marloAnalysis-Ben.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import json\n",
    "from pprint import pprint\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "from numpy import genfromtxt\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "with open('QTable_Alpha_0_5_Gamma_1_Decay_0_97.json') as f:\n",
    "    Qtable = json.load(f)\n",
    "print(\"Length of QTable: \" + str(len(Qtable)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\benja\\\\OneDrive\\\\Documents\\\\Queen Mary\\\\Artificial Intelligence in Games\\\\Assignments\\\\MARLO\\\\marlo'"
      ]
     },
     "execution_count": 470,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()\n",
    "#os.chdir(\"C:\\\\Users\\\\benja\\\\OneDrive\\\\Documents\\\\Queen Mary\\\\Artificial Intelligence in Games\\\\Assignments\\\\MARLO\\\\marlo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 0, 2, 3, 4, 5]\n",
      "[0, 1, 2, 3, 4, 5, 6]\n",
      "[0, 90, 180, 270]\n"
     ]
    }
   ],
   "source": [
    "xs = []; zs = []; yaws = []\n",
    "for key in Qtable:\n",
    "    values = key.split(\":\")\n",
    "    x = int(values[0])\n",
    "    z = int(values[1])\n",
    "    yaw = int(values[2])\n",
    "    if x not in xs: xs.append(x)\n",
    "    if z not in zs: zs.append(z)\n",
    "    if yaw not in yaws: yaws.append(yaw)\n",
    "    \n",
    "print(xs); print(zs); print(yaws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 653,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'3:0:0:56:0': [-0.018024960000000003, -0.043320952782848005, 0, -0.008],\n",
       " '3:0:270:55:0': [-0.0651172118462464,\n",
       "  -0.0680480899043471,\n",
       "  -0.05968052922089473,\n",
       "  -0.059905949263462405],\n",
       " '4:0:270:55:0': [-0.05717337944883201,\n",
       "  -0.06757469409168589,\n",
       "  -0.06427988073844311,\n",
       "  -0.06329546985570304],\n",
       " '4:0:180:55:0': [-0.05982464,\n",
       "  -0.06017003625906177,\n",
       "  -0.059002610614272005,\n",
       "  -0.06225891267340927],\n",
       " '4:0:90:55:0': [-0.06389719735336961,\n",
       "  -0.061124846498611204,\n",
       "  -0.060016102604800003,\n",
       "  -0.058951378146259115],\n",
       " '4:0:0:55:0': [-0.06038236960405067,\n",
       "  -0.059056355660791815,\n",
       "  -0.0615956262617088,\n",
       "  -0.06216186628118938],\n",
       " '3:0:90:55:0': [-0.05925790722424833,\n",
       "  -0.06106051066593281,\n",
       "  -0.061369547325440005,\n",
       "  -0.06327930825993723],\n",
       " '3:0:180:55:0': [-0.056,\n",
       "  -0.05932389441732609,\n",
       "  -0.05757398063513601,\n",
       "  -0.06040487255080961],\n",
       " '3:1:180:55:0': [-0.06116740408279041,\n",
       "  -0.05444944155770881,\n",
       "  -0.05931253415411713,\n",
       "  -0.054081150794501986],\n",
       " '3:2:180:55:0': [-0.054874088302182406,\n",
       "  -0.05237841541201921,\n",
       "  -0.05969611742621206,\n",
       "  -0.05650650152960001],\n",
       " '3:1:270:55:0': [-0.061368143872000014,\n",
       "  -0.054947306864640014,\n",
       "  -0.06232388955166003,\n",
       "  -0.05690422004940801],\n",
       " '3:1:0:55:0': [-0.05595227764340818,\n",
       "  -0.05871025778547753,\n",
       "  -0.056677248252677996,\n",
       "  -0.05920994541568001],\n",
       " '3:2:0:55:0': [-0.04667788509184001,\n",
       "  -0.04773018189824001,\n",
       "  -0.0548473402838483,\n",
       "  -0.05840505084161557],\n",
       " '3:1:90:55:0': [-0.05732873304014849,\n",
       "  -0.05480405292576785,\n",
       "  -0.06837353720053761,\n",
       "  -0.05794547062475326],\n",
       " '3:2:270:55:0': [-0.0481714167967803,\n",
       "  -0.060505673606679765,\n",
       "  -0.053682779029504,\n",
       "  -0.052982230777856014],\n",
       " '2:2:270:55:0': [-0.05621301236203521,\n",
       "  -0.05475605196395422,\n",
       "  -0.055174667624775686,\n",
       "  -0.05322294411264],\n",
       " '3:3:180:55:0': [-0.04740952064000001,\n",
       "  -0.046971883115018986,\n",
       "  -0.03884082416058369,\n",
       "  -0.047300018274304],\n",
       " '3:3:90:55:0': [-0.04373411840000001,\n",
       "  -0.05584188216689297,\n",
       "  -0.049199229004677124,\n",
       "  -0.048085360640000005],\n",
       " '3:4:180:55:0': [-0.04777846243983361,\n",
       "  -0.04134324437549843,\n",
       "  -0.05021066985472,\n",
       "  -0.04499789133905921],\n",
       " '3:4:270:55:0': [-0.050640559273664815,\n",
       "  -0.05071545466809688,\n",
       "  -0.046073526190080004,\n",
       "  -0.0481414168576],\n",
       " '2:4:270:55:0': [-0.048099045376,\n",
       "  -0.05108401897472,\n",
       "  -0.05043195248624766,\n",
       "  -0.05141398665026378],\n",
       " '1:4:270:55:0': [-0.05005424345017688,\n",
       "  -0.04377847169024001,\n",
       "  -0.052123762032640006,\n",
       "  -0.05293507264512001],\n",
       " '3:4:90:55:0': [-0.048365270170944885,\n",
       "  -0.04307759939583771,\n",
       "  -0.0433645192347648,\n",
       "  -0.047483838464000006],\n",
       " '3:4:0:55:0': [-0.04616054502588417,\n",
       "  -0.04850517114880001,\n",
       "  -0.044830351360000006,\n",
       "  -0.05141995192320001],\n",
       " '2:4:90:55:0': [-0.049259417599824225,\n",
       "  -0.0460125069311524,\n",
       "  -0.05148454362542661,\n",
       "  -0.04807120097194803],\n",
       " '2:4:180:55:0': [-0.049205946265339175,\n",
       "  -0.05390477342707549,\n",
       "  -0.051756467345015995,\n",
       "  -0.05138664734704326],\n",
       " '2:3:180:55:0': [-0.05197684199600948,\n",
       "  -0.04850207264764593,\n",
       "  -0.05228917164516312,\n",
       "  -0.046532341720678404],\n",
       " '2:3:270:55:0': [-0.046029865779200006,\n",
       "  -0.04800214138880001,\n",
       "  -0.06001951135577932,\n",
       "  -0.03712483843254518],\n",
       " '2:2:180:55:0': [-0.046719107891200005,\n",
       "  -0.052817797663948805,\n",
       "  -0.05004939168579585,\n",
       "  -0.062218427894595585],\n",
       " '3:3:0:55:0': [-0.04546081751040001,\n",
       "  -0.04913621729280001,\n",
       "  -0.04875221401600001,\n",
       "  -0.049949895660404746],\n",
       " '3:3:270:55:0': [-0.04739020741017601,\n",
       "  -0.04206637687808983,\n",
       "  -0.051341722709131274,\n",
       "  -0.04507619328],\n",
       " '4:3:90:55:0': [-0.050324021968896016,\n",
       "  -0.05642274239283201,\n",
       "  -0.05201788645539841,\n",
       "  -0.052436505242422486],\n",
       " '4:3:0:55:0': [-0.04963896022162207,\n",
       "  -0.044835594240000005,\n",
       "  -0.049363480576,\n",
       "  -0.049560252416000004],\n",
       " '5:3:90:55:0': [-0.055827358253056014,\n",
       "  -0.058209536585989104,\n",
       "  -0.05217386495868165,\n",
       "  -0.051149140623360005],\n",
       " '5:3:180:55:0': [-0.057530938979587505,\n",
       "  -0.05282146844641239,\n",
       "  -0.05528209196164597,\n",
       "  -0.05342272176125071],\n",
       " '5:3:0:55:0': [-0.04841554552356775,\n",
       "  -0.049170983321600005,\n",
       "  -0.056567460519810736,\n",
       "  -0.05387381342208],\n",
       " '5:4:0:55:0': [-0.05127919157248001,\n",
       "  -0.045683843888387504,\n",
       "  -0.04926746624000001,\n",
       "  -0.04593640017026972],\n",
       " '5:4:270:55:0': [-0.05027991024148677,\n",
       "  -0.056288958867041716,\n",
       "  -0.05238263218164282,\n",
       "  -0.05205702475775268],\n",
       " '6:4:270:55:0': [-0.051775999999999996,\n",
       "  -0.05774296813392325,\n",
       "  -0.05415815886984492,\n",
       "  -0.050489697238417446],\n",
       " '5:4:180:55:0': [-0.053323891179461416,\n",
       "  -0.05497147403106214,\n",
       "  -0.04368796794833125,\n",
       "  -0.05226384260651251],\n",
       " '6:4:0:55:0': [-0.058355941816540774,\n",
       "  -0.05604507878597252,\n",
       "  -0.05653507719106478,\n",
       "  -0.051365179392],\n",
       " '6:5:0:55:0': [-0.052484521951186226,\n",
       "  -0.057448526627471366,\n",
       "  -0.05651382996143504,\n",
       "  -0.050957476003840006],\n",
       " '6:3:0:55:0': [-0.05556839303213309,\n",
       "  -0.05097621710738148,\n",
       "  -0.053052915483934635,\n",
       "  -0.049315025560141196],\n",
       " '5:5:180:55:0': [-0.052868628480000006,\n",
       "  -0.051701891006279435,\n",
       "  -0.05067692308909348,\n",
       "  -0.05176748263336988],\n",
       " '5:5:270:55:0': [-0.0458589116788979,\n",
       "  -0.04445826220032001,\n",
       "  -0.054997368667691254,\n",
       "  -0.05364338229187209],\n",
       " '6:5:270:55:0': [-0.05047552,\n",
       "  -0.0311005118464,\n",
       "  -0.05508847151338775,\n",
       "  -0.05521639617459216],\n",
       " '6:5:180:55:0': [-0.052755809802063666,\n",
       "  -0.05160160395190758,\n",
       "  -0.05865742694854629,\n",
       "  -0.05765159877783844],\n",
       " '6:5:90:55:0': [-0.05515930451968001,\n",
       "  -0.056,\n",
       "  -0.05786208095389926,\n",
       "  -0.05567887430086899],\n",
       " '6:6:0:55:0': [-0.048,\n",
       "  -0.05302078917836801,\n",
       "  -0.0565202509824,\n",
       "  -0.04867708927994279],\n",
       " '6:4:180:55:0': [-0.05147889680095243,\n",
       "  -0.05941362313730847,\n",
       "  -0.05661350323759909,\n",
       "  -0.052378168852480006],\n",
       " '6:4:90:55:0': [-0.05069711867824899,\n",
       "  -0.051532288,\n",
       "  -0.058114113444095804,\n",
       "  -0.05768039137280001],\n",
       " '5:5:90:55:0': [-0.05094657692139403,\n",
       "  -0.060044985396656494,\n",
       "  -0.04777330524155423,\n",
       "  -0.05274250090822983],\n",
       " '5:5:0:55:0': [-0.051439220326400005,\n",
       "  -0.04802502983621407,\n",
       "  -0.05692651259815606,\n",
       "  -0.05120119078912001],\n",
       " '4:4:270:55:0': [-0.04993690983300195,\n",
       "  -0.05003695505408,\n",
       "  -0.04891303224919752,\n",
       "  -0.05195485593600001],\n",
       " '5:4:90:55:0': [-0.0483247112190169,\n",
       "  -0.049695600640000004,\n",
       "  -0.05640539013100225,\n",
       "  -0.05418752000000001],\n",
       " '4:4:0:55:0': [-0.051399822409728006,\n",
       "  -0.04799013519753216,\n",
       "  -0.05202272180629938,\n",
       "  -0.0493614512133556],\n",
       " '4:3:180:55:0': [-0.051037019504640006,\n",
       "  -0.05282079010062336,\n",
       "  -0.04717549568000001,\n",
       "  -0.05711625827909633],\n",
       " '4:4:180:55:0': [-0.04753048539889664,\n",
       "  -0.046057559320166405,\n",
       "  -0.05181127311360001,\n",
       "  -0.05190297406664406],\n",
       " '4:5:180:55:0': [-0.046979507984793605,\n",
       "  -0.0496243499008,\n",
       "  -0.029396520534016,\n",
       "  -0.04373825372145352],\n",
       " '4:6:180:55:0': [-0.0180063605948416,\n",
       "  -0.0496,\n",
       "  -0.0461803380736,\n",
       "  -0.04198522880000001],\n",
       " '4:6:270:55:0': [-0.04240302358528,\n",
       "  -0.044682018816,\n",
       "  -0.04411070464,\n",
       "  -0.040899375104000005],\n",
       " '4:5:270:55:0': [-0.052208538909028115,\n",
       "  -0.023826545770495998,\n",
       "  -0.049574157090816,\n",
       "  -0.047933788160000004],\n",
       " '4:5:90:55:0': [-0.04785836321066189,\n",
       "  -0.048611234283366195,\n",
       "  -0.04984789303296,\n",
       "  -0.044588406505472],\n",
       " '4:6:0:55:0': [-0.049175552000000004,\n",
       "  -0.052593855987712004,\n",
       "  -0.0500830994432,\n",
       "  -0.04539979776],\n",
       " '4:6:90:55:0': [-0.042340556800000005,\n",
       "  -0.056692187136,\n",
       "  -0.0496659652608,\n",
       "  -0.05043488768000001],\n",
       " '5:6:90:55:0': [-0.05051357298688001,\n",
       "  -0.05661061283803379,\n",
       "  -0.0510123425792,\n",
       "  -0.05761406484480001],\n",
       " '5:6:270:55:0': [-0.045889449984000005,\n",
       "  -0.04576801062912,\n",
       "  -0.05282399092736,\n",
       "  -0.050564530007391945],\n",
       " '6:6:270:55:0': [-0.043532288,\n",
       "  -0.05048155602944,\n",
       "  -0.04731052900352,\n",
       "  -0.04539541503633789],\n",
       " '5:6:180:55:0': [-0.05292091116125106,\n",
       "  -0.043841060864000005,\n",
       "  -0.043882748313600006,\n",
       "  -0.0513576771584],\n",
       " '5:2:0:55:0': [-0.054010462208,\n",
       "  -0.05564127333449728,\n",
       "  -0.05476422385664,\n",
       "  -0.06605775840804776],\n",
       " '5:2:270:55:0': [-0.051781593497600004,\n",
       "  -0.05486580038500353,\n",
       "  -0.05135674198065153,\n",
       "  -0.05267300341466475],\n",
       " '5:2:180:55:0': [-0.05626780155904001,\n",
       "  -0.0603303897794758,\n",
       "  -0.059366588317696006,\n",
       "  -0.054661164005666195],\n",
       " '4:2:270:55:0': [-0.056797563642183684,\n",
       "  -0.05933330079416321,\n",
       "  -0.05700785139990136,\n",
       "  -0.05041388651151361],\n",
       " '4:2:180:55:0': [-0.05048773017600001,\n",
       "  -0.059273696110182406,\n",
       "  -0.04796182413312001,\n",
       "  -0.05360230355304449],\n",
       " '4:2:0:55:0': [-0.0506934724704338,\n",
       "  -0.05789140265089486,\n",
       "  -0.06291659881468396,\n",
       "  -0.050347738392166407],\n",
       " '4:2:90:55:0': [-0.054457407733760003,\n",
       "  -0.0542557542136662,\n",
       "  -0.05543370325360641,\n",
       "  -0.055912154718325974],\n",
       " '5:2:90:55:0': [-0.05431165527654401,\n",
       "  -0.04938505651433986,\n",
       "  -0.05470632738816,\n",
       "  -0.049638172262400006],\n",
       " '6:2:90:55:0': [-0.0490506911744,\n",
       "  -0.04319999999999999,\n",
       "  -0.05066511789520366,\n",
       "  -0.051181464803506105],\n",
       " '4:4:90:55:0': [-0.049537805451253025,\n",
       "  -0.04523675647926759,\n",
       "  -0.04957522739200001,\n",
       "  -0.0459326879563776],\n",
       " '2:5:180:55:0': [-0.048040354925248524,\n",
       "  -0.052304052224,\n",
       "  -0.05255442822479217,\n",
       "  -0.05187778242859682],\n",
       " '2:5:270:55:0': [-0.045787915878400004,\n",
       "  -0.05362864848896001,\n",
       "  -0.05025737277440001,\n",
       "  -0.05164376216604509],\n",
       " '1:5:270:55:0': [-0.04953473597440001,\n",
       "  -0.049970783027200004,\n",
       "  -0.046988652544000004,\n",
       "  -0.05295948038144],\n",
       " '0:5:270:55:0': [-0.041760435404800005,\n",
       "  -0.044561920000000005,\n",
       "  -0.044735690342400006,\n",
       "  -0.05430025154723841],\n",
       " '0:5:0:55:0': [-0.04610197419791182,\n",
       "  -0.045861842944,\n",
       "  -0.050696371044352,\n",
       "  -0.04940982272655361],\n",
       " '0:4:0:55:0': [-0.04306073263164727,\n",
       "  -0.047431300022272006,\n",
       "  -0.046951216414720005,\n",
       "  -0.051088449536],\n",
       " '0:3:0:55:0': [-0.047297377370112005,\n",
       "  -0.06093218693075743,\n",
       "  -0.051755363403038726,\n",
       "  -0.05823333917786113],\n",
       " '0:2:0:55:0': [-0.05396121954654619,\n",
       "  -0.05430160283888698,\n",
       "  -0.05493415712304323,\n",
       "  -0.05720067150500909],\n",
       " '0:1:0:55:0': [-0.05865261043538397,\n",
       "  -0.06113492221860367,\n",
       "  -0.06282630977138197,\n",
       "  -0.05643461735638045],\n",
       " '0:1:270:55:0': [-0.05218193823542615,\n",
       "  -0.057033728000000006,\n",
       "  -0.05825956942817021,\n",
       "  -0.05732954336497779],\n",
       " '0:1:90:55:0': [-0.058737967104,\n",
       "  -0.05577084125970433,\n",
       "  -0.06006071029137408,\n",
       "  -0.05905988710259564],\n",
       " '0:3:90:55:0': [-0.052089568133120004,\n",
       "  -0.056833134248853515,\n",
       "  -0.05652162913370113,\n",
       "  -0.05461377545281675],\n",
       " '0:3:180:55:0': [-0.054993526187622405,\n",
       "  -0.054685136584704,\n",
       "  -0.057452200591360005,\n",
       "  -0.058848287687246946],\n",
       " '0:4:180:55:0': [-0.05682751970344961,\n",
       "  -0.0558329292783616,\n",
       "  -0.04844075204608001,\n",
       "  -0.04680341913600001],\n",
       " '0:5:180:55:0': [-0.050499104571392006,\n",
       "  -0.04914992922624001,\n",
       "  -0.052256435117752326,\n",
       "  -0.0533273033965568],\n",
       " '0:6:180:55:0': [-0.05112681646653441,\n",
       "  -0.043699199999999994,\n",
       "  -0.042034745344,\n",
       "  -0.042030182400000005],\n",
       " '0:6:270:55:0': [-0.04178966528,\n",
       "  -0.04550399982118607,\n",
       "  -0.042090676224000007,\n",
       "  -0.0448884662272],\n",
       " '1:6:270:55:0': [-0.05102645739520001,\n",
       "  -0.044020920320000004,\n",
       "  -0.047837433856000006,\n",
       "  -0.04836472832],\n",
       " '1:6:180:55:0': [-0.047475831603200014,\n",
       "  -0.04832,\n",
       "  -0.05034661888000001,\n",
       "  -0.042582122496],\n",
       " '1:6:90:55:0': [-0.041677697024,\n",
       "  -0.042115252224000005,\n",
       "  -0.042362354892800005,\n",
       "  -0.04750983168],\n",
       " '1:5:180:55:0': [-0.053170888540160006,\n",
       "  -0.04852814069760001,\n",
       "  -0.04980288552960001,\n",
       "  -0.050648839815017624],\n",
       " '1:4:180:55:0': [-0.049533825024000014,\n",
       "  -0.0542088315967254,\n",
       "  -0.05167027650560001,\n",
       "  -0.04863284920287775],\n",
       " '1:4:90:55:0': [-0.04514026323968001,\n",
       "  -0.047409128316519764,\n",
       "  -0.04881499258880001,\n",
       "  -0.05330941214720001],\n",
       " '2:0:0:55:0': [-0.059612683786937966,\n",
       "  -0.0671872,\n",
       "  -0.06615995379950289,\n",
       "  -0.06115068979314688],\n",
       " '2:0:270:55:0': [-0.06522239959433217,\n",
       "  -0.07039556997991317,\n",
       "  -0.061479049550050104,\n",
       "  -0.06176728489000959],\n",
       " '1:0:270:55:0': [-0.06904307758384809,\n",
       "  -0.06895878484854191,\n",
       "  -0.06914329231275577,\n",
       "  -0.06730290728602038],\n",
       " '0:0:270:55:0': [-0.06449999070155998,\n",
       "  -0.06045868031885559,\n",
       "  -0.06170278828426952,\n",
       "  -0.06188768941845049],\n",
       " '1:0:0:55:0': [-0.05920943498854401,\n",
       "  -0.064384,\n",
       "  -0.06607383119995812,\n",
       "  -0.07380654188108314],\n",
       " '1:1:0:55:0': [-0.05577094004039746,\n",
       "  -0.05994227680411531,\n",
       "  -0.05517272239308801,\n",
       "  -0.05503442656296961],\n",
       " '1:1:90:55:0': [-0.05842630706066575,\n",
       "  -0.05336254765465601,\n",
       "  -0.058790388990948156,\n",
       "  -0.052118411673600006],\n",
       " '1:1:180:55:0': [-0.06380503794201609,\n",
       "  -0.05685186352602364,\n",
       "  -0.059674805698560006,\n",
       "  -0.05586707878379521],\n",
       " '1:0:180:55:0': [-0.06571407710748672,\n",
       "  -0.06399803759237531,\n",
       "  -0.06546632918221189,\n",
       "  -0.06810261192427386],\n",
       " '0:0:0:55:0': [-0.0626993738711151,\n",
       "  -0.06019351645374152,\n",
       "  -0.06534320443097089,\n",
       "  -0.06809981570062912],\n",
       " '0:0:90:55:0': [-0.0616320979294895,\n",
       "  -0.06800191431394723,\n",
       "  -0.05747233508205238,\n",
       "  -0.05892198932281436],\n",
       " '0:0:180:55:0': [-0.056469504000000004,\n",
       "  -0.05475657364260552,\n",
       "  -0.0663726792061184,\n",
       "  -0.06374256825189165],\n",
       " '1:0:90:55:0': [-0.06581944114825135,\n",
       "  -0.06773914798475184,\n",
       "  -0.06436576197501886,\n",
       "  -0.07168994219760026],\n",
       " '1:1:270:55:0': [-0.05781894990397441,\n",
       "  -0.05713307860228268,\n",
       "  -0.050860821708800005,\n",
       "  -0.05317111982823834],\n",
       " '2:1:270:55:0': [-0.06087360838551943,\n",
       "  -0.05382032834560001,\n",
       "  -0.05878610144788481,\n",
       "  -0.05483239768064001],\n",
       " '2:1:180:55:0': [-0.0538125950976,\n",
       "  -0.05525542222246708,\n",
       "  -0.05244953152716801,\n",
       "  -0.05781736613478401],\n",
       " '2:3:90:55:0': [-0.056717332480000014,\n",
       "  -0.05006677508096001,\n",
       "  -0.048885278343168005,\n",
       "  -0.05744635294247814],\n",
       " '1:3:90:55:0': [-0.058652917186953216,\n",
       "  -0.049579359240192016,\n",
       "  -0.05043823763757466,\n",
       "  -0.04894089543680001],\n",
       " '1:3:180:55:0': [-0.04522407442572641,\n",
       "  -0.04905946726400001,\n",
       "  -0.03913119121408,\n",
       "  -0.051503689728000006],\n",
       " '0:5:90:55:0': [-0.04832,\n",
       "  -0.05658419223808908,\n",
       "  -0.056215630151680004,\n",
       "  -0.0483948413190144],\n",
       " '0:6:0:55:0': [-0.041577697280000006,\n",
       "  -0.041780838400000006,\n",
       "  -0.04441815040000001,\n",
       "  -0.0470011696062424],\n",
       " '0:6:90:55:0': [-0.04,\n",
       "  -0.04278108160000001,\n",
       "  -0.038218674176,\n",
       "  -0.048412647424000005],\n",
       " '2:6:90:55:0': [-0.04729216286720001,\n",
       "  -0.047326942822400006,\n",
       "  -0.05196886261760001,\n",
       "  -0.0480025796608],\n",
       " '1:6:0:55:0': [-0.041977344,\n",
       "  -0.045630726144,\n",
       "  -0.04619851776,\n",
       "  -0.042986188800000005],\n",
       " '1:5:0:55:0': [-0.042628714496,\n",
       "  -0.0456364122112,\n",
       "  -0.05193100818157405,\n",
       "  -0.045576789196800006],\n",
       " '1:4:0:55:0': [-0.05072024354816,\n",
       "  -0.04639062373171201,\n",
       "  -0.05130197073920001,\n",
       "  -0.05184939556864001],\n",
       " '0:4:270:55:0': [-0.04272974217216,\n",
       "  -0.0524928,\n",
       "  -0.04992497664000001,\n",
       "  -0.04882684513484801],\n",
       " '0:4:90:55:0': [-0.041673728,\n",
       "  -0.040659456000000004,\n",
       "  -0.0510193763024896,\n",
       "  -0.05050452574208],\n",
       " '0:2:270:55:0': [-0.053779612382857125,\n",
       "  -0.05337783893261328,\n",
       "  -0.055567728454990514,\n",
       "  -0.05316805900811178],\n",
       " '0:2:180:55:0': [-0.054860670494310405,\n",
       "  -0.05722867561424488,\n",
       "  -0.058132520115846985,\n",
       "  -0.058319769033559246],\n",
       " '0:3:270:55:0': [-0.05338466752921601,\n",
       "  -0.052016566272000006,\n",
       "  -0.055728603863449605,\n",
       "  -0.05273327566848001],\n",
       " '1:3:270:55:0': [-0.05186856805990401,\n",
       "  -0.051380618035200006,\n",
       "  -0.05188881350656001,\n",
       "  -0.047543852171264],\n",
       " '4:3:270:55:0': [-0.04768125336428126,\n",
       "  -0.04586426223689728,\n",
       "  -0.043697270784,\n",
       "  -0.05257080015945729],\n",
       " '5:3:270:55:0': [-0.054634617382007145,\n",
       "  -0.04651505664000001,\n",
       "  -0.055466981684671006,\n",
       "  -0.050354012648590324],\n",
       " '6:3:270:55:0': [-0.05327063028555908,\n",
       "  -0.046804331528192,\n",
       "  -0.05105150583671153,\n",
       "  -0.05033629286361548],\n",
       " '6:2:0:55:0': [-0.0509458956466078,\n",
       "  -0.05442322808832001,\n",
       "  -0.05031764822386285,\n",
       "  -0.050646188032],\n",
       " '6:1:0:55:0': [-0.04695262183322751,\n",
       "  -0.0519232233472,\n",
       "  -0.050152973762560005,\n",
       "  -0.053481683618417875],\n",
       " '6:2:180:55:0': [-0.047133625548800004,\n",
       "  -0.05171745328081946,\n",
       "  -0.046381511475200005,\n",
       "  -0.04635612682538155],\n",
       " '6:3:180:55:0': [-0.048205497139200006,\n",
       "  -0.055200922075018824,\n",
       "  -0.04972590058695079,\n",
       "  -0.051966374539610005],\n",
       " '6:3:90:55:0': [-0.05069151551050892,\n",
       "  -0.051850932220154786,\n",
       "  -0.05400727770840436,\n",
       "  -0.05187223442376522],\n",
       " '5:1:0:55:0': [-0.05646732935168001,\n",
       "  -0.052095447040000006,\n",
       "  -0.05620446357775975,\n",
       "  -0.05011217776640001],\n",
       " '5:1:90:55:0': [-0.055083163365582416,\n",
       "  -0.05277956960944128,\n",
       "  -0.05867686615449601,\n",
       "  -0.05767464566784001],\n",
       " '4:1:90:55:0': [-0.057556350818595954,\n",
       "  -0.058305996167632136,\n",
       "  -0.05913049378213835,\n",
       "  -0.05607054768105063],\n",
       " '4:1:0:55:0': [-0.05373909717200405,\n",
       "  -0.0643844576517664,\n",
       "  -0.05574670535953545,\n",
       "  -0.061136794693455065],\n",
       " '5:0:270:55:0': [-0.05465362366464,\n",
       "  -0.05939852639510464,\n",
       "  -0.055014117081088006,\n",
       "  -0.05156534632448],\n",
       " '6:0:270:55:0': [-0.056,\n",
       "  -0.05476093840588801,\n",
       "  -0.0506205298688,\n",
       "  -0.05111224113561601],\n",
       " '6:0:180:55:0': [-0.054089441280000004,\n",
       "  -0.05453795714446401,\n",
       "  -0.0590865706385408,\n",
       "  -0.048775367065600005],\n",
       " '6:1:180:55:0': [-0.053103137587200006,\n",
       "  -0.052970260364772,\n",
       "  -0.048115069812736,\n",
       "  -0.05028822384640001],\n",
       " '6:1:90:55:0': [-0.04807003766784,\n",
       "  -0.05126144,\n",
       "  -0.049899549589504,\n",
       "  -0.05077519138816001],\n",
       " '6:0:0:55:0': [-0.05286424821760001,\n",
       "  -0.050603008,\n",
       "  -0.0504253472768,\n",
       "  -0.054473195356160006],\n",
       " '6:0:90:55:0': [-0.0507491770368,\n",
       "  -0.05241878528,\n",
       "  -0.054740354433024004,\n",
       "  -0.056257598095360004],\n",
       " '6:1:270:55:0': [-0.048,\n",
       "  -0.058828104586168904,\n",
       "  -0.051887877816320004,\n",
       "  -0.04993166229504001],\n",
       " '5:1:270:55:0': [-0.05612121473024,\n",
       "  -0.05451750405628232,\n",
       "  -0.05154437030320538,\n",
       "  -0.05716312913346561],\n",
       " '4:1:270:55:0': [-0.056106015400604475,\n",
       "  -0.05479541207859201,\n",
       "  -0.058637793139235096,\n",
       "  -0.05899317675035555],\n",
       " '4:1:180:55:0': [-0.06549496724695548,\n",
       "  -0.05339633146265601,\n",
       "  -0.05521362853560321,\n",
       "  -0.05638160979515419],\n",
       " '5:1:180:55:0': [-0.057959817216,\n",
       "  -0.06116056036128449,\n",
       "  -0.04906447090483201,\n",
       "  -0.054768640032768004],\n",
       " '4:5:0:55:0': [-0.05006377369600001,\n",
       "  -0.046542678720512,\n",
       "  -0.05092312353996801,\n",
       "  -0.04708914013798401],\n",
       " '3:6:270:55:0': [-0.047246427914240004,\n",
       "  -0.05378617904988161,\n",
       "  -0.048606011392000006,\n",
       "  -0.04852535296000001],\n",
       " '0:2:90:55:0': [-0.0534798336,\n",
       "  -0.06402073057688551,\n",
       "  -0.056624079116579976,\n",
       "  -0.05474526506239761],\n",
       " '1:2:90:55:0': [-0.06129610828721346,\n",
       "  -0.059344709582848015,\n",
       "  -0.05649180375525333,\n",
       "  -0.05874243948044616],\n",
       " '1:2:180:55:0': [-0.06372052928605425,\n",
       "  -0.05092941590691841,\n",
       "  -0.04910064675314368,\n",
       "  -0.05448639212349608],\n",
       " '5:0:90:55:0': [-0.057514024800102655,\n",
       "  -0.05760545931264001,\n",
       "  -0.05990318050508801,\n",
       "  -0.0639762336382976],\n",
       " '6:6:90:55:0': [-0.05444993892352001,\n",
       "  -0.05142016,\n",
       "  -0.05269962162161352,\n",
       "  -0.053108811366308455],\n",
       " '6:6:180:55:0': [-0.05394512031977263,\n",
       "  -0.0496,\n",
       "  -0.043994141818880006,\n",
       "  -0.0434822070270169],\n",
       " '3:6:90:55:0': [-0.0442026213376,\n",
       "  -0.04969011200000001,\n",
       "  -0.047618732032000004,\n",
       "  -0.04389632],\n",
       " '2:6:180:55:0': [-0.05120102236160001,\n",
       "  -0.051466649600000004,\n",
       "  -0.04886569222144001,\n",
       "  -0.05071589703680001],\n",
       " '2:6:270:55:0': [-0.05401619531694081,\n",
       "  -0.04867526656,\n",
       "  -0.05044255952076801,\n",
       "  -0.046939081932800004],\n",
       " '2:6:0:55:0': [-0.0418614272,\n",
       "  -0.047313144526929925,\n",
       "  -0.05142357852160001,\n",
       "  -0.051169533041049604],\n",
       " '1:5:90:55:0': [-0.05489726550835201,\n",
       "  -0.05265174057416378,\n",
       "  -0.05034746035121708,\n",
       "  -0.0507475623936],\n",
       " '3:6:180:55:0': [-0.03973410816, -0.04, -0.0408045568, -0.05021652172800001],\n",
       " '3:6:0:55:0': [-0.044563968,\n",
       "  -0.049808059122581516,\n",
       "  -0.042244628480000004,\n",
       "  -0.06008801280000001],\n",
       " '2:0:180:55:0': [-0.0543084544,\n",
       "  -0.054538277879808014,\n",
       "  -0.06686459240752533,\n",
       "  -0.0668078451089893],\n",
       " '3:0:0:55:0': [-0.06357908485606842,\n",
       "  -0.05849561979420673,\n",
       "  -0.05748076633548391,\n",
       "  -0.061326736949248006],\n",
       " '1:2:270:55:0': [-0.053548038848512,\n",
       "  -0.05724056034112654,\n",
       "  -0.061754724888084485,\n",
       "  -0.05332870257863296],\n",
       " '1:2:0:55:0': [-0.0490850380671982,\n",
       "  -0.05703280394240001,\n",
       "  -0.05824488237826049,\n",
       "  -0.059303280725930815],\n",
       " '1:3:0:55:0': [-0.052188061728768005,\n",
       "  -0.05791302407005668,\n",
       "  -0.05275519598067713,\n",
       "  -0.051290774732800005],\n",
       " '2:3:0:55:0': [-0.05137466762854401,\n",
       "  -0.05748162264180983,\n",
       "  -0.05679078903214245,\n",
       "  -0.05190419773563863],\n",
       " '2:2:0:55:0': [-0.05085440627205736,\n",
       "  -0.0593252715575378,\n",
       "  -0.051359303139328,\n",
       "  -0.05612641593044501],\n",
       " '2:1:0:55:0': [-0.056746383147532296,\n",
       "  -0.06012996975329281,\n",
       "  -0.055715044779360255,\n",
       "  -0.06181659443200001],\n",
       " '2:2:90:55:0': [-0.060685241966067324,\n",
       "  -0.058707731382272005,\n",
       "  -0.05321910033580033,\n",
       "  -0.05765562395498907],\n",
       " '3:2:90:55:0': [-0.05375148717899777,\n",
       "  -0.06098536129542226,\n",
       "  -0.05316240683827201,\n",
       "  -0.055383435731271684],\n",
       " '5:0:180:55:0': [-0.05827121152,\n",
       "  -0.058392021489225406,\n",
       "  -0.05173057699840001,\n",
       "  -0.06340090445824001],\n",
       " '6:2:270:55:0': [-0.0515820544,\n",
       "  -0.05153406722244608,\n",
       "  -0.052859334656000005,\n",
       "  -0.047647727448864106],\n",
       " '5:6:0:55:0': [-0.0532992,\n",
       "  -0.052663984127999997,\n",
       "  -0.051237106483200005,\n",
       "  -0.051789079511040004],\n",
       " '5:0:0:55:0': [-0.05606460831039488,\n",
       "  -0.05632,\n",
       "  -0.06027315901235201,\n",
       "  -0.05936405594112001],\n",
       " '4:0:0:56:0': [-0.032754222530560005,\n",
       "  -0.03552672169984,\n",
       "  -0.04869182957158401,\n",
       "  -0.008],\n",
       " '2:0:90:55:0': [-0.07001052916062618,\n",
       "  -0.06562072588661347,\n",
       "  -0.06306295220246937,\n",
       "  -0.0679053516133735],\n",
       " '2:1:90:55:0': [-0.05744366151651898,\n",
       "  -0.0616595336766423,\n",
       "  -0.05792760106241229,\n",
       "  -0.05739049729392641],\n",
       " '2:5:0:55:0': [-0.04936828911616001,\n",
       "  -0.042937790376760064,\n",
       "  -0.046792142778007556,\n",
       "  -0.04932034232320001],\n",
       " '2:5:90:55:0': [-0.05122234529090483,\n",
       "  -0.01672287317666038,\n",
       "  -0.05015632265216001,\n",
       "  -0.04973759902515201],\n",
       " '3:5:90:55:0': [-0.015820578760425474,\n",
       "  -0.041287624290737894,\n",
       "  -0.041069096796160005,\n",
       "  -0.04823882742759424],\n",
       " '3:5:0:55:0': [-0.04614225891740878,\n",
       "  -0.0415807512444928,\n",
       "  -0.042009730471690244,\n",
       "  -0.02820830220700877],\n",
       " '3:5:180:55:0': [-0.04126620426399908,\n",
       "  -0.041685504000000005,\n",
       "  -0.043623015383040004,\n",
       "  -0.042359162064076805],\n",
       " '3:5:270:55:0': [-0.0302034400903168,\n",
       "  -0.038746816512,\n",
       "  -0.043255314078760966,\n",
       "  -0.04054128640000001],\n",
       " '0:1:180:55:0': [-0.054935882951306844,\n",
       "  -0.06168706740814246,\n",
       "  -0.05166094345066668,\n",
       "  -0.0513649738776576],\n",
       " '2:4:0:55:0': [-0.04714006962634753,\n",
       "  -0.055043667165184,\n",
       "  -0.0488136161682188,\n",
       "  -0.04496592896],\n",
       " '0:0:0:56:0': [-0.03180457984000001,\n",
       "  -0.03437863321600001,\n",
       "  -0.050786927528209506,\n",
       "  0],\n",
       " '2:0:0:56:0': [-0.02336, -0.03926046301749248, -0.047604373790916615, 0],\n",
       " '1:0:0:56:0': [-0.04508255826608129, -0.03639095603620742, 0, 0]}"
      ]
     },
     "execution_count": 653,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Qtable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4:4\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "positiveGrid(xs,zs,Qtable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "printGrid(xs,zs,Qtable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def positiveGrid(xs,zs,Qtable):\n",
    "    \n",
    "    xLen = max(xs); zLen = max(zs);  \n",
    "    positiveGrid = np.zeros(((xLen+1)*36,(zLen+1)*36))\n",
    "    block = np.ones((int(36),int(36)))\n",
    "\n",
    "    for key in Qtable:\n",
    "        values = key.split(\":\")\n",
    "        x = float(values[0])\n",
    "        z = float(values[1])\n",
    "        reward = max(Qtable[key])\n",
    "        if reward > 0:\n",
    "            gridXStart = int(x*36)\n",
    "            gridXEnd = int(x*36 + 36)\n",
    "            gridZStart = int(z*36)\n",
    "            gridZEnd = int(z*36 + 36)\n",
    "            print(\"%s:%s\"%(int(x),int(z)))\n",
    "\n",
    "            positiveGrid[gridXStart:gridXEnd,gridZStart:gridZEnd] += block*(reward/4)\n",
    "\n",
    "    ax = plt.axes()\n",
    "    ax.imshow(positiveGrid)\n",
    "\n",
    "    ax.set_xticks([(i*36 + 18 ) for i in range(xLen+1)]);\n",
    "    ax.set_xticklabels([str(i) for i in range(xLen+1)]);\n",
    "    ax.set_yticks([(i*36 + 18 ) for i in range(xLen+1)]);\n",
    "    ax.set_yticklabels([str(i) for i in range(xLen+1)]);\n",
    "    \n",
    "def printGrid(xs,zs,Qtable):\n",
    "\n",
    "    xLen = max(xs); zLen = max(zs);  \n",
    "    grid = np.ones(((xLen+1)*36,(zLen+1)*36))\n",
    "    block = np.ones((int(36),int(36)))\n",
    "\n",
    "    for key in Qtable:\n",
    "        values = key.split(\":\")\n",
    "        x = float(values[0])\n",
    "        z = float(values[1])\n",
    "        reward = max(Qtable[key])\n",
    "        if reward > 0:\n",
    "            reward *= 0.1\n",
    "\n",
    "        gridXStart = int(x*36)\n",
    "        gridXEnd = int(x*36 + 36)\n",
    "        gridZStart = int(z*36)\n",
    "        gridZEnd = int(z*36 + 36)\n",
    "\n",
    "        grid[gridXStart:gridXEnd,gridZStart:gridZEnd] += block*(reward/4)\n",
    "\n",
    "    ax = plt.axes()\n",
    "    ax.imshow(grid)\n",
    "\n",
    "    ax.set_xticks([(i*36 + 18 ) for i in range(xLen+1)]);\n",
    "    ax.set_xticklabels([str(i) for i in range(xLen+1)]);\n",
    "    ax.set_yticks([(i*36 + 18 ) for i in range(xLen+1)]);\n",
    "    ax.set_yticklabels([str(i) for i in range(xLen+1)]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "invalid index to scalar variable.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-15-68b002798da4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplotScores\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Results_Alpha_0_5_Gamma_1_Decay_0_97.csv\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;31m#plotScores(\"QLearning_completeAction\\Results_Alpha_0_8_Gamma_1_Decay_0_97.csv\")\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-6-88d34d1c0a34>\u001b[0m in \u001b[0;36mplotScores\u001b[1;34m(file)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mplotScores\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mmy_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgenfromtxt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m','\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmy_data\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0mtime\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmy_data\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mepsilon\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmy_data\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-6-88d34d1c0a34>\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mplotScores\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mmy_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgenfromtxt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m','\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmy_data\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0mtime\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmy_data\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mepsilon\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmy_data\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: invalid index to scalar variable."
     ]
    }
   ],
   "source": [
    "plotScores(\"Results_Alpha_0_5_Gamma_1_Decay_0_97.csv\")\n",
    "#plotScores(\"QLearning_completeAction\\Results_Alpha_0_8_Gamma_1_Decay_0_97.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 844,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Games:  140  - Average Time:  220.20714285714286\n",
      "Average reward for the last 5 games: 0.33 with an epsilon of 0.00\n",
      "Scores that have reached over 0: 121 out of 140\n",
      "Scores that have reached -2.5: 3 out of 140\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotScores(\"Results_Alpha_0_8_Gamma_0_5_Decay_0_97.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Games:  173  - Average Time:  199.8612716763006\n",
      "Average reward for the last 5 games: 0.27 with an epsilon of 0.00\n",
      "Scores that have reached over 0: 152 out of 173\n",
      "Scores that have reached -2.5: 1 out of 173\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotScores(\"QLearning_completeAction\\Results_Alpha_0_8_Gamma_1_Decay_0_97.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def plotScores(file):\n",
    "    my_data = genfromtxt(file, delimiter=',')\n",
    "    scores = [x[0] for x in my_data]\n",
    "    time = [x[2] for x in my_data]\n",
    "    epsilon = [x[-1] for x in my_data]\n",
    "\n",
    "    print(\"Games: \" , str(len(scores)), \" - Average Time: \", str(sum(time)/len(scores)))\n",
    "    print(\"Average reward for the last 5 games: %.2f with an epsilon of %.2f\" % (sum(scores[-5:])/5, epsilon[-1]))\n",
    "    plt.plot(scores);\n",
    "\n",
    "    lowCount = 0\n",
    "    highCount = 0\n",
    "    for score in scores:\n",
    "        if score < -2.4:\n",
    "            lowCount += 1\n",
    "        if score > 0:\n",
    "            highCount += 1\n",
    "    print(\"Scores that have reached over 0: %s out of %s\"%(highCount,len(scores)))\n",
    "    print(\"Scores that have reached -2.5: %s out of %s\"%(lowCount,len(scores)))\n",
    "    \n",
    "    averageScores = []\n",
    "    for i,score in enumerate(scores):\n",
    "        averageScores.append((sum(scores[:(i+1)])/(i+1)))\n",
    "\n",
    "    plt.plot(averageScores);\n",
    "    plt.title(\"Scores for %s games - total average score is %.3f\"%(len(averageScores),averageScores[-1]));\n",
    "    plt.xlabel(\"Game\");\n",
    "    plt.ylabel(\"Score\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 790,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 768,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epsilon);\n",
    "plt.title(\"Epsilon of %0.2f\"%(epsilon[-1]));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}