248480099597683, 9.862850677133501, 18.95475459828898,
1.981941403265762, 7.509742979506548, 19.364672423856124
]]
sendData = getSendData()
output = getOutput(sendData, 11, 5, 2, weight)
turn, thrust = "N", "N"
if output[0] >= .55:
ai.turnRight(1)
turn = "R"
elif output[0] < .45:
ai.turnLeft(1)
turn = "L"
ai.setTurnSpeed(abs(output[0] - .5) * 100)
if output[1] > random():
ai.thrust(1)
thrust = "Y"
if ai.selfAlive():
print(turn + " " + str(round(output[0], 3)) + " | " + thrust +
" " + str(round(output[1], 3)))
def AI_loop():
#Release keys
ai.thrust(0)
ai.turnLeft(0)
ai.turnRight(0)
ai.setTurnSpeed(45)
turn, thrust = 0.5, 0
maxSpeed = 3
shotAngle = 9
wallClose = 12
heading = int(ai.selfHeadingDeg())
tracking = int(ai.selfTrackingDeg())
trackWall = ai.wallFeeler(500, tracking)
trackLWall = ai.wallFeeler(500, tracking + 3)
trackRWall = ai.wallFeeler(500, tracking - 3)
frontWall = ai.wallFeeler(500, heading)
flWall = ai.wallFeeler(500, heading + 10)
frWall = ai.wallFeeler(500, heading - 10)
leftWall = ai.wallFeeler(500, heading + 90)
rightWall = ai.wallFeeler(500, heading - 90)
trackWall = ai.wallFeeler(500, tracking)
backWall = ai.wallFeeler(500, heading - 180)
backLeftWall = ai.wallFeeler(500, heading - 185)
backRightWall = ai.wallFeeler(500, heading - 175)
speed = ai.selfSpeed()
closest = min(frontWall, leftWall, rightWall, backWall, flWall, frWall)
def closestWall(x): #Find the closest Wall
return {
frontWall: 1,
leftWall: 2,
rightWall: 3,
backWall: 4,
flWall: 5,
frWall: 6,
}[x]
wallNum = closestWall(closest)
crashWall = min(
trackWall, trackLWall, trackRWall
) #The wall we are likely to crash into if we continue on our current course
#Rules for turning
if wallNum == 1 or wallNum == 5 or wallNum == 6: #Front Wall is Closest (Turn Away From It)
ai.turnLeft(1)
turn = 0
elif wallNum == 2: # Left Wall is Closest (Turn Away From It)
ai.turnRight(1)
turn = 1
elif wallNum == 3: #Right Wall is Closest (Turn Away From It)
ai.turnLeft(1)
turn = 0
else: #Back Wall is closest- turn so that we are facing directly away from it
if backLeftWall < backRightWall:
ai.turnRight(
1) #We need to turn right to face more directly away from it
turn = 1
if backLeftWall > backRightWall: # We need to turn left to face more directly away from it
ai.turnLeft(1)
turn = 0
#Rules for thrusting
if speed < maxSpeed and frontWall > 100: #If we are moving slowly and we won't ram into anything, accelerate
ai.thrust(1)
thrust = 1
elif trackWall < 250 and (
ai.angleDiff(heading, tracking) > 120
): #If we are getting close to a wall, and we can thrust away from it, do so
ai.thrust(1)
thrust = 1
elif backWall < 20: #If there is a wall very close behind us, get away from it
ai.thrust(1)
thrust = 1
def AI_loop():
turn, thrust = .5, 0
ai.turnLeft(0)
ai.turnRight(0)
ai.thrust(0)
ai.setTurnSpeed(64)
heading = int(ai.selfHeadingDeg())
tracking = int(ai.selfTrackingDeg())
trackWall = ai.wallFeeler(500, tracking)
trackL3 = ai.wallFeeler(500, tracking + 3)
trackL10 = ai.wallFeeler(500, tracking + 10)
trackR3 = ai.wallFeeler(500, tracking - 3)
trackR10 = ai.wallFeeler(500, tracking - 10)
frontWall = ai.wallFeeler(500, heading)
frontL = ai.wallFeeler(500, heading + 15)
frontR = ai.wallFeeler(500, heading - 15)
leftWall = ai.wallFeeler(500, heading + 90)
leftF = ai.wallFeeler(500, heading + 65)
leftB = ai.wallFeeler(500, heading + 115)
rightWall = ai.wallFeeler(500, heading - 90)
rightF = ai.wallFeeler(500, heading - 65)
rightB = ai.wallFeeler(500, heading - 115)
backWall = ai.wallFeeler(500, heading - 180)
backL = ai.wallFeeler(500, heading - 195)
backR = ai.wallFeeler(500, heading - 165)
trackHeadRelative = (tracking - heading)
speed = ai.selfSpeed()
def findClosestArea(x):
return {
frontWall: 1,
frontL: 2,
leftF: 3,
leftWall: 4,
leftB: 5,
backL: 6,
backWall: 7,
backR: 8,
rightB: 9,
rightWall: 10,
rightF: 11,
frontR: 12
}[x]
closestVal = min(frontWall, frontL, leftF, leftWall, leftB, backL,
backWall, backR, rightB, rightWall, rightF, frontR)
#Find the closest Wall to our ship
closestWall = findClosestArea(closestVal)
#The wall we are likely to crash into if we continue on our current course
crashWall = min(trackWall, trackL3, trackL10, trackR3, trackR10)
#Rules for turning
if closestWall == 1:
ai.setTurnSpeed(64)
ai.turnLeft(1)
turn = 0
elif closestWall == 2:
ai.setTurnSpeed(64)
ai.turnRight(1)
turn = 1
elif closestWall == 3:
ai.setTurnSpeed(52)
ai.turnRight(1)
turn = .9
elif closestWall == 4:
ai.setTurnSpeed(40)
ai.turnRight(1)
turn = .8
elif closestWall == 5:
ai.setTurnSpeed(28)
ai.turnRight(1)
turn = .7
elif closestWall == 6:
ai.setTurnSpeed(16)
ai.turnRight(1)
turn = .6
elif closestWall == 7:
pass
elif closestWall == 8:
ai.setTurnSpeed(16)
ai.turnLeft(1)
turn = .4
elif closestWall == 9:
ai.setTurnSpeed(28)
ai.turnLeft(1)
turn = .3
elif closestWall == 10:
ai.setTurnSpeed(40)
ai.turnLeft(1)
turn = .2
elif closestWall == 11:
ai.setTurnSpeed(52)
ai.turnLeft(1)
turn = .1
elif closestWall == 12:
ai.setTurnSpeed(64)
ai.turnLeft(1)
turn = 0
#Rules for thrusting
#if we are going slow and there isn't a wall in front of us
if min(frontWall, frontL, frontR) > 100 and speed < 4:
ai.thrust(1)
thrust = 1
#if we are heading toward a wall and we are not facing it
elif crashWall < 150 and (ai.angleDiff(heading, tracking) > 90):
ai.thrust(1)
thrust = 1
#If there is a wall very close behind us, get away from it
elif backWall < 20 or backL < 20 or backR < 20:
ai.thrust(1)
thrust = 1
doBackPropigation = False
if ai.selfAlive() and doBackPropigation:
#adjust the the learning NN
infile = open("Sem2W_2.txt", "r")
weight = eval(infile.read())
infile.close()
sendData = getSendData(turn, thrust)
weight = adjustNN(sendData, 17, 7, 2, weight)
outfile = open("Sem2W_2.txt", "w")
outfile.write(str(weight))
outfile.close()
def dummyLoop():
global maxSpeed, shotAngle, wallClose, dead, previousScore
global turnedLeft, turnedRight, thrusted, shot
#Release keys
DataMinerBD.tthrustDummy(0)
DataMinerBD.tturnLeftDummy(0)
DataMinerBD.tturnRightDummy(0)
ai.setTurnSpeed(45)
#Set variables"""
heading = int(ai.selfHeadingDeg())
tracking = int(ai.selfTrackingDeg())
trackWall = ai.wallFeeler(500, tracking)
trackLWall = ai.wallFeeler(500, tracking + 3)
trackRWall = ai.wallFeeler(500, tracking - 3)
frontWall = ai.wallFeeler(500, heading)
flWall = ai.wallFeeler(500, heading + 10)
frWall = ai.wallFeeler(500, heading - 10)
leftWall = ai.wallFeeler(500, heading + 90)
llWall = ai.wallFeeler(500, heading + 100)
rlWall = ai.wallFeeler(500, heading + 80)
rightWall = ai.wallFeeler(500, heading - 90)
lrWall = ai.wallFeeler(500, heading - 80)
rrWall = ai.wallFeeler(500, heading - 100)
trackWall = ai.wallFeeler(500, tracking)
backWall = ai.wallFeeler(500, heading - 180)
backLeftWall = ai.wallFeeler(500, heading - 190)
backRightWall = ai.wallFeeler(500, heading - 170)
speed = ai.selfSpeed()
closest = min(frontWall, leftWall, rightWall, backWall, flWall,
frWall)
def closestWall(x): #Find the closest Wall
return {
frontWall: 1,
leftWall: 2,
rightWall: 3,
backWall: 4,
flWall: 5,
frWall: 6,
}[x]
wallNum = closestWall(closest)
#Code for finding the angle to the closest ship
targetX, targetY = ai.screenEnemyX(0), ai.screenEnemyY(0)
#baseString = "["+str(flWall/500)+","+str(frontWall/500)+","+str(frWall/500) + "," + str(backLeftWall/500) + "," + str(backWall/500) + "," + str(backRightWall/500) + ","+str(leftWall/500)+","+str(rightWall/500)+","+str(trackLWall/500) + "," + str(trackWall/500) + ","+str(trackRWall/500) + "," + str(speed/10)
calcDir = -1
if targetX - ai.selfX() != 0:
calcDir = (math.degrees(
math.atan2((targetY - ai.selfY()),
(targetX - ai.selfX()))) + 360) % 360
crashWall = min(
trackWall, trackLWall, trackRWall
) #The wall we are likely to crash into if we continue on our current course
#Rules for turning
if crashWall > wallClose * speed and closest > 25 and targetX != -1: #If we are far enough away from a predicted crash and no closer than 25 pixels to a wall we can try and aim and kill them
diff = (calcDir - heading)
#if ai.shotAlert(0) > -1 and ai.shotAlert(0) < 35: #If we are about to get shot
# tturnRight(1) #Screw aiming and turn right and thrust
# tthrust(1) #This is arguably a horrible strategy because our sideways profile is much larger, but it's required for the grade
if diff >= 0:
if diff >= 180:
DataMinerBD.tturnRightDummy(
1) #If the target is to our right- turn right
else:
DataMinerBD.tturnLeftDummy(
1) #If the target is to our left - turn left
else:
if diff > -180:
DataMinerBD.tturnRightDummy(
1) #If the target is to our right - turn right
else:
DataMinerBD.tturnLeftDummy(
1) #If the target is to our left - turn left
else: #Rules for avoiding death
# if crashWall/ai.selfSpeed() > ai.closestShot() :
if wallNum == 1 or wallNum == 5 or wallNum == 6: #Front Wall is Closest (Turn Away From It)
DataMinerBD.tturnLeftDummy(1)
elif wallNum == 2: # Left Wall is Closest (Turn Away From It)
DataMinerBD.tturnRightDummy(1)
elif wallNum == 3: #Right Wall is Closest (Turn Away From It)
DataMinerBD.tturnLeftDummy(1)
else: #Back Wall is closest- turn so that we are facing directly away from it
if backLeftWall < backRightWall:
DataMinerBD.tturnRightDummy(
1
) #We need to turn right to face more directly away from it
if backLeftWall > backRightWall: # We need to turn left to face more directly away from it
DataMinerBD.tturnLeftDummy(1)
#Rules for thrusting
if speed < maxSpeed and frontWall > 100: #If we are moving slowly and we won't ram into anything, accelerate
DataMinerBD.tthrustDummy(1)
elif trackWall < 200 and (
ai.angleDiff(heading, tracking) > 120
): #If we are getting close to a wall, and we can thrust away from it, do so
DataMinerBD.tthrustDummy(1)
elif backWall < 20: #If there is a wall very close behind us, get away from it
DataMinerBD.tthrustDummy(1)
if abs(
calcDir - heading
) < shotAngle and calcDir != -1: #If we are close to the current proper trajectory for a shot then fire
DataMinerBD.tshootDummy()
previousScore = ai.selfScore()
def AI_loop():
turn, thrust = .5, 0
ai.turnLeft(0)
ai.turnRight(0)
ai.thrust(0)
ai.setTurnSpeed(64)
heading = int(ai.selfHeadingDeg())
tracking = int(ai.selfTrackingDeg())
trackWall = ai.wallFeeler(500, tracking)
frontL = ai.wallFeeler(500, heading + 10)
frontR = ai.wallFeeler(500, heading - 10)
leftF = ai.wallFeeler(500, heading + 70)
leftB = ai.wallFeeler(500, heading + 110)
rightF = ai.wallFeeler(500, heading - 70)
rightB = ai.wallFeeler(500, heading - 110)
backL = ai.wallFeeler(500, heading - 200)
backR = ai.wallFeeler(500, heading - 160)
trackHeadRelative = (tracking - heading)
speed = ai.selfSpeed()
def findClosestArea(x):
return {
frontL: 1,
leftF: 2,
leftB: 3,
backL: 4,
backR: 5,
rightF: 6,
rightB: 7,
frontR: 8
}[x]
closestVal = min(frontL, leftF, leftB, backL, backR, rightF, rightB,
frontR)
#Find the closest Wall to our ship
closestWall = findClosestArea(closestVal)
#Rules for turning
if closestWall == 1:
ai.setTurnSpeed(64)
ai.turnRight(1)
turn = 1
elif closestWall == 2:
ai.setTurnSpeed(46)
ai.turnRight(1)
turn = .9
elif closestWall == 3:
ai.setTurnSpeed(28)
ai.turnRight(1)
turn = .8
elif closestWall == 4:
ai.setTurnSpeed(10)
ai.turnRight(1)
turn = .6
elif closestWall == 5:
ai.setTurnSpeed(10)
ai.turnLeft(1)
turn = .4
elif closestWall == 6:
ai.setTurnSpeed(28)
ai.turnLeft(1)
turn = .2
elif closestWall == 7:
ai.setTurnSpeed(26)
ai.turnLeft(1)
turn = .1
elif closestWall == 8:
ai.setTurnSpeed(64)
ai.turnLeft(1)
turn = 0
#Rules for thrusting
#if we are going slow and there isn't a wall in front of us
if min(frontL, frontR) > 100 and speed < 3:
ai.thrust(1)
thrust = 1
#if we are heading toward a wall and we are not facing it
elif trackWall < 150 and (ai.angleDiff(heading, tracking) > 90):
ai.thrust(1)
thrust = 1
#If there is a wall very close behind us, get away from it
elif backL < 20 or backR < 20:
ai.thrust(1)
thrust = 1
doBackPropigation = True
if ai.selfAlive() and doBackPropigation:
#adjust the the learning NN
infile = open("Sem2W_1.txt", "r")
weight = eval(infile.read())
infile.close()
sendData = getSendData(turn, thrust)
weight = adjustNN(sendData, 12, 5, 2, weight)
outfile = open("Sem2W_1.txt", "w")
outfile.write(str(weight))
outfile.close()
def AI_loop():
#Inserted Code
if ai.selfAlive():
DataMinerBD.updateInputs()
DataMinerBD.updateOutputs()
DataMinerBD.savePair()
print(DataMinerBD.length())
if DataMinerBD.length() > 10000:
DataMinerBD.writeData()
print("Finished")
ai.quitAI()
global lastTurn
#Release keys
DataMinerBD.tthrust(0)
DataMinerBD.tturnLeft(0)
DataMinerBD.tturnRight(0)
ai.setTurnSpeed(45)
#Set variables"""
heading = int(ai.selfHeadingDeg())
tracking = int(ai.selfTrackingDeg())
trackWall = ai.wallFeeler(500, tracking)
trackLWall = ai.wallFeeler(500, tracking+3)
trackRWall = ai.wallFeeler(500, tracking - 3)
frontWall = ai.wallFeeler(500,heading)
flWall = ai.wallFeeler(500, heading + 10)
frWall = ai.wallFeeler(500, heading - 10)
leftWall = ai.wallFeeler(500,heading+90)
rightWall = ai.wallFeeler(500,heading-90)
trackWall = ai.wallFeeler(500,tracking)
backWall = ai.wallFeeler(500, heading - 180)
backLeftWall = ai.wallFeeler(500, heading - 185)
backRightWall = ai.wallFeeler(500, heading - 175)
speed = ai.selfSpeed()
closest = min(frontWall, leftWall, rightWall, backWall)
def closestWall(x): #Find the closest Wall
return {
frontWall : 1,
leftWall : 2,
rightWall : 3,
backWall : 4,
flWall : 5,
frWall : 6,
}[x]
wallNum = closestWall(closest)
#Code for finding the angle to the closest ship
target = ai.closestShipId()
targetX, targetY = ai.screenEnemyX(0), ai.screenEnemyY(0)
calcDir = 0
if targetX- ai.selfX() != 0:
calcDir = (math.degrees(math.atan2((targetY - ai.selfY()), (targetX- ai.selfX()))) + 360)%360
targetDir = calcDir
crashWall = min(trackWall, trackLWall, trackRWall) #The wall we are likely to crash into if we continue on our current course
#Rules for turning
if crashWall > 25*speed and closest > 30 and targetX != -1: #If we are far enough away from a predicted crash and no closer than 25 pixels to a wall, and there isn't a wall between us and the closest enemy
#print("Aiming", targetDir, " Current", heading)
diff = (calcDir - heading)
if ai.shotAlert(0) > -1 and ai.shotAlert(0) < 35:
DataMinerBD.tturnRight(1)
DataMinerBD.tthrust(1)
elif diff >= 0:
if diff >= 180:
a = 0
DataMinerBD.tturnRight(1) #If the target is to our right- turn right
elif diff != 0 :
a = 0
DataMinerBD.tturnLeft(1) #If the target is to our left - turn left
else :
if diff > -180:
a = 0
DataMinerBD.tturnRight(1) #If the target is to our right - turn right
else :
a = 0
DataMinerBD.tturnLeft(1) #If the target is to our left - turn left
else : #Rules for avoiding death
# if crashWall/ai.selfSpeed() > ai.closestShot() :
#We find a target heading using our current trajectory and the closest wall then turn in it's direction
targetHeading = heading
print(heading)
if wallNum == 1or wallNum == 6 or wallNum == 5: #Front Wall is Closest
if lastTurn == 1:
targetHeading += 270
targetHeading = (targetHeading)%360
else :
targetHeading +=90
targetHeading = targetHeading%360
print("front")
elif wallNum == 2 : # Left Wall is Closest
targetHeading += 270
targetHeading = (targetHeading)%360
lastTurn = 1
print("leftwall")
elif wallNum == 3 :
targetHeading = targetHeading + 90
targetHeading = (targetHeading)%360
lastTurn = 2
print("rightWall")
else :
if backLeftWall < backRightWall:
lastTurn = 2
targetHeading += 5
targetHeading = (targetHeading)%360
if backLeftWall > backRightWall:
lastTurn = 1
targetHeading -= 5
targetHeading = (targetHeading)%360
speedConcern = ai.selfSpeed() - 4
if speedConcern < 0:
speedConcern = 0
elif speedConcern > 5:
speedConcern = 5
#targetHeading = (targetHeading*(1-(speedConcern/5))) + (((tracking+170)%360)*(speedConcern/5))
if speedConcern > 2:
targetHeading = (tracking + 180)%360
diff = (targetHeading - heading)
print("targetHEading : ", targetHeading, " heading : ", heading)
if diff >= 0:
if diff >= 180:
DataMinerBD.tturnRight(1) #If the targetHEading is to our right- turn right
print("right")
elif diff != 0 :
DataMinerBD.tturnLeft(1) #If the targeHeadingt is to our left - turn left
print("left")
else :
if diff > -180:
print("right")
DataMinerBD.tturnRight(1) #If the targetHeading is to our right - turn right
#print("right")
else :
print("left")
DataMinerBD.tturnLeft(1) #If the targetHeading is to our left - turn left
#print("nice")
#Rules for thrusting
if speed < 5 and frontWall > 200: #If we are moving slowly and we won't ram into anything, accelerate
DataMinerBD.tthrust(1)
elif crashWall < 25*speed and (abs(tracking - heading) > 120): #If we are getting close to a wall, and we can thrust away from it, do so
DataMinerBD.tthrust(1)
elif backWall < 30: #If there is a wall very close behind us, get away from it
DataMinerBD.tthrust(1)
if abs(calcDir - heading) < 15 : #If we are close to the current proper trajectory for a shot then fire
DataMinerBD.tshoot()
def AI_loop():
turn, thrust = .5, 0
ai.turnLeft(0)
ai.turnRight(0)
ai.thrust(0)
ai.setTurnSpeed(64)
heading = int(ai.selfHeadingDeg())
tracking = int(ai.selfTrackingDeg())
trackWall = ai.wallFeeler(500, tracking)
frontL = ai.wallFeeler(500, heading + 10)
frontR = ai.wallFeeler(500, heading - 10)
leftF = ai.wallFeeler(500, heading + 70)
leftB = ai.wallFeeler(500, heading + 110)
rightF = ai.wallFeeler(500, heading - 70)
rightB = ai.wallFeeler(500, heading - 110)
backL = ai.wallFeeler(500, heading - 190)
backR = ai.wallFeeler(500, heading - 170)
speed = ai.selfSpeed()
def findClosestArea(x):
return {
frontL: 1,
leftF: 2,
leftB: 3,
backL: 4,
backR: 5,
rightF: 6,
rightB: 7,
frontR: 8
}[x]
closestVal = min(frontL, leftF, leftB, backL, backR, rightF, rightB,
frontR)
#Find the closest Wall to our ship
closestWall = findClosestArea(closestVal)
#Rules for turning
#if we are heading for a wall, turn away from it
if trackWall < 100:
#round(abs(ai.angleDiff(heading, tracking))/3)
ai.setTurnSpeed(15 + round(abs(ai.angleDiff(heading, tracking)) / 4))
if ai.angleDiff(heading, tracking) > 0:
ai.turnRight(1)
turn = .9
else:
ai.turnLeft(1)
turn = .1
#otherwise turn away from the closest wall
elif closestWall == 1:
ai.setTurnSpeed(64)
ai.turnRight(1)
turn = 1
elif closestWall == 2:
ai.setTurnSpeed(46)
ai.turnRight(1)
turn = .9
elif closestWall == 3:
ai.setTurnSpeed(28)
ai.turnRight(1)
turn = .8
elif closestWall == 4:
ai.setTurnSpeed(10)
ai.turnRight(1)
turn = .6
elif closestWall == 5:
ai.setTurnSpeed(10)
ai.turnLeft(1)
turn = .4
elif closestWall == 6:
ai.setTurnSpeed(28)
ai.turnLeft(1)
turn = .2
elif closestWall == 7:
ai.setTurnSpeed(26)
ai.turnLeft(1)
turn = .1
elif closestWall == 8:
ai.setTurnSpeed(64)
ai.turnLeft(1)
turn = 0
#if we are going too fast and are not in danger turn around
# if speed > 2.5 and closestVal > 100:
# ai.setTurnSpeed(64)
# if ai.angleDiff(heading, tracking) > 0:
# ai.turnRight(1)
# turn = 1
# #print("R",random())
# else:
# ai.turnLeft(1)
# turn = 0
#print("L",random())
#Rules for thrusting
#if we are going slow and there isn't a wall in front of us
if min(frontL, frontR) > 100 and speed < 2.5:
ai.thrust(1)
thrust = 1
#if we are going too fast and are facing away from the direction we are heading
elif abs(ai.angleDiff(heading, tracking)) > 135 and speed > 2.5:
ai.thrust(1)
thrust = 1
#if we are heading toward a wall and we are not facing it
elif trackWall < 150 and (abs(ai.angleDiff(heading, tracking)) > 120):
ai.thrust(1)
thrust = 1
#If there is a wall very close behind us, get away from it
elif backL < 25 or backR < 25:
ai.thrust(1)
thrust = 1
doBackPropigation = False
if ai.selfAlive() and doBackPropigation:
#adjust the the learning NN
infile = open("Sem2W_1.txt", "r")
weight = eval(infile.read())
infile.close()
sendData = getSendData(turn, thrust)
weight = adjustNN(sendData, 12, 5, 2, weight)
outfile = open("Sem2W_1.txt", "w")
outfile.write(str(weight))
outfile.close()
def AI_loop(self):
# print("AI_LOOP")
if ai.selfAlive() == 0:
print("selfAlive is 0")
# if ai.selfAlive() == 0 and time2quit:
outputFile = open("output.txt", "w")
outputFile.write(str(self.counter))
outputFile.close()
# ai.quitAI()
# print(countFrames)
# Release keys
ai.thrust(0)
ai.turnLeft(0)
ai.turnRight(0)
ai.setTurnSpeed(55)
turnSpeedMin = 15
turnSpeedMax = 64
# Heuristics
#frontFeelerOffset = 35
ffo = self.frontFeelerOffset
rfo = self.rearFeelerOffset
perpFeelerOffset = 90
#rearFeelerOffset = 135
# speedLimit = 5
lowSpeedLimit = 2
targetingAccuracy = 4 # 1/2 tolerance in deg for aiming accuracy
shotIsDangerous = 130
# Acquire information
heading = int(ai.selfHeadingDeg())
tracking = int(ai.selfTrackingDeg())
# Wall feeling
feelers = []
frontWall = ai.wallFeeler(750, heading)
leftWall = ai.wallFeeler(500, heading + perpFeelerOffset)
rightWall = ai.wallFeeler(500, heading - perpFeelerOffset)
trackWall = ai.wallFeeler(750, tracking)
rearWall = ai.wallFeeler(250, heading - 180)
backLeftWall = ai.wallFeeler(500, heading + round(rfo))
backRightWall = ai.wallFeeler(500, heading - round(rfo))
frontLeftWall = ai.wallFeeler(500, heading + round(ffo))
frontRightWall = ai.wallFeeler(500, heading - round(ffo))
feelers.append(frontWall)
feelers.append(leftWall)
feelers.append(rightWall)
feelers.append(trackWall)
feelers.append(rearWall)
feelers.append(backLeftWall)
feelers.append(backRightWall)
feelers.append(frontLeftWall)
feelers.append(frontRightWall)
if min(feelers) < self.veryNearLimit:
self.speedLimit = lowSpeedLimit
# Movement controls
# Compute angles to the nearest things
m = self.angleToPointDeg((ai.selfX(), ai.selfY()),
(ai.shotX(0), ai.shotY(0)))
n = self.angleToPointDeg((ai.selfX(), ai.selfY()),
(ai.shotX(0), ai.shotY(0)))
# Sets turn speed and degree to the enemy
if ai.screenEnemyX(0) >= 0:
enemyDeg = self.angleToPointDeg(
(ai.selfX(), ai.selfY()),
(ai.screenEnemyX(0), ai.screenEnemyY(0)))
ai.setTurnSpeed(
self.rangeMap(abs(enemyDeg), 0, 180, turnSpeedMin,
turnSpeedMax))
else:
enemyDeg = self.angleToPointDeg(
(ai.selfRadarX(), ai.selfRadarY()),
(ai.closestRadarX(), ai.closestRadarY()))
ai.setTurnSpeed(
self.rangeMap(abs(enemyDeg), 0, 180, turnSpeedMin,
turnSpeedMax))
# Turn towards unoccluded enemies while in open space
if ai.aimdir(0) >= 0 and self.headingDiff(
heading, ai.aimdir(0)) > 0 and not self.enemyBehindWall(0):
ai.turnRight(1)
elif ai.aimdir(0) >= 0 and self.headingDiff(
heading, ai.aimdir(0)) < 0 and not self.enemyBehindWall(0):
ai.turnLeft(1)
# Turn away from nearby walls
elif min(feelers) < ai.enemyDistance(
0
) and trackWall < self.nearLimit and leftWall < rightWall: #DONE
ai.turnRight(1)
elif min(feelers) < ai.enemyDistance(
0
) and trackWall < self.nearLimit and rightWall < leftWall: #DONE
ai.turnLeft(1)
elif min(feelers) < ai.enemyDistance(
0
) and backLeftWall < self.nearLimit and rightWall > self.nearLimit:
ai.turnRight(1)
elif min(feelers) < ai.enemyDistance(
0
) and backRightWall < self.nearLimit and leftWall > self.nearLimit:
ai.turnLeft(1)
elif min(feelers) < ai.enemyDistance(
0) and frontRightWall < self.nearLimit:
ai.turnLeft(1)
elif min(feelers) < ai.enemyDistance(
0) and frontLeftWall < self.nearLimit:
ai.turnRight(1)
# TODO: NEED RULES FOR WHEN ENEMY IS OCCLUDED
elif self.enemyBehindWall and enemyDeg < 0:
ai.turnRight(1)
elif self.enemyBehindWall and enemyDeg >= 0:
ai.turnLeft(1)
# Turn away from shots
elif m > 0:
ai.turnRight(1)
elif m < 0:
ai.turnLeft(1)
# THRUST (includes fuzzy controller)
# Power levels
power1 = 55
power2 = 45
power3 = 55
power4 = 36
power5 = 36
power6 = 28
power7 = 24
power8 = 30
mfS = self.mfSpeed(ai.selfSpeed())
mfD = self.mfDanger(ai.shotAlert(0))
# Aggregation
# if S is high and D is moderate or high:
p1 = max(mfS[2], min(mfD[1], mfD[2]))
# if S is moderate and D is moderate:
p2 = max(mfS[1], mfD[1])
# if S is low and D is high:
p3 = max(mfS[0], mfD[2])
# if S is moderate and D is moderate:
p4 = max(mfS[1], mfD[1])
# if S is low and D is moderate:
p5 = max(mfS[0], mfD[1])
# if S is high and D is low:
p6 = max(mfS[2], mfD[0])
# if S is moderate and D is low:
p7 = max(mfS[1], mfD[0])
# if S is low and D is low:
p8 = max(mfS[0], mfD[0])
consequents = [
power1, power2, power3, power4, power5, power6, power7, power8
]
memberships = [p1, p2, p3, p4, p5, p6, p7, p8]
# Defuzzification
ai.setPower(self.crispify(memberships, consequents))
# Further thrusting rules
if ai.shotAlert(0) < 130 and ai.shotAlert(0) != -1 and ai.wallBetween(
ai.selfX(), ai.selfY(), ai.shotX(0), ai.shotY(0)) == -1:
ai.thrust(1)
elif ai.selfSpeed() <= self.speedLimit:
ai.thrust(1)
elif trackWall < self.nearLimit and self.angleDiff(heading,
tracking) > 75:
ai.thrust(1)
elif rearWall < self.nearLimit and self.angleDiff(heading,
tracking) > 90:
ai.thrust(1)
# FIRE
# Restrict firing to reasonably accurate attempts
if self.headingDiff(heading, ai.aimdir(
0)) < targetingAccuracy and not self.enemyBehindWall(0):
ai.fireShot()
self.counter += 1
def AI_loop(self):
# print("AI_LOOP")
if ai.selfAlive() == 0:
outputFile = open("fitness.txt", "a")
# outputFile.write(str((self.totalDists/self.counter))+"\t")
outputFile.write(str(int((self.fitness**1.2))) + "\t")
[
print(str("%.5f" % g) + "\t", end="", file=outputFile)
for g in self.chromosome
]
print("\n", end="", file=outputFile)
outputFile.close()
# Release keys
ai.thrust(0)
ai.turnLeft(0)
ai.turnRight(0)
ai.setTurnSpeed(55)
# Heuristics
frontFeelerOffset = 45
perpFeelerOffset = 90
rearFeelerOffset = 135
# turnSpeedMin = 15 # learn range: 4 - 24
turnSpeedMax = 55
speedLimit = 5 # learn range: 2-6
lowSpeedLimit = 2
targetingAccuracy = 4 # 1/2 tolerance in deg for aiming accuracy
shotIsDangerous = 130
# Acquire information
heading = int(ai.selfHeadingDeg())
tracking = int(ai.selfTrackingDeg())
###=== ENEMY FEELERS ===###
# gets angle to enemy
enemyDeg = self.angleToPointDeg(
(ai.selfX(), ai.selfY()), (ai.screenEnemyX(0), ai.screenEnemyY(0)))
enemyWallDistances = []
# maxAngleOffset = 90 # learn range: 30 - 120
# resolution = 5 # learn range: 2 - 10
distAngleTuples = []
# creates tuples of degrees and wallFeelers
for m in (0, self.maxAngleOffset, self.resolution):
distAngleTuples.append(
(enemyDeg - m, ai.wallFeeler(500, int(enemyDeg - m))))
distAngleTuples.append(
(enemyDeg + m, ai.wallFeeler(500, int(enemyDeg + m))))
# gets furthest feeler
maxFeelerAngle = max(distAngleTuples, key=self.returnSecond)
angleToOpenSpace = self.headingDiff(ai.selfHeadingDeg(),
maxFeelerAngle[0])
###=== WALL FEELERS ===###
frontWall = ai.wallFeeler(
self.genericFeelerDist,
heading) # wall feeler for wall directly ahead
leftFrontWall = ai.wallFeeler(
self.genericFeelerDist, heading +
frontFeelerOffset) # wall feeler for wall 45 degrees to the left
rightFrontWall = ai.wallFeeler(
self.genericFeelerDist, heading -
frontFeelerOffset) # wall feeler for wall 45 degrees to the right
leftWall = ai.wallFeeler(
self.genericFeelerDist, heading +
perpFeelerOffset) # wall feeler for wall 90 degrees to the left
rightWall = ai.wallFeeler(
self.genericFeelerDist, heading -
perpFeelerOffset) # wall feeler for wall 90 degrees to the right
backWall = ai.wallFeeler(self.genericFeelerDist, heading -
180) # wall feeler for wall straight back
leftBackWall = ai.wallFeeler(
self.genericFeelerDist, heading +
rearFeelerOffset) # wall feeler for wall 135 degrees to the left
rightBackWall = ai.wallFeeler(
self.genericFeelerDist, heading -
rearFeelerOffset) # wall feeler for wall 135 degrees to the right
trackWall = ai.wallFeeler(
self.genericFeelerDist,
tracking) # wall in front of where ship is moving
# Keep track of all the feeler distances
feelers = [
frontWall, leftFrontWall, rightFrontWall, leftWall, rightWall,
backWall, leftBackWall, rightBackWall, trackWall
]
# Aim assist
leftDir = (heading +
90) % 360 # angle 90 degrees to the left of current heading
rightDir = (heading - 90
) % 360 # angle 90 degrees to the right of current heading
aimer = ai.aimdir(
0
) # direction that the ship needs to turn to in order to face the enemy in degrees
shot = ai.shotAlert(
0
) # returns a danger rating of a shot, the smaller the number the more likely the shot is to hit the ship
enemyX = ai.screenEnemyX(0) # returns the closest enemy's x-coord
enemyY = ai.screenEnemyY(0) # returns the closest enemy's y-coord
selfX = ai.selfX() # returns the ship's x-coord
selfY = ai.selfY() # returns the ship's x-coord
# Fuzzy variable declaration
trackRisk = riskEval(trackWall,
ai.selfSpeed()) #risk of running into trackWall
frontRisk = riskEval(frontWall,
ai.selfSpeed()) #risk of running into frontWall
leftRisk = riskEval(leftWall,
ai.selfSpeed()) #risk of running into leftWall
rightRisk = riskEval(rightWall,
ai.selfSpeed()) #risk of running into rightWall
LFRisk = riskEval(leftFrontWall,
ai.selfSpeed()) #risk of running into leftFrontWall
RFRisk = riskEval(rightFrontWall,
ai.selfSpeed()) #risk of running into rightFrontWall
LBRisk = riskEval(leftBackWall,
ai.selfSpeed()) #risk of running into leftBackWall
RBRisk = riskEval(rightBackWall,
ai.selfSpeed()) #risk of running into rightBackWall
backRisk = riskEval(backWall,
ai.selfSpeed()) #risk of running into backWall
# Compress some wall feelers
sTrack = self.squisher(trackWall)
sLeft = self.squisher(leftFrontWall)
sRight = self.squisher(rightFrontWall)
sLeftStraight = self.squisher(leftWall)
sRightStraight = self.squisher(rightWall)
# output from neural network that tells how much to turn and which direction
turn = self.trainedNeuralNetwork(sTrack, sLeft, sRight, sLeftStraight,
sRightStraight)
###=== THRUST POWER ADJUSTMENT ===#
# Power levels
mfS = self.mfSpeed(ai.selfSpeed())
mfD = self.mfDanger(ai.shotAlert(0))
# if S is high and D is moderate or high:
p1 = max(mfS[2], min(mfD[1], mfD[2]))
# if S is moderate and D is moderate:
p2 = max(mfS[1], mfD[1])
# if S is low and D is high:
p3 = max(mfS[0], mfD[2])
# if S is moderate and D is moderate:
p4 = max(mfS[1], mfD[1])
# if S is low and D is moderate:
p5 = max(mfS[0], mfD[1])
# if S is high and D is low:
p6 = max(mfS[2], mfD[0])
# if S is moderate and D is low:
p7 = max(mfS[1], mfD[0])
# if S is low and D is low:
p8 = max(mfS[0], mfD[0])
consequents = [55, 45, 55, 36, 36, 28, 24, 30]
memberships = [p1, p2, p3, p4, p5, p6, p7, p8]
ai.setPower(self.crispify(memberships, consequents))
if ai.enemyDistance(0) > self.lastDist and ai.enemyDistance(
0) < self.enemyClose:
ai.thrust(1)
elif ai.selfSpeed(
) <= 3 and frontWall >= 200: # if speed is slow and front wall is far away, thrust
ai.thrust(1)
elif trackWall < 60 and frontWall >= 200: # if the track wall is close, thrust
ai.thrust(1)
elif backWall < 20: # if the back wall is close, thrust
ai.thrust(1)
###=== TURNING RULES ===###
# Escape shots
if shot > 0 and shot < 70:
# if a shot is closeby, turn and thrust to avoid
if self.angleDif(rightDir, ai.shotX(0)) < self.angleDif(
leftDir, ai.shotX(0)
) or self.angleDif(rightDir, ai.shotY(0)) < self.angleDif(
leftDir, ai.shotY(0)
): # if shot is coming from the right, turn away and thrust
# print("Turning: avoiding shot")#debug
ai.turnLeft(1)
ai.thrust(1)
elif self.angleDif(leftDir, ai.shotX(0)) < self.angleDif(
rightDir, ai.shotX(0)
) or self.angleDif(leftDir, ai.shotY(0)) < self.angleDif(
rightDir, ai.shotY(0)
): # if shot is coming from the left, turn away and shoot ------> change this shot is just a number
# print("Turning: avoiding shot")#debug
ai.turnRight(1)
ai.thrust(1)
# Turn towards unoccluded enemy
elif aimer >= 0 and self.angleDif(rightDir, aimer) < self.angleDif(
leftDir, aimer) and not self.enemyBehindWall(
0): # if an enemy to the right, turn and shoot it
if ai.screenEnemyX(0) >= 0:
enemyDeg = self.angleToPointDeg(
(ai.selfX(), ai.selfY()),
(ai.screenEnemyX(0), ai.screenEnemyY(0)))
ai.setTurnSpeed(
self.rangeMap(abs(enemyDeg), 0, 180, self.turnSpeedMin,
turnSpeedMax))
else:
enemyDeg = self.angleToPointDeg(
(ai.selfRadarX(), ai.selfRadarY()),
(ai.closestRadarX(), ai.closestRadarY()))
ai.setTurnSpeed(
self.rangeMap(abs(enemyDeg), 0, 180, self.turnSpeedMin,
turnSpeedMax))
# print("Turning: aiming right")#debug
ai.turnRight(1)
elif aimer >= 0 and self.angleDif(leftDir, aimer) < self.angleDif(
rightDir, aimer) and not self.enemyBehindWall(
0): # if an enemy to the left, turn and shoot it
if ai.screenEnemyX(0) >= 0:
enemyDeg = self.angleToPointDeg(
(ai.selfX(), ai.selfY()),
(ai.screenEnemyX(0), ai.screenEnemyY(0)))
ai.setTurnSpeed(
self.rangeMap(abs(enemyDeg), 0, 180, self.turnSpeedMin,
turnSpeedMax))
else:
enemyDeg = self.angleToPointDeg(
(ai.selfRadarX(), ai.selfRadarY()),
(ai.closestRadarX(), ai.closestRadarY()))
ai.setTurnSpeed(
self.rangeMap(abs(enemyDeg), 0, 180, self.turnSpeedMin,
turnSpeedMax))
# print("Turning: aiming left")#debug
ai.turnLeft(1)
#fuzzy avoid walls ahead
elif leftRisk > rightRisk and trackRisk > 0.5: # and min(feelers) < self.nearLimit: #if the left wall and track walls are close, turn right
#if enemyX >=0 and enemyY >= 0 and ai.wallBetween(selfX, selfY, enemyX, enemyY) == -1:
ai.turnRight(1)
# print("Turning: fuzzy right")#debug
elif rightRisk > leftRisk and trackRisk > 0.5: # and min(feelers) < self.nearLimit: #if the right wall and track walls are close, turn left
# if enemyX >=0 and enemyY >= 0 and ai.wallBetween(selfX, selfY, enemyX, enemyY) == -1:
ai.turnLeft(1)
# print("Turning: fuzzy left")#debug
# Turn to open space nearest the angle to the enemy
elif self.enemyBehindWall(0) and min(feelers) > self.nearLimit:
if angleToOpenSpace < 0:
# print("Turning: open space left")#debug
ai.turnLeft(1)
elif angleToOpenSpace > 0:
# print("Turning: open space right")#debug
ai.turnRight(1)
# if neural net value is not between 0.48 and 0.52 then we have to turn right or left
elif not (turn >= 0.43 and turn <= 0.57):
if turn < 0.43: # turn right if value is below 0.43
# print("Turning: neural net right")#debug
ai.turnRight(1)
elif turn > 0.57: # turn left if value is below 0.57
# print("Turning: neural net left")#debug
ai.turnLeft(1)
###=== FIRING RULES ===###
# Restrict firing to reasonably accurate attempts:
# accurate range, enemy not behind wall and enemy close enough
if self.headingDiff(
heading,
ai.aimdir(0)) < targetingAccuracy and not self.enemyBehindWall(
0) and ai.enemyDistance(0) < self.enemyFireDist:
ai.fireShot()
# print("Shot Fired")#debug
# print("Firing Dist: ", self.enemyFireDist)#debug
self.counter += 1
###=== How did we die? and other Fitness Calculations ===###
# Fitness function information
self.totalDists += ai.enemyDistance(0)
if ai.enemyDistance(0) > 0:
self.currentDist = ai.enemyDistance(0)
if self.currentDist < self.lastDist:
self.fitness += 1
self.lastDist = self.currentDist
self.fitness += 1
alive = ai.selfAlive()
message = ai.scanGameMsg(1)
# print(message)#debug
if alive == 0:
self.framesDead += 1
# print(self.framesDead, message)#debug
if self.framesDead == 2:
# print("dead now")#debug
# Ran into wall
if message.find("Beal-Morneault") != -1 and message.find(
"wall") != -1:
print("End of match: wall collision.") #debug
self.fitness -= self.wallPenalty
# Crashed into player
elif message.find("crashed.") != -1:
print("End of match: player collision.") #debug
self.fitness -= self.crashPenalty
# Killed by bullet
elif message.find("Beal-Morneault was") != -1:
print("End of match: killed by opponent.") #debug
self.fitness -= self.killedPenalty
# Killed the opponent
elif message.find("by a shot from Beal-Morneault") != -1:
print("End of match: killed the opponent!") #debug
self.fitness += self.killerBonus
else:
print("End of match: enemy died.")
self.fitness += (ai.selfScore() - ai.enemyScoreId(0)
) * self.scoreDiffBonusFactor
ai.quitAI()
else:
self.framesDead = 0
def AI_loop():
#Release keys
ai.thrust(0)
ai.turnLeft(0)
ai.turnRight(0)
ai.setTurnSpeed(45)
turn, thrust, shoot = 0.5, 0, 0
maxSpeed = 3
shotAngle = 9
wallClose = 12
#Set variables"""
heading = int(ai.selfHeadingDeg())
tracking = int(ai.selfTrackingDeg())
trackWall = ai.wallFeeler(500, tracking)
trackLWall = ai.wallFeeler(500, tracking+3)
trackRWall = ai.wallFeeler(500, tracking - 3)
frontWall = ai.wallFeeler(500,heading)
flWall = ai.wallFeeler(500, heading + 10)
frWall = ai.wallFeeler(500, heading - 10)
leftWall = ai.wallFeeler(500,heading+90)
rightWall = ai.wallFeeler(500,heading-90)
trackWall = ai.wallFeeler(500,tracking)
backWall = ai.wallFeeler(500, heading - 180)
backLeftWall = ai.wallFeeler(500, heading - 185)
backRightWall = ai.wallFeeler(500, heading - 175)
speed = ai.selfSpeed()
closest = min(frontWall, leftWall, rightWall, backWall, flWall, frWall)
def closestWall(x): #Find the closest Wall
return {
frontWall : 1,
leftWall : 2,
rightWall : 3,
backWall : 4,
flWall : 5,
frWall : 6,
}[x]
wallNum = closestWall(closest)
#Code for finding the angle to the closest ship
targetX, targetY = ai.screenEnemyX(0), ai.screenEnemyY(0)
calcDir = 0
if targetX- ai.selfX() != 0:
calcDir = (math.degrees(math.atan2((targetY - ai.selfY()), (targetX- ai.selfX()))) + 360)%360
crashWall = min(trackWall, trackLWall, trackRWall) #The wall we are likely to crash into if we continue on our current course
#Rules for turning
if crashWall > wallClose*speed and closest > 25 and targetX != -1: #If we are far enough away from a predicted crash and no closer than 25 pixels to a wall we can try and aim and kill them
diff = (calcDir - heading)
if ai.shotAlert(0) > -1 and ai.shotAlert(0) < 35: #If we are about to get shot
ai.turnRight(1) #Screw aiming and turn right and thrust
ai.thrust(1)
thrust = 1
#This is arguably a horrible strategy because our sideways profile is much larger, but it's required for the grade
elif diff >= 0:
if diff >= 180:
ai.turnRight(1) #If the target is to our right- turn right
turn = 1
else :
ai.turnLeft(1) #If the target is to our left - turn left
turn = 0
else :
if diff > -180:
ai.turnRight(1) #If the target is to our right - turn right
turn = 1
else :
ai.turnLeft(1) #If the target is to our left - turn left
turn = 0
#Rules for avoiding death
else :
# if crashWall/ai.selfSpeed() > ai.closestShot() :
if wallNum == 1 or wallNum == 5 or wallNum == 6: #Front Wall is Closest (Turn Away From It)
ai.turnLeft(1)
turn = 0
elif wallNum == 2 : # Left Wall is Closest (Turn Away From It)
ai.turnRight(1)
turn = 1
elif wallNum == 3 : #Right Wall is Closest (Turn Away From It)
ai.turnLeft(1)
turn = 0
else : #Back Wall is closest- turn so that we are facing directly away from it
if backLeftWall < backRightWall:
ai.turnRight(1) #We need to turn right to face more directly away from it
turn = 1
if backLeftWall > backRightWall: # We need to turn left to face more directly away from it
ai.turnLeft(1)
turn = 0
#Rules for thrusting
if speed < maxSpeed and frontWall > 100: #If we are moving slowly and we won't ram into anything, accelerate
ai.thrust(1)
thrust = 1
elif trackWall < 250 and (ai.angleDiff(heading, tracking) > 120): #If we are getting close to a wall, and we can thrust away from it, do so
ai.thrust(1)
thrust = 1
elif backWall < 20: #If there is a wall very close behind us, get away from it
ai.thrust(1)
thrust = 1
if abs(calcDir - heading) < shotAngle : #If we are close to the current proper trajectory for a shot then fire
ai.fireShot()
shoot = 1
#adjust the the learning NN
infile = open("myBotWeights.txt","r")
weight = eval(infile.read())
infile.close()
sendData = getSendData(turn, thrust, shoot)
weight = adjustNN(sendData, 21, 8, 3, weight)
outfile = open("myBotWeights.txt","w")
outfile.write(str(weight))
outfile.close()
