plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from scipy.ndimage.filters import gaussian_filter1d\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "\n",
    "\n",
    "def AVERAGE(data,factor = 0.3):\n",
    "    return(gaussian_filter1d(np.array(data), sigma=factor))\n",
    "    \n",
    "def sd_format_plot(result, types_lin = None):\n",
    "    mean , sd = np.mean(result,axis= 0), np.std(result,axis= 0)\n",
    "    x = range(1,len(mean) + 1)\n",
    "    if types_lin:\n",
    "        plt.plot(x, mean,types_lin) \n",
    "    else:\n",
    "        plt.plot(x, mean) \n",
    "    plt.fill_between(x, mean - sd, mean + sd, alpha=0.25)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======Start======\n",
      "======Train id: 0 ======\n",
      "Epoch: 0 P Loss: 10.351604577691626\n",
      "Social Welfare of Episode  0 , (nu, random): 23.195893725401163\n",
      "Social Welfare of Episode  0 , (nu, pi): 23.195893725401163\n",
      "Social Welfare of Episode  0 , (nu, pi*(nu)): 34.80349584390366\n",
      "Social Welfare of Episode  0 , (nu*(nu), pi*(nu*(nu))): 34.80354466683837\n",
      "Epoch: 1 P Loss: 10.214762621129609\n",
      "Social Welfare of Episode  1 , (nu, random): 23.1579533989526\n",
      "Social Welfare of Episode  1 , (nu, pi): 23.158128938112192\n",
      "Social Welfare of Episode  1 , (nu, pi*(nu)): 34.7429234476534\n",
      "Social Welfare of Episode  1 , (nu*(nu), pi*(nu*(nu))): 34.74293661893348\n",
      "Epoch: 2 P Loss: 10.113098562195411\n",
      "Social Welfare of Episode  2 , (nu, random): 23.178955769874943\n",
      "Social Welfare of Episode  2 , (nu, pi): 23.179242397419948\n",
      "Social Welfare of Episode  2 , (nu, pi*(nu)): 34.70894349889977\n",
      "Social Welfare of Episode  2 , (nu*(nu), pi*(nu*(nu))): 34.70906048812863\n",
      "Epoch: 3 P Loss: 10.00860188086411\n",
      "Social Welfare of Episode  3 , (nu, random): 23.21508641372967\n",
      "Social Welfare of Episode  3 , (nu, pi): 23.284662154861262\n",
      "Social Welfare of Episode  3 , (nu, pi*(nu)): 34.773035849471434\n",
      "Social Welfare of Episode  3 , (nu*(nu), pi*(nu*(nu))): 34.773076162621074\n",
      "Epoch: 4 P Loss: 10.010538395875512\n",
      "Social Welfare of Episode  4 , (nu, random): 23.190925185907798\n",
      "Social Welfare of Episode  4 , (nu, pi): 23.329668024584247\n",
      "Social Welfare of Episode  4 , (nu, pi*(nu)): 34.759408754646955\n",
      "Social Welfare of Episode  4 , (nu*(nu), pi*(nu*(nu))): 34.75945787041398\n",
      "Epoch: 5 P Loss: 9.956972518239697\n",
      "Social Welfare of Episode  5 , (nu, random): 23.191253950580133\n",
      "Social Welfare of Episode  5 , (nu, pi): 23.39841731190026\n",
      "Social Welfare of Episode  5 , (nu, pi*(nu)): 34.76465860852496\n",
      "Social Welfare of Episode  5 , (nu*(nu), pi*(nu*(nu))): 34.79714605058781\n",
      "Epoch: 6 P Loss: 9.953398898593754\n",
      "Social Welfare of Episode  6 , (nu, random): 23.196007446210846\n",
      "Social Welfare of Episode  6 , (nu, pi): 23.471351103591072\n",
      "Social Welfare of Episode  6 , (nu, pi*(nu)): 34.79692436966353\n",
      "Social Welfare of Episode  6 , (nu*(nu), pi*(nu*(nu))): 34.98859285003881\n",
      "Epoch: 7 P Loss: 9.825261239665107\n",
      "Social Welfare of Episode  7 , (nu, random): 23.17645494157309\n",
      "Social Welfare of Episode  7 , (nu, pi): 23.516219915284953\n",
      "Social Welfare of Episode  7 , (nu, pi*(nu)): 34.738091878109906\n",
      "Social Welfare of Episode  7 , (nu*(nu), pi*(nu*(nu))): 34.73825234033075\n",
      "Epoch: 8 P Loss: 9.804741632214435\n",
      "Social Welfare of Episode  8 , (nu, random): 23.161279004177107\n",
      "Social Welfare of Episode  8 , (nu, pi): 23.572277085679925\n",
      "Social Welfare of Episode  8 , (nu, pi*(nu)): 34.744940160522496\n",
      "Social Welfare of Episode  8 , (nu*(nu), pi*(nu*(nu))): 34.7449587731805\n",
      "Epoch: 9 P Loss: 9.713369304090403\n",
      "Social Welfare of Episode  9 , (nu, random): 23.169229679672952\n",
      "Social Welfare of Episode  9 , (nu, pi): 23.647858444123358\n",
      "Social Welfare of Episode  9 , (nu, pi*(nu)): 34.74441259085455\n",
      "Social Welfare of Episode  9 , (nu*(nu), pi*(nu*(nu))): 34.74441786207308\n",
      "Epoch: 10 P Loss: 9.66345697256644\n",
      "Social Welfare of Episode  10 , (nu, random): 23.180853585962268\n",
      "Social Welfare of Episode  10 , (nu, pi): 23.724057641158044\n",
      "Social Welfare of Episode  10 , (nu, pi*(nu)): 34.73426747297482\n",
      "Social Welfare of Episode  10 , (nu*(nu), pi*(nu*(nu))): 34.840469620180414\n",
      "Epoch: 11 P Loss: 9.636984936330807\n",
      "Social Welfare of Episode  11 , (nu, random): 23.173094140416595\n",
      "Social Welfare of Episode  11 , (nu, pi): 23.77619984301672\n",
      "Social Welfare of Episode  11 , (nu, pi*(nu)): 34.710718453599384\n",
      "Social Welfare of Episode  11 , (nu*(nu), pi*(nu*(nu))): 34.71073349127054\n",
      "Epoch: 12 P Loss: 9.536591192847023\n",
      "Social Welfare of Episode  12 , (nu, random): 23.19005572521096\n",
      "Social Welfare of Episode  12 , (nu, pi): 23.84577529220071\n",
      "Social Welfare of Episode  12 , (nu, pi*(nu)): 34.76980013865583\n",
      "Social Welfare of Episode  12 , (nu*(nu), pi*(nu*(nu))): 34.76980013865583\n",
      "Epoch: 13 P Loss: 9.479937325606437\n",
      "Social Welfare of Episode  13 , (nu, random): 23.20358603187055\n",
      "Social Welfare of Episode  13 , (nu, pi): 23.924444123819015\n",
      "Social Welfare of Episode  13 , (nu, pi*(nu)): 34.80214962606532\n",
      "Social Welfare of Episode  13 , (nu*(nu), pi*(nu*(nu))): 35.066079349151465\n",
      "Epoch: 14 P Loss: 9.457335026075386\n",
      "Social Welfare of Episode  14 , (nu, random): 23.141049291527427\n",
      "Social Welfare of Episode  14 , (nu, pi): 23.91675939115929\n",
      "Social Welfare of Episode  14 , (nu, pi*(nu)): 34.685067512017866\n",
      "Social Welfare of Episode  14 , (nu*(nu), pi*(nu*(nu))): 34.81762834704629\n",
      "Epoch: 15 P Loss: 9.406237795769176\n",
      "Social Welfare of Episode  15 , (nu, random): 23.180057868694707\n",
      "Social Welfare of Episode  15 , (nu, pi): 24.014525883397283\n",
      "Social Welfare of Episode  15 , (nu, pi*(nu)): 34.723841390788785\n",
      "Social Welfare of Episode  15 , (nu*(nu), pi*(nu*(nu))): 34.86840503535591\n",
      "Epoch: 16 P Loss: 9.319560402202887\n",
      "Social Welfare of Episode  16 , (nu, random): 23.201355159875135\n",
      "Social Welfare of Episode  16 , (nu, pi): 24.097397399605637\n",
      "Social Welfare of Episode  16 , (nu, pi*(nu)): 34.776276110454205\n",
      "Social Welfare of Episode  16 , (nu*(nu), pi*(nu*(nu))): 34.97827518267844\n",
      "Epoch: 17 P Loss: 9.274736730048982\n",
      "Social Welfare of Episode  17 , (nu, random): 23.13486999582055\n",
      "Social Welfare of Episode  17 , (nu, pi): 24.08042465791732\n",
      "Social Welfare of Episode  17 , (nu, pi*(nu)): 34.66799411554854\n",
      "Social Welfare of Episode  17 , (nu*(nu), pi*(nu*(nu))): 34.66800368510628\n",
      "Epoch: 18 P Loss: 9.242336719746078\n",
      "Social Welfare of Episode  18 , (nu, random): 23.15766340573369\n",
      "Social Welfare of Episode  18 , (nu, pi): 24.151689105334043\n",
      "Social Welfare of Episode  18 , (nu, pi*(nu)): 34.70249408771894\n",
      "Social Welfare of Episode  18 , (nu*(nu), pi*(nu*(nu))): 34.70260606412775\n",
      "Epoch: 19 P Loss: 9.117586153110576\n",
      "Social Welfare of Episode  19 , (nu, random): 23.16756473236444\n",
      "Social Welfare of Episode  19 , (nu, pi): 24.194852772144415\n",
      "Social Welfare of Episode  19 , (nu, pi*(nu)): 34.709097766631544\n",
      "Social Welfare of Episode  19 , (nu*(nu), pi*(nu*(nu))): 34.709149873016045\n",
      "Epoch: 20 P Loss: 9.144061183523657\n",
      "Social Welfare of Episode  20 , (nu, random): 23.22152716405894\n",
      "Social Welfare of Episode  20 , (nu, pi): 24.291822538628463\n",
      "Social Welfare of Episode  20 , (nu, pi*(nu)): 34.846559371481845\n",
      "Social Welfare of Episode  20 , (nu*(nu), pi*(nu*(nu))): 34.84659589912074\n",
      "Epoch: 21 P Loss: 9.139200599249222\n",
      "Social Welfare of Episode  21 , (nu, random): 23.15725655341159\n",
      "Social Welfare of Episode  21 , (nu, pi): 24.249958603978882\n",
      "Social Welfare of Episode  21 , (nu, pi*(nu)): 34.71090902803227\n",
      "Social Welfare of Episode  21 , (nu*(nu), pi*(nu*(nu))): 34.71093429696461\n",
      "Epoch: 22 P Loss: 9.052813966199952\n",
      "Social Welfare of Episode  22 , (nu, random): 23.194382212122505\n",
      "Social Welfare of Episode  22 , (nu, pi): 24.312559887214466\n",
      "Social Welfare of Episode  22 , (nu, pi*(nu)): 34.74192054157587\n",
      "Social Welfare of Episode  22 , (nu*(nu), pi*(nu*(nu))): 35.090927897336094\n",
      "Epoch: 23 P Loss: 8.941090394229983\n",
      "Social Welfare of Episode  23 , (nu, random): 23.18588450586357\n",
      "Social Welfare of Episode  23 , (nu, pi): 24.349078945872503\n",
      "Social Welfare of Episode  23 , (nu, pi*(nu)): 34.74879083064505\n",
      "Social Welfare of Episode  23 , (nu*(nu), pi*(nu*(nu))): 34.801580754194205\n",
      "Epoch: 24 P Loss: 8.881132466845479\n",
      "Social Welfare of Episode  24 , (nu, random): 23.174601261877246\n",
      "Social Welfare of Episode  24 , (nu, pi): 24.383305697519223\n",
      "Social Welfare of Episode  24 , (nu, pi*(nu)): 34.76569374138401\n",
      "Social Welfare of Episode  24 , (nu*(nu), pi*(nu*(nu))): 34.765697504915494\n",
      "Epoch: 25 P Loss: 8.881132466845479\n",
      "Social Welfare of Episode  25 , (nu, random): 23.19513363623393\n",
      "Social Welfare of Episode  25 , (nu, pi): 24.421379032806463\n",
      "Social Welfare of Episode  25 , (nu, pi*(nu)): 34.77958647304045\n",
      "Social Welfare of Episode  25 , (nu*(nu), pi*(nu*(nu))): 34.77958647304045\n",
      "Epoch: 26 P Loss: 8.881132466845479\n",
      "Social Welfare of Episode  26 , (nu, random): 23.24291297147334\n",
      "Social Welfare of Episode  26 , (nu, pi): 24.482725080219833\n",
      "Social Welfare of Episode  26 , (nu, pi*(nu)): 34.79983874801265\n",
      "Social Welfare of Episode  26 , (nu*(nu), pi*(nu*(nu))): 34.79984109352861\n",
      "Epoch: 27 P Loss: 8.881132466845479\n",
      "Social Welfare of Episode  27 , (nu, random): 23.21360797804212\n",
      "Social Welfare of Episode  27 , (nu, pi): 24.470463244436917\n",
      "Social Welfare of Episode  27 , (nu, pi*(nu)): 34.746022949604054\n",
      "Social Welfare of Episode  27 , (nu*(nu), pi*(nu*(nu))): 34.754971139570344\n",
      "Epoch: 28 P Loss: 8.829071866410638\n",
      "Social Welfare of Episode  28 , (nu, random): 23.188480006546964\n",
      "Social Welfare of Episode  28 , (nu, pi): 24.462279786115687\n",
      "Social Welfare of Episode  28 , (nu, pi*(nu)): 34.74887457373551\n",
      "Social Welfare of Episode  28 , (nu*(nu), pi*(nu*(nu))): 34.76038324045685\n",
      "Epoch: 29 P Loss: 8.829150195959004\n",
      "Social Welfare of Episode  29 , (nu, random): 23.184251928232804\n",
      "Social Welfare of Episode  29 , (nu, pi): 24.476500404316997\n",
      "Social Welfare of Episode  29 , (nu, pi*(nu)): 34.70559168524195\n",
      "Social Welfare of Episode  29 , (nu*(nu), pi*(nu*(nu))): 34.705603518461565\n",
      "Epoch: 30 P Loss: 8.829150195959004\n",
      "Social Welfare of Episode  30 , (nu, random): 23.21024476444898\n",
      "Social Welfare of Episode  30 , (nu, pi): 24.51904463250327\n",
      "Social Welfare of Episode  30 , (nu, pi*(nu)): 34.7588452749217\n",
      "Social Welfare of Episode  30 , (nu*(nu), pi*(nu*(nu))): 34.758859618668026\n",
      "Epoch: 31 P Loss: 8.858945988054993\n",
      "Social Welfare of Episode  31 , (nu, random): 23.20549645097239\n",
      "Social Welfare of Episode  31 , (nu, pi): 24.537022365138867\n",
      "Social Welfare of Episode  31 , (nu, pi*(nu)): 34.751087796032415\n",
      "Social Welfare of Episode  31 , (nu*(nu), pi*(nu*(nu))): 34.82007709388991\n",
      "Epoch: 32 P Loss: 8.838982305082197\n",
      "Social Welfare of Episode  32 , (nu, random): 23.20244576944026\n",
      "Social Welfare of Episode  32 , (nu, pi): 24.570062397861385\n",
      "Social Welfare of Episode  32 , (nu, pi*(nu)): 34.83060879735524\n",
      "Social Welfare of Episode  32 , (nu*(nu), pi*(nu*(nu))): 34.83068169348677\n",
      "Epoch: 33 P Loss: 8.728149040693381\n",
      "Social Welfare of Episode  33 , (nu, random): 23.150824746028164\n",
      "Social Welfare of Episode  33 , (nu, pi): 24.528103010546886\n",
      "Social Welfare of Episode  33 , (nu, pi*(nu)): 34.72387780433848\n",
      "Social Welfare of Episode  33 , (nu*(nu), pi*(nu*(nu))): 34.72390177438511\n",
      "Epoch: 34 P Loss: 8.792016960997724\n",
      "Social Welfare of Episode  34 , (nu, random): 23.21055569599331\n",
      "Social Welfare of Episode  34 , (nu, pi): 24.611927038212254\n",
      "Social Welfare of Episode  34 , (nu, pi*(nu)): 34.74302166444259\n",
      "Social Welfare of Episode  34 , (nu*(nu), pi*(nu*(nu))): 34.96844396440768\n",
      "Epoch: 35 P Loss: 8.749057676184771\n",
      "Social Welfare of Episode  35 , (nu, random): 23.185021687702985\n",
      "Social Welfare of Episode  35 , (nu, pi): 24.603758162735335\n",
      "Social Welfare of Episode  35 , (nu, pi*(nu)): 34.747466343176406\n",
      "Social Welfare of Episode  35 , (nu*(nu), pi*(nu*(nu))): 34.74750884928761\n",
      "Epoch: 36 P Loss: 8.749057676184771\n",
      "Social Welfare of Episode  36 , (nu, random): 23.168006648614885\n",
      "Social Welfare of Episode  36 , (nu, pi): 24.602093051639578\n",
      "Social Welfare of Episode  36 , (nu, pi*(nu)): 34.68564545631545\n",
      "Social Welfare of Episode  36 , (nu*(nu), pi*(nu*(nu))): 34.76586980906416\n",
      "Epoch: 37 P Loss: 8.568214131815141\n",
      "Social Welfare of Episode  37 , (nu, random): 23.221394856682437\n",
      "Social Welfare of Episode  37 , (nu, pi): 24.681841041764088\n",
      "Social Welfare of Episode  37 , (nu, pi*(nu)): 34.82019453380431\n",
      "Social Welfare of Episode  37 , (nu*(nu), pi*(nu*(nu))): 34.849199607403634\n",
      "Epoch: 38 P Loss: 8.563471086805796\n",
      "Social Welfare of Episode  38 , (nu, random): 23.193583212369408\n",
      "Social Welfare of Episode  38 , (nu, pi): 24.66705139413926\n",
      "Social Welfare of Episode  38 , (nu, pi*(nu)): 34.77140715615161\n",
      "Social Welfare of Episode  38 , (nu*(nu), pi*(nu*(nu))): 34.77147286575809\n",
      "Epoch: 39 P Loss: 8.563471086805796\n",
      "Social Welfare of Episode  39 , (nu, random): 23.186658550330325\n",
      "Social Welfare of Episode  39 , (nu, pi): 24.671546226159943\n",
      "Social Welfare of Episode  39 , (nu, pi*(nu)): 34.73819020385082\n",
      "Social Welfare of Episode  39 , (nu*(nu), pi*(nu*(nu))): 34.73823247465354\n",
      "Epoch: 40 P Loss: 8.540414231741499\n",
      "Social Welfare of Episode  40 , (nu, random): 23.209933918364158\n",
      "Social Welfare of Episode  40 , (nu, pi): 24.732957571709683\n",
      "Social Welfare of Episode  40 , (nu, pi*(nu)): 34.80234035292341\n",
      "Social Welfare of Episode  40 , (nu*(nu), pi*(nu*(nu))): 34.80250800562713\n",
      "Epoch: 41 P Loss: 8.426126479154323\n",
      "Social Welfare of Episode  41 , (nu, random): 23.208699955595083\n",
      "Social Welfare of Episode  41 , (nu, pi): 24.736927501430976\n",
      "Social Welfare of Episode  41 , (nu, pi*(nu)): 34.782172714320716\n",
      "Social Welfare of Episode  41 , (nu*(nu), pi*(nu*(nu))): 34.78217588129756\n",
      "Epoch: 42 P Loss: 8.37624127663224\n",
      "Social Welfare of Episode  42 , (nu, random): 23.20207270567071\n",
      "Social Welfare of Episode  42 , (nu, pi): 24.7645193042683\n",
      "Social Welfare of Episode  42 , (nu, pi*(nu)): 34.77459040144617\n",
      "Social Welfare of Episode  42 , (nu*(nu), pi*(nu*(nu))): 34.7746461710411\n",
      "Epoch: 43 P Loss: 8.398863712156606\n",
      "Social Welfare of Episode  43 , (nu, random): 23.201085509014536\n",
      "Social Welfare of Episode  43 , (nu, pi): 24.786903894282872\n",
      "Social Welfare of Episode  43 , (nu, pi*(nu)): 34.77673937755408\n",
      "Social Welfare of Episode  43 , (nu*(nu), pi*(nu*(nu))): 35.16971558420142\n",
      "Epoch: 44 P Loss: 8.398863712156606\n",
      "Social Welfare of Episode  44 , (nu, random): 23.20002545034987\n",
      "Social Welfare of Episode  44 , (nu, pi): 24.82054272907254\n",
      "Social Welfare of Episode  44 , (nu, pi*(nu)): 34.75504861143483\n",
      "Social Welfare of Episode  44 , (nu*(nu), pi*(nu*(nu))): 34.75505338554348\n",
      "Epoch: 45 P Loss: 8.420538128352577\n",
      "Social Welfare of Episode  45 , (nu, random): 23.18842322811086\n",
      "Social Welfare of Episode  45 , (nu, pi): 24.819834395785033\n",
      "Social Welfare of Episode  45 , (nu, pi*(nu)): 34.75230300798142\n",
      "Social Welfare of Episode  45 , (nu*(nu), pi*(nu*(nu))): 34.75230781248964\n",
      "Epoch: 46 P Loss: 8.396937641920976\n",
      "Social Welfare of Episode  46 , (nu, random): 23.198105363651695\n",
      "Social Welfare of Episode  46 , (nu, pi): 24.87840602614955\n",
      "Social Welfare of Episode  46 , (nu, pi*(nu)): 34.75119414134339\n",
      "Social Welfare of Episode  46 , (nu*(nu), pi*(nu*(nu))): 34.898984723428384\n",
      "Epoch: 47 P Loss: 8.376315221275592\n",
      "Social Welfare of Episode  47 , (nu, random): 23.206335407724225\n",
      "Social Welfare of Episode  47 , (nu, pi): 24.915633261468873\n",
      "Social Welfare of Episode  47 , (nu, pi*(nu)): 34.774375573261906\n",
      "Social Welfare of Episode  47 , (nu*(nu), pi*(nu*(nu))): 34.77438977103617\n",
      "Epoch: 48 P Loss: 8.400170205031644\n",
      "Social Welfare of Episode  48 , (nu, random): 23.204497415805246\n",
      "Social Welfare of Episode  48 , (nu, pi): 24.949145407491585\n",
      "Social Welfare of Episode  48 , (nu, pi*(nu)): 34.75405997268847\n",
      "Social Welfare of Episode  48 , (nu*(nu), pi*(nu*(nu))): 34.754062119563784\n",
      "Epoch: 49 P Loss: 8.412162644681652\n",
      "Social Welfare of Episode  49 , (nu, random): 23.230151619079436\n",
      "Social Welfare of Episode  49 , (nu, pi): 25.015227760262142\n",
      "Social Welfare of Episode  49 , (nu, pi*(nu)): 34.80811177291532\n",
      "Social Welfare of Episode  49 , (nu*(nu), pi*(nu*(nu))): 34.86831728263775\n",
      "Epoch: 50 P Loss: 8.44414511319097\n",
      "Social Welfare of Episode  50 , (nu, random): 23.197264346020482\n",
      "Social Welfare of Episode  50 , (nu, pi): 25.017248016215497\n",
      "Social Welfare of Episode  50 , (nu, pi*(nu)): 34.712655692740945\n",
      "Social Welfare of Episode  50 , (nu*(nu), pi*(nu*(nu))): 34.78492444132697\n",
      "Epoch: 51 P Loss: 8.357263154995833\n",
      "Social Welfare of Episode  51 , (nu, random): 23.216628178816624\n",
      "Social Welfare of Episode  51 , (nu, pi): 25.066544931732867\n",
      "Social Welfare of Episode  51 , (nu, pi*(nu)): 34.796119989452365\n",
      "Social Welfare of Episode  51 , (nu*(nu), pi*(nu*(nu))): 34.7961756712463\n",
      "Epoch: 52 P Loss: 8.34994570769328\n",
      "Social Welfare of Episode  52 , (nu, random): 23.21856550680016\n",
      "Social Welfare of Episode  52 , (nu, pi): 25.094225116260358\n",
      "Social Welfare of Episode  52 , (nu, pi*(nu)): 34.77748294528025\n",
      "Social Welfare of Episode  52 , (nu*(nu), pi*(nu*(nu))): 34.77748294528025\n",
      "Epoch: 53 P Loss: 8.345012330459625\n",
      "Social Welfare of Episode  53 , (nu, random): 23.209397979319526\n",
      "Social Welfare of Episode  53 , (nu, pi): 25.102698139143868\n",
      "Social Welfare of Episode  53 , (nu, pi*(nu)): 34.805470048575124\n",
      "Social Welfare of Episode  53 , (nu*(nu), pi*(nu*(nu))): 34.89746712443585\n",
      "Epoch: 54 P Loss: 8.23778406398821\n",
      "Social Welfare of Episode  54 , (nu, random): 23.210949315659462\n",
      "Social Welfare of Episode  54 , (nu, pi): 25.125164436823837\n",
      "Social Welfare of Episode  54 , (nu, pi*(nu)): 34.77362827803748\n",
      "Social Welfare of Episode  54 , (nu*(nu), pi*(nu*(nu))): 34.77468221483723\n",
      "Epoch: 55 P Loss: 8.189773803249333\n",
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      "Epoch: 56 P Loss: 8.189773803249333\n",
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      "Epoch: 57 P Loss: 8.188914928980733\n",
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      "Epoch: 58 P Loss: 8.176070573393122\n",
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      "Epoch: 59 P Loss: 8.167440353577053\n",
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      "Epoch: 60 P Loss: 8.132587257667415\n",
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      "Epoch: 61 P Loss: 8.137397200642114\n",
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      "Epoch: 62 P Loss: 8.12857239329943\n",
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      "Epoch: 63 P Loss: 8.050069024090682\n",
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      "Epoch: 64 P Loss: 8.050069024090682\n",
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      "Epoch: 65 P Loss: 8.063568352560212\n",
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      "Epoch: 69 P Loss: 8.071198419382252\n",
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      "Epoch: 70 P Loss: 8.071502753103822\n",
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      "Epoch: 71 P Loss: 8.0372180415142\n",
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      "Epoch: 72 P Loss: 8.03548276038582\n",
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      "Epoch: 73 P Loss: 8.032148316998857\n",
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      "Epoch: 75 P Loss: 7.946186002851739\n",
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      "Epoch: 76 P Loss: 7.945117298662604\n",
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      "Epoch: 77 P Loss: 7.9457552606541055\n",
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      "Epoch: 78 P Loss: 7.98320733251204\n",
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      "Epoch: 79 P Loss: 7.985340397846835\n",
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      "Epoch: 80 P Loss: 7.985014064301642\n",
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      "Epoch: 81 P Loss: 7.987800073868199\n",
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      "Epoch: 82 P Loss: 7.98486691872944\n",
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      "Epoch: 83 P Loss: 7.9216779417150605\n",
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      "Epoch: 84 P Loss: 7.87937942052246\n",
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      "Epoch: 85 P Loss: 7.878211187309745\n",
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      "Epoch: 86 P Loss: 7.885503176001459\n",
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      "Epoch: 87 P Loss: 7.862921669926355\n",
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      "Epoch: 95 P Loss: 7.771397427025855\n",
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      "Epoch: 113 P Loss: 7.417828800431322\n",
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      "Epoch: 133 P Loss: 7.347119858649888\n",
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      "Epoch: 136 P Loss: 7.292411916785149\n",
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      "Epoch: 137 P Loss: 7.284776259503403\n",
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      "Epoch: 138 P Loss: 7.270433952316613\n",
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      "Epoch: 139 P Loss: 7.26140208062384\n",
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      "Epoch: 141 P Loss: 7.250972939890193\n",
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      "Epoch: 142 P Loss: 7.251121577498625\n",
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      "Epoch: 143 P Loss: 7.250401433970356\n",
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      "Epoch: 144 P Loss: 7.250401433970356\n",
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      "Epoch: 146 P Loss: 7.2374434080517425\n",
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      "Epoch: 147 P Loss: 7.235135742168988\n",
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      "Epoch: 148 P Loss: 7.240826167268987\n",
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      "Epoch: 149 P Loss: 7.2334591234523336\n",
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      "Epoch: 150 P Loss: 7.244191949042136\n",
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      "Epoch: 151 P Loss: 7.244128148424299\n",
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      "Epoch: 152 P Loss: 7.196294461457263\n",
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      "Epoch: 153 P Loss: 7.2003018607070235\n",
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      "Social Welfare of Episode  170 , (nu*(nu), pi*(nu*(nu))): 34.751010119002956\n",
      "Epoch: 171 P Loss: 7.12863812100991\n",
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      "Social Welfare of Episode  171 , (nu*(nu), pi*(nu*(nu))): 34.82804002330107\n",
      "Epoch: 172 P Loss: 7.1129621979249835\n",
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      "Social Welfare of Episode  172 , (nu*(nu), pi*(nu*(nu))): 34.81492498138072\n",
      "Epoch: 173 P Loss: 7.114023036569078\n",
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      "Social Welfare of Episode  173 , (nu*(nu), pi*(nu*(nu))): 34.758220875402756\n",
      "Epoch: 174 P Loss: 7.111025868150523\n",
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      "Social Welfare of Episode  174 , (nu*(nu), pi*(nu*(nu))): 34.750131100936116\n",
      "Epoch: 175 P Loss: 7.102057282878381\n",
      "Social Welfare of Episode  175 , (nu, random): 23.199485273963443\n",
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      "Social Welfare of Episode  175 , (nu*(nu), pi*(nu*(nu))): 34.76819485071362\n",
      "Epoch: 176 P Loss: 7.100870075039611\n",
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      "Social Welfare of Episode  176 , (nu*(nu), pi*(nu*(nu))): 34.71364089204627\n",
      "Epoch: 177 P Loss: 7.108267100235021\n",
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      "Social Welfare of Episode  177 , (nu*(nu), pi*(nu*(nu))): 34.80823346910834\n",
      "Epoch: 178 P Loss: 7.108864076605929\n",
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      "Social Welfare of Episode  178 , (nu*(nu), pi*(nu*(nu))): 34.75715165770433\n",
      "Epoch: 179 P Loss: 7.108829919932398\n",
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      "Social Welfare of Episode  179 , (nu*(nu), pi*(nu*(nu))): 34.764868767262634\n",
      "Epoch: 180 P Loss: 7.107080804463819\n",
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      "Epoch: 181 P Loss: 7.107192035847084\n",
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      "Epoch: 182 P Loss: 7.107160789236831\n",
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      "Epoch: 183 P Loss: 7.105514603533671\n",
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      "Epoch: 184 P Loss: 7.106019650333591\n",
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      "Epoch: 185 P Loss: 7.104656756207651\n",
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      "Epoch: 186 P Loss: 7.103969784633859\n",
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      "Epoch: 187 P Loss: 7.103208850866482\n",
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      "Epoch: 188 P Loss: 7.109702415130362\n",
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      "Epoch: 189 P Loss: 7.090254395281218\n",
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      "Social Welfare of Episode  189 , (nu*(nu), pi*(nu*(nu))): 34.8161462684571\n",
      "Epoch: 190 P Loss: 7.090254395281218\n",
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      "Social Welfare of Episode  190 , (nu*(nu), pi*(nu*(nu))): 34.871444158783476\n",
      "Epoch: 191 P Loss: 7.0996281196447\n",
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      "Social Welfare of Episode  191 , (nu*(nu), pi*(nu*(nu))): 35.093878233763704\n",
      "Epoch: 192 P Loss: 7.09531530507921\n",
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      "Social Welfare of Episode  192 , (nu*(nu), pi*(nu*(nu))): 34.78441259129316\n",
      "Epoch: 193 P Loss: 7.091335975258903\n",
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      "Social Welfare of Episode  193 , (nu*(nu), pi*(nu*(nu))): 34.81227276852116\n",
      "Epoch: 194 P Loss: 7.091456491619693\n",
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      "Epoch: 195 P Loss: 7.081100766595731\n",
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      "Social Welfare of Episode  195 , (nu*(nu), pi*(nu*(nu))): 34.890296049850264\n",
      "Epoch: 196 P Loss: 7.073597270893365\n",
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      "Social Welfare of Episode  196 , (nu*(nu), pi*(nu*(nu))): 34.73078401002313\n",
      "Epoch: 197 P Loss: 7.071037099711457\n",
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      "Social Welfare of Episode  197 , (nu*(nu), pi*(nu*(nu))): 34.79808511198279\n",
      "Epoch: 198 P Loss: 7.070722291303755\n",
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      "Social Welfare of Episode  198 , (nu*(nu), pi*(nu*(nu))): 34.83922996803262\n",
      "Epoch: 199 P Loss: 7.070502921559954\n",
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      "Social Welfare of Episode  199 , (nu*(nu), pi*(nu*(nu))): 34.90834094602556\n",
      "======Train id: 1 ======\n",
      "Epoch: 0 P Loss: 10.213775097538715\n",
      "Social Welfare of Episode  0 , (nu, random): 23.206989768377948\n",
      "Social Welfare of Episode  0 , (nu, pi): 23.206989768377948\n",
      "Social Welfare of Episode  0 , (nu, pi*(nu)): 34.759216862493055\n",
      "Social Welfare of Episode  0 , (nu*(nu), pi*(nu*(nu))): 34.759537336803646\n",
      "Epoch: 1 P Loss: 10.230992705835396\n",
      "Social Welfare of Episode  1 , (nu, random): 23.229904597770258\n",
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      "Social Welfare of Episode  1 , (nu*(nu), pi*(nu*(nu))): 34.823390383595964\n",
      "Epoch: 2 P Loss: 10.16266060479679\n",
      "Social Welfare of Episode  2 , (nu, random): 23.22403597450895\n",
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      "Social Welfare of Episode  2 , (nu*(nu), pi*(nu*(nu))): 34.800694460913334\n",
      "Epoch: 3 P Loss: 10.123664780014096\n",
      "Social Welfare of Episode  3 , (nu, random): 23.19611221525205\n",
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      "Social Welfare of Episode  3 , (nu*(nu), pi*(nu*(nu))): 34.81024424990723\n",
      "Epoch: 4 P Loss: 10.025376934706944\n",
      "Social Welfare of Episode  4 , (nu, random): 23.204567255635247\n",
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      "Social Welfare of Episode  4 , (nu*(nu), pi*(nu*(nu))): 34.833513459470794\n",
      "Epoch: 5 P Loss: 9.964294621219404\n",
      "Social Welfare of Episode  5 , (nu, random): 23.206527764095224\n",
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      "Social Welfare of Episode  5 , (nu*(nu), pi*(nu*(nu))): 34.80945214720722\n",
      "Epoch: 6 P Loss: 9.890195745797447\n",
      "Social Welfare of Episode  6 , (nu, random): 23.175919707456206\n",
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      "Social Welfare of Episode  6 , (nu*(nu), pi*(nu*(nu))): 34.85831242301401\n",
      "Epoch: 7 P Loss: 9.85506772320419\n",
      "Social Welfare of Episode  7 , (nu, random): 23.167242562148296\n",
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      "Social Welfare of Episode  7 , (nu*(nu), pi*(nu*(nu))): 34.75504580991406\n",
      "Epoch: 8 P Loss: 9.857584215217434\n",
      "Social Welfare of Episode  8 , (nu, random): 23.2272176179914\n",
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      "Social Welfare of Episode  8 , (nu*(nu), pi*(nu*(nu))): 34.81896074381544\n",
      "Epoch: 9 P Loss: 9.743817992842327\n",
      "Social Welfare of Episode  9 , (nu, random): 23.223775868695707\n",
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      "Social Welfare of Episode  9 , (nu*(nu), pi*(nu*(nu))): 34.83485108397745\n",
      "Epoch: 10 P Loss: 9.753091124890316\n",
      "Social Welfare of Episode  10 , (nu, random): 23.19582713385294\n",
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      "Social Welfare of Episode  10 , (nu*(nu), pi*(nu*(nu))): 34.76038877530663\n",
      "Epoch: 11 P Loss: 9.656012335049212\n",
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      "Epoch: 12 P Loss: 9.703920861696297\n",
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      "Epoch: 13 P Loss: 9.677766488245549\n",
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      "Epoch: 14 P Loss: 9.71794944722796\n",
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      "Epoch: 15 P Loss: 9.598652155455222\n",
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      "Epoch: 16 P Loss: 9.495474599829462\n",
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      "Epoch: 17 P Loss: 9.455916745794676\n",
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      "Social Welfare of Episode  17 , (nu*(nu), pi*(nu*(nu))): 34.78713093141273\n",
      "Epoch: 18 P Loss: 9.428528722467043\n",
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      "Epoch: 19 P Loss: 9.37727452650877\n",
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      "Epoch: 20 P Loss: 9.368341299543198\n",
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      "Epoch: 21 P Loss: 9.341909684598926\n",
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      "Epoch: 22 P Loss: 9.259557581438449\n",
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      "Epoch: 23 P Loss: 9.190322741090101\n",
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      "Epoch: 24 P Loss: 9.134327796193851\n",
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      "Epoch: 25 P Loss: 9.081138837032302\n",
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      "Epoch: 26 P Loss: 9.01841462718268\n",
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      "Epoch: 27 P Loss: 8.973254047556198\n",
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      "Epoch: 28 P Loss: 8.958382667433458\n",
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      "Epoch: 29 P Loss: 8.95325888727818\n",
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      "Epoch: 30 P Loss: 8.891140042478012\n",
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      "Epoch: 31 P Loss: 8.902065641821949\n",
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      "Epoch: 32 P Loss: 8.909302491333722\n",
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      "Epoch: 33 P Loss: 8.80829789163244\n",
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      "Epoch: 35 P Loss: 8.764521074166684\n",
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      "Epoch: 36 P Loss: 8.792714761892865\n",
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      "Epoch: 37 P Loss: 8.784201094819066\n",
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      "Epoch: 38 P Loss: 8.723324247863136\n",
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      "Epoch: 39 P Loss: 8.780040144441358\n",
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      "Epoch: 40 P Loss: 8.773037067558654\n",
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      "Epoch: 41 P Loss: 8.71720658482399\n",
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      "Epoch: 43 P Loss: 8.723261023237214\n",
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      "Epoch: 44 P Loss: 8.72460501970156\n",
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      "Epoch: 46 P Loss: 8.69975343246378\n",
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      "Epoch: 48 P Loss: 8.693498996578485\n",
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      "Epoch: 49 P Loss: 8.640374179603091\n",
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      "Epoch: 50 P Loss: 8.637612838232961\n",
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      "Epoch: 51 P Loss: 8.636138741611564\n",
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      "Epoch: 52 P Loss: 8.567197309087309\n",
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      "Epoch: 53 P Loss: 8.507322485727064\n",
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      "Epoch: 54 P Loss: 8.50724969196168\n",
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      "Epoch: 55 P Loss: 8.495141363088496\n",
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      "Epoch: 56 P Loss: 8.383494197252354\n",
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      "Epoch: 57 P Loss: 8.37751084182707\n",
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      "Epoch: 58 P Loss: 8.37751084182707\n",
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      "Epoch: 59 P Loss: 8.3763941300321\n",
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      "Epoch: 60 P Loss: 8.376419575629518\n",
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      "Epoch: 61 P Loss: 8.376513938906353\n",
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      "Epoch: 62 P Loss: 8.377947723164977\n",
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      "Epoch: 63 P Loss: 8.374542003362231\n",
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      "Epoch: 64 P Loss: 8.32756924247658\n",
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      "Epoch: 65 P Loss: 8.266504020387844\n",
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      "Epoch: 66 P Loss: 8.236182204130484\n",
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      "Epoch: 68 P Loss: 8.232763705458341\n",
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      "Epoch: 69 P Loss: 8.232937555454937\n",
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      "Epoch: 70 P Loss: 8.218817153945512\n",
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      "Epoch: 71 P Loss: 8.167326505139131\n",
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      "Epoch: 75 P Loss: 8.12731039608717\n",
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      "Epoch: 78 P Loss: 8.025682737383915\n",
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      "Epoch: 87 P Loss: 7.939264332549796\n",
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      "Epoch: 89 P Loss: 7.916821540145513\n",
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      "Epoch: 90 P Loss: 7.896972426973853\n",
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      "Epoch: 91 P Loss: 7.891489036597961\n",
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      "Epoch: 93 P Loss: 7.893696675084963\n",
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      "Epoch: 94 P Loss: 7.832449839916496\n",
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      "Epoch: 95 P Loss: 7.770128719128261\n",
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      "Epoch: 96 P Loss: 7.769117670634139\n",
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      "Epoch: 97 P Loss: 7.642211746808607\n",
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      "Epoch: 98 P Loss: 7.647431668648686\n",
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      "Epoch: 100 P Loss: 7.683316213657282\n",
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      "Epoch: 101 P Loss: 7.683316213657282\n",
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      "Epoch: 102 P Loss: 7.625231163775553\n",
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      "Epoch: 104 P Loss: 7.6243109052981115\n",
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      "Epoch: 105 P Loss: 7.620108254109668\n",
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      "Epoch: 106 P Loss: 7.6190107410611\n",
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      "Epoch: 107 P Loss: 7.573623368770094\n",
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      "Epoch: 108 P Loss: 7.573623368770094\n",
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      "Epoch: 109 P Loss: 7.557680480951454\n",
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      "Epoch: 110 P Loss: 7.566405219486175\n",
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      "Epoch: 111 P Loss: 7.558007150733069\n",
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      "Epoch: 112 P Loss: 7.558231242775486\n",
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      "Epoch: 113 P Loss: 7.55828542905972\n",
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      "Epoch: 114 P Loss: 7.5525800700974175\n",
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      "Epoch: 115 P Loss: 7.553661232559519\n",
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      "Epoch: 116 P Loss: 7.554573531375816\n",
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      "Epoch: 117 P Loss: 7.556194245885359\n",
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      "Epoch: 145 P Loss: 7.441463092805316\n",
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      "Epoch: 146 P Loss: 7.438121907180177\n",
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      "Epoch: 147 P Loss: 7.438118506839742\n",
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      "Epoch: 148 P Loss: 7.437978262104738\n",
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      "Epoch: 149 P Loss: 7.437952989586057\n",
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      "Epoch: 150 P Loss: 7.438150950617296\n",
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      "Epoch: 151 P Loss: 7.438150950617296\n",
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      "Epoch: 152 P Loss: 7.438150950617296\n",
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      "Epoch: 153 P Loss: 7.412172206274598\n",
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      "Epoch: 154 P Loss: 7.412086101638324\n",
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      "Epoch: 155 P Loss: 7.411736287837411\n",
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      "Epoch: 156 P Loss: 7.411374729055395\n",
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      "Epoch: 157 P Loss: 7.411374729055395\n",
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      "Epoch: 158 P Loss: 7.411374729055395\n",
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      "Epoch: 159 P Loss: 7.41498918475779\n",
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      "Epoch: 160 P Loss: 7.4105342591472185\n",
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      "Epoch: 161 P Loss: 7.404366283422734\n",
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      "Epoch: 162 P Loss: 7.404454658637785\n",
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      "Epoch: 163 P Loss: 7.407094857775292\n",
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      "Epoch: 164 P Loss: 7.407068261176806\n",
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      "Epoch: 165 P Loss: 7.405956277077453\n",
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      "Epoch: 166 P Loss: 7.402456151650034\n",
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      "Epoch: 167 P Loss: 7.403273478328538\n",
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      "Epoch: 168 P Loss: 7.399040279850459\n",
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      "Epoch: 169 P Loss: 7.399188219931449\n",
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      "Epoch: 170 P Loss: 7.399985635483183\n",
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      "Epoch: 171 P Loss: 7.400129459711116\n",
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      "Epoch: 172 P Loss: 7.399097239275171\n",
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      "Epoch: 173 P Loss: 7.399590601701398\n",
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      "Epoch: 174 P Loss: 7.4015568294677765\n",
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      "Epoch: 175 P Loss: 7.4014383635585\n",
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      "Epoch: 176 P Loss: 7.401454087967684\n",
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      "Epoch: 177 P Loss: 7.401563079172661\n",
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      "Epoch: 178 P Loss: 7.385366168126893\n",
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      "Epoch: 179 P Loss: 7.3914491042021\n",
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      "Epoch: 180 P Loss: 7.390812602760417\n",
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      "Epoch: 181 P Loss: 7.385053616008059\n",
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      "Epoch: 182 P Loss: 7.387801309965969\n",
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      "Epoch: 183 P Loss: 7.391831478554889\n",
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      "Epoch: 184 P Loss: 7.391351894393144\n",
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      "Epoch: 185 P Loss: 7.391466444728805\n",
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      "Epoch: 186 P Loss: 7.391937651791593\n",
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      "Epoch: 187 P Loss: 7.391277606741591\n",
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      "Epoch: 190 P Loss: 7.392968180851607\n",
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      "======Train id: 2 ======\n",
      "Epoch: 0 P Loss: 10.295552906747377\n",
      "Social Welfare of Episode  0 , (nu, random): 23.198206081652877\n",
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      "Epoch: 1 P Loss: 10.189942458124813\n",
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      "Epoch: 2 P Loss: 10.116741488404806\n",
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      "Social Welfare of Episode  2 , (nu, pi*(nu)): 34.79980495440629\n",
      "Social Welfare of Episode  2 , (nu*(nu), pi*(nu*(nu))): 34.79983061378603\n",
      "Epoch: 3 P Loss: 10.155579885456278\n",
      "Social Welfare of Episode  3 , (nu, random): 23.20154586127913\n",
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      "Social Welfare of Episode  3 , (nu*(nu), pi*(nu*(nu))): 34.8033123927537\n",
      "Epoch: 4 P Loss: 10.16698349632703\n",
      "Social Welfare of Episode  4 , (nu, random): 23.228024479221105\n",
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      "Social Welfare of Episode  4 , (nu*(nu), pi*(nu*(nu))): 34.8196439732269\n",
      "Epoch: 5 P Loss: 10.140337548203775\n",
      "Social Welfare of Episode  5 , (nu, random): 23.213223411848688\n",
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      "Social Welfare of Episode  5 , (nu*(nu), pi*(nu*(nu))): 34.80253359515275\n",
      "Epoch: 6 P Loss: 10.126266278282976\n",
      "Social Welfare of Episode  6 , (nu, random): 23.204162732680828\n",
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      "Social Welfare of Episode  6 , (nu*(nu), pi*(nu*(nu))): 34.79512856570908\n",
      "Epoch: 7 P Loss: 10.040163628768168\n",
      "Social Welfare of Episode  7 , (nu, random): 23.24186142488666\n",
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      "Social Welfare of Episode  7 , (nu*(nu), pi*(nu*(nu))): 35.12456702563741\n",
      "Epoch: 8 P Loss: 10.011152560802454\n",
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      "Social Welfare of Episode  8 , (nu*(nu), pi*(nu*(nu))): 34.84195721680826\n",
      "Epoch: 9 P Loss: 9.915801014281719\n",
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      "Social Welfare of Episode  9 , (nu*(nu), pi*(nu*(nu))): 34.829038967505355\n",
      "Epoch: 10 P Loss: 9.885666921505228\n",
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      "Social Welfare of Episode  10 , (nu*(nu), pi*(nu*(nu))): 34.95613721166557\n",
      "Epoch: 11 P Loss: 9.757963987466741\n",
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      "Social Welfare of Episode  11 , (nu*(nu), pi*(nu*(nu))): 34.770479247155876\n",
      "Epoch: 12 P Loss: 9.66896411804097\n",
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      "Social Welfare of Episode  12 , (nu*(nu), pi*(nu*(nu))): 34.95589982307144\n",
      "Epoch: 13 P Loss: 9.58421234423268\n",
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      "Epoch: 14 P Loss: 9.565385879212545\n",
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      "Epoch: 15 P Loss: 9.642964947977624\n",
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      "Epoch: 16 P Loss: 9.580170224016742\n",
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      "Social Welfare of Episode  16 , (nu*(nu), pi*(nu*(nu))): 34.85083969570799\n",
      "Epoch: 17 P Loss: 9.503111662599519\n",
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      "Epoch: 18 P Loss: 9.489111615311897\n",
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      "Epoch: 19 P Loss: 9.46518189248294\n",
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      "Epoch: 20 P Loss: 9.326437565410545\n",
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      "Epoch: 21 P Loss: 9.245273884716674\n",
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      "Epoch: 22 P Loss: 9.083121772126317\n",
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      "Epoch: 23 P Loss: 8.96386470427836\n",
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      "Epoch: 24 P Loss: 8.920581730394158\n",
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      "Epoch: 25 P Loss: 8.769775016867287\n",
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      "Epoch: 26 P Loss: 8.669683930750386\n",
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      "Epoch: 27 P Loss: 8.637585276519468\n",
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      "Epoch: 28 P Loss: 8.621262243032884\n",
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      "Epoch: 29 P Loss: 8.609297623790825\n",
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      "Epoch: 30 P Loss: 8.597369043098173\n",
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      "Epoch: 31 P Loss: 8.499769468559233\n",
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      "Epoch: 32 P Loss: 8.563664692499954\n",
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      "Epoch: 33 P Loss: 8.519371640879713\n",
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      "Epoch: 34 P Loss: 8.519371640879713\n",
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      "Epoch: 35 P Loss: 8.523931295542939\n",
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      "Epoch: 36 P Loss: 8.573444053785757\n",
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      "Epoch: 37 P Loss: 8.561293160005057\n",
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      "Epoch: 38 P Loss: 8.490788551970073\n",
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      "Epoch: 39 P Loss: 8.493282558380342\n",
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      "Epoch: 40 P Loss: 8.481303993301733\n",
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      "Epoch: 41 P Loss: 8.437294719350898\n",
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      "Epoch: 42 P Loss: 8.35635021026239\n",
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      "Epoch: 43 P Loss: 8.362163127573593\n",
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      "Epoch: 44 P Loss: 8.362163127573593\n",
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      "Epoch: 45 P Loss: 8.346644732371775\n",
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      "Epoch: 46 P Loss: 8.344562544183134\n",
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      "Epoch: 47 P Loss: 8.295215683690254\n",
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      "Epoch: 48 P Loss: 8.190199703786025\n",
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      "Epoch: 49 P Loss: 8.18301805109514\n",
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      "Epoch: 50 P Loss: 8.166571317965746\n",
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      "Epoch: 51 P Loss: 8.154008612709003\n",
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      "Epoch: 52 P Loss: 8.139645340136836\n",
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      "Epoch: 53 P Loss: 8.137264767754884\n",
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      "Epoch: 54 P Loss: 8.104940333624086\n",
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      "Epoch: 55 P Loss: 8.103682166113868\n",
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      "Epoch: 56 P Loss: 8.010500724158224\n",
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      "Epoch: 57 P Loss: 8.009325517912854\n",
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      "Epoch: 58 P Loss: 8.003204042217147\n",
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      "Epoch: 60 P Loss: 7.942837664908524\n",
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      "Epoch: 61 P Loss: 7.879727291823232\n",
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      "Epoch: 62 P Loss: 7.811083814306024\n",
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      "Epoch: 63 P Loss: 7.816552116028609\n",
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      "Epoch: 64 P Loss: 7.812651796500609\n",
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      "Epoch: 65 P Loss: 7.812420756321877\n",
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      "Epoch: 66 P Loss: 7.817708955075391\n",
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      "Epoch: 67 P Loss: 7.764375286366266\n",
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      "Epoch: 68 P Loss: 7.757002485392453\n",
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      "Epoch: 69 P Loss: 7.693576046876885\n",
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      "Epoch: 70 P Loss: 7.669233851338247\n",
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      "Epoch: 71 P Loss: 7.671577036924988\n",
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      "Epoch: 72 P Loss: 7.684707706599196\n",
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      "Epoch: 73 P Loss: 7.682977102054854\n",
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      "Epoch: 74 P Loss: 7.67871285865669\n",
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      "Epoch: 75 P Loss: 7.681832830117968\n",
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      "Epoch: 76 P Loss: 7.672590267198897\n",
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      "Epoch: 77 P Loss: 7.613499212327359\n",
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      "Epoch: 78 P Loss: 7.614202209447553\n",
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      "Epoch: 79 P Loss: 7.61211804533981\n",
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      "Epoch: 80 P Loss: 7.671222838068383\n",
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      "Epoch: 81 P Loss: 7.606809642450901\n",
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      "Epoch: 82 P Loss: 7.60197904794355\n",
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      "Epoch: 83 P Loss: 7.612981770172732\n",
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      "Epoch: 84 P Loss: 7.619605057387227\n",
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      "Epoch: 85 P Loss: 7.617159853012113\n",
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      "Epoch: 86 P Loss: 7.615099201973657\n",
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      "Epoch: 87 P Loss: 7.614639869677957\n",
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      "Epoch: 88 P Loss: 7.613816236690444\n",
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      "Epoch: 89 P Loss: 7.565533793307588\n",
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      "Epoch: 90 P Loss: 7.563427086401678\n",
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      "Epoch: 91 P Loss: 7.565508508670496\n",
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      "Epoch: 93 P Loss: 7.499509608680114\n",
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      "Epoch: 94 P Loss: 7.502043877926324\n",
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      "Epoch: 95 P Loss: 7.567195554184718\n",
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      "Epoch: 96 P Loss: 7.567651260380859\n",
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      "Epoch: 97 P Loss: 7.561649739557399\n",
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      "Epoch: 98 P Loss: 7.496949002564283\n",
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      "Epoch: 105 P Loss: 7.421684268791326\n",
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      "Epoch: 106 P Loss: 7.415205057149133\n",
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      "Epoch: 107 P Loss: 7.407585644145974\n",
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      "Epoch: 126 P Loss: 7.45857968394645\n",
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      "Epoch: 127 P Loss: 7.459940640829459\n",
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      "Epoch: 133 P Loss: 7.454489490745059\n",
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      "Epoch: 135 P Loss: 7.429498656542689\n",
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      "Epoch: 136 P Loss: 7.429022166631109\n",
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      "Epoch: 137 P Loss: 7.430111249556635\n",
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      "Epoch: 138 P Loss: 7.428538963465872\n",
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      "Epoch: 139 P Loss: 7.430689347371062\n",
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      "Epoch: 141 P Loss: 7.437650950408209\n",
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      "Epoch: 142 P Loss: 7.438986348689551\n",
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      "Epoch: 143 P Loss: 7.392629070294192\n",
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      "Epoch: 144 P Loss: 7.293302103888606\n",
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      "Epoch: 146 P Loss: 7.283884220751803\n",
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      "Epoch: 147 P Loss: 7.282671271549328\n",
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      "Epoch: 148 P Loss: 7.282425216168821\n",
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      "Epoch: 150 P Loss: 7.183160198706191\n",
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      "Epoch: 151 P Loss: 7.182851529338851\n",
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      "Epoch: 152 P Loss: 7.184544171529535\n",
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      "Epoch: 153 P Loss: 7.187850701834804\n",
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      "Epoch: 176 P Loss: 6.941035558828674\n",
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      "Social Welfare of Episode  176 , (nu*(nu), pi*(nu*(nu))): 34.829973862910244\n",
      "Epoch: 177 P Loss: 6.941254219136279\n",
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      "Social Welfare of Episode  177 , (nu*(nu), pi*(nu*(nu))): 34.76843302659694\n",
      "Epoch: 178 P Loss: 6.941051766793636\n",
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      "Social Welfare of Episode  178 , (nu*(nu), pi*(nu*(nu))): 34.75209477640588\n",
      "Epoch: 179 P Loss: 6.940893589476455\n",
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      "Social Welfare of Episode  179 , (nu*(nu), pi*(nu*(nu))): 35.160134844325974\n",
      "Epoch: 180 P Loss: 6.939907532894158\n",
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      "Social Welfare of Episode  180 , (nu*(nu), pi*(nu*(nu))): 35.04242578045317\n",
      "Epoch: 181 P Loss: 6.9462442292203415\n",
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      "Social Welfare of Episode  181 , (nu*(nu), pi*(nu*(nu))): 34.764896936204714\n",
      "Epoch: 182 P Loss: 6.944211160875746\n",
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      "Social Welfare of Episode  182 , (nu*(nu), pi*(nu*(nu))): 34.81903492737661\n",
      "Epoch: 183 P Loss: 6.944348434364588\n",
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      "Social Welfare of Episode  183 , (nu*(nu), pi*(nu*(nu))): 34.87152752937048\n",
      "Epoch: 184 P Loss: 6.939866195866338\n",
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      "Social Welfare of Episode  184 , (nu*(nu), pi*(nu*(nu))): 34.77553189718421\n",
      "Epoch: 185 P Loss: 6.938862769987725\n",
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      "Epoch: 186 P Loss: 6.936298493017678\n",
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      "Epoch: 187 P Loss: 6.93500359179399\n",
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      "Epoch: 188 P Loss: 6.934723276838073\n",
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      "Epoch: 189 P Loss: 6.935142326135149\n",
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      "Epoch: 190 P Loss: 6.860422826254669\n",
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      "Social Welfare of Episode  190 , (nu*(nu), pi*(nu*(nu))): 35.00849553466088\n",
      "Epoch: 191 P Loss: 6.859704651400288\n",
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      "Social Welfare of Episode  191 , (nu*(nu), pi*(nu*(nu))): 34.92093556116116\n",
      "Epoch: 192 P Loss: 6.860250878664776\n",
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      "Social Welfare of Episode  192 , (nu*(nu), pi*(nu*(nu))): 34.75345614425467\n",
      "Epoch: 193 P Loss: 6.859433685696295\n",
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      "Social Welfare of Episode  193 , (nu*(nu), pi*(nu*(nu))): 34.88179510519282\n",
      "Epoch: 194 P Loss: 6.8590476246195555\n",
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      "Epoch: 195 P Loss: 6.859150504706937\n",
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      "Social Welfare of Episode  195 , (nu*(nu), pi*(nu*(nu))): 34.78701592071701\n",
      "Epoch: 196 P Loss: 6.860281167991407\n",
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      "Social Welfare of Episode  196 , (nu*(nu), pi*(nu*(nu))): 34.907955098061976\n",
      "Epoch: 197 P Loss: 6.860251224868066\n",
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      "Social Welfare of Episode  197 , (nu*(nu), pi*(nu*(nu))): 34.84727358204201\n",
      "Epoch: 198 P Loss: 6.860515668692434\n",
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      "Social Welfare of Episode  198 , (nu*(nu), pi*(nu*(nu))): 34.856090390612756\n",
      "Epoch: 199 P Loss: 6.860691711733436\n",
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      "Social Welfare of Episode  199 , (nu*(nu), pi*(nu*(nu))): 34.733116333089086\n",
      "======Train id: 3 ======\n",
      "Epoch: 0 P Loss: 10.219568366228206\n",
      "Social Welfare of Episode  0 , (nu, random): 23.235140665191857\n",
      "Social Welfare of Episode  0 , (nu, pi): 23.235140665191857\n",
      "Social Welfare of Episode  0 , (nu, pi*(nu)): 34.82032586663157\n",
      "Social Welfare of Episode  0 , (nu*(nu), pi*(nu*(nu))): 34.832197800636216\n",
      "Epoch: 1 P Loss: 10.142323526759494\n",
      "Social Welfare of Episode  1 , (nu, random): 23.23755366099146\n",
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      "Social Welfare of Episode  1 , (nu*(nu), pi*(nu*(nu))): 35.04968074302331\n",
      "Epoch: 2 P Loss: 10.158329608857095\n",
      "Social Welfare of Episode  2 , (nu, random): 23.205009778226554\n",
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      "Social Welfare of Episode  2 , (nu*(nu), pi*(nu*(nu))): 35.02870897213562\n",
      "Epoch: 3 P Loss: 10.04088648427949\n",
      "Social Welfare of Episode  3 , (nu, random): 23.20600459661311\n",
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      "Social Welfare of Episode  3 , (nu*(nu), pi*(nu*(nu))): 34.84804243458272\n",
      "Epoch: 4 P Loss: 9.955687120477181\n",
      "Social Welfare of Episode  4 , (nu, random): 23.2099535949722\n",
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      "Social Welfare of Episode  4 , (nu*(nu), pi*(nu*(nu))): 34.79429302351196\n",
      "Epoch: 5 P Loss: 9.944254044108828\n",
      "Social Welfare of Episode  5 , (nu, random): 23.194854724273604\n",
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      "Social Welfare of Episode  5 , (nu*(nu), pi*(nu*(nu))): 34.768501561997994\n",
      "Epoch: 6 P Loss: 9.858695991969205\n",
      "Social Welfare of Episode  6 , (nu, random): 23.187732358593625\n",
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      "Social Welfare of Episode  6 , (nu*(nu), pi*(nu*(nu))): 34.76379662609901\n",
      "Epoch: 7 P Loss: 9.801656097885688\n",
      "Social Welfare of Episode  7 , (nu, random): 23.222635576973204\n",
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      "Social Welfare of Episode  7 , (nu*(nu), pi*(nu*(nu))): 34.88318244160442\n",
      "Epoch: 8 P Loss: 9.784304365661075\n",
      "Social Welfare of Episode  8 , (nu, random): 23.21995589944572\n",
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      "Social Welfare of Episode  8 , (nu*(nu), pi*(nu*(nu))): 34.84763099029096\n",
      "Epoch: 9 P Loss: 9.70818553776641\n",
      "Social Welfare of Episode  9 , (nu, random): 23.21729420956848\n",
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      "Social Welfare of Episode  9 , (nu*(nu), pi*(nu*(nu))): 34.812493069529474\n",
      "Epoch: 10 P Loss: 9.632828380483637\n",
      "Social Welfare of Episode  10 , (nu, random): 23.230005440780502\n",
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      "Social Welfare of Episode  10 , (nu*(nu), pi*(nu*(nu))): 34.80519148026869\n",
      "Epoch: 11 P Loss: 9.622174730216148\n",
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      "Social Welfare of Episode  11 , (nu*(nu), pi*(nu*(nu))): 34.809485042650174\n",
      "Epoch: 12 P Loss: 9.530893468942724\n",
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      "Social Welfare of Episode  12 , (nu*(nu), pi*(nu*(nu))): 34.75406555529251\n",
      "Epoch: 13 P Loss: 9.473454565264301\n",
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      "Social Welfare of Episode  13 , (nu*(nu), pi*(nu*(nu))): 34.859977388227044\n",
      "Epoch: 14 P Loss: 9.402753698425325\n",
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      "Social Welfare of Episode  14 , (nu*(nu), pi*(nu*(nu))): 34.81057566676515\n",
      "Epoch: 15 P Loss: 9.32900110659488\n",
      "Social Welfare of Episode  15 , (nu, random): 23.22315395267738\n",
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      "Social Welfare of Episode  15 , (nu*(nu), pi*(nu*(nu))): 35.10434082217055\n",
      "Epoch: 16 P Loss: 9.286395459189263\n",
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      "Social Welfare of Episode  16 , (nu*(nu), pi*(nu*(nu))): 34.880452767516964\n",
      "Epoch: 17 P Loss: 9.16967328876801\n",
      "Social Welfare of Episode  17 , (nu, random): 23.20629771444267\n",
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      "Social Welfare of Episode  17 , (nu*(nu), pi*(nu*(nu))): 34.791517585935104\n",
      "Epoch: 18 P Loss: 9.139260816692817\n",
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      "Social Welfare of Episode  18 , (nu*(nu), pi*(nu*(nu))): 35.12614858793208\n",
      "Epoch: 19 P Loss: 9.157288147428783\n",
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      "Social Welfare of Episode  19 , (nu*(nu), pi*(nu*(nu))): 34.81735707489192\n",
      "Epoch: 20 P Loss: 9.122465040837326\n",
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      "Social Welfare of Episode  20 , (nu*(nu), pi*(nu*(nu))): 34.944696616940554\n",
      "Epoch: 21 P Loss: 9.147432637304044\n",
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      "Social Welfare of Episode  21 , (nu*(nu), pi*(nu*(nu))): 34.96906179114376\n",
      "Epoch: 22 P Loss: 9.0891123450938\n",
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      "Social Welfare of Episode  22 , (nu*(nu), pi*(nu*(nu))): 34.9853209058691\n",
      "Epoch: 23 P Loss: 9.041353384056597\n",
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      "Social Welfare of Episode  23 , (nu*(nu), pi*(nu*(nu))): 34.92203897755883\n",
      "Epoch: 24 P Loss: 9.037187368146219\n",
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      "Social Welfare of Episode  24 , (nu*(nu), pi*(nu*(nu))): 34.77521414607219\n",
      "Epoch: 25 P Loss: 8.968452963400994\n",
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      "Epoch: 26 P Loss: 9.012275569353205\n",
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      "Social Welfare of Episode  26 , (nu*(nu), pi*(nu*(nu))): 34.97026997807836\n",
      "Epoch: 27 P Loss: 8.958640221210187\n",
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      "Social Welfare of Episode  27 , (nu*(nu), pi*(nu*(nu))): 34.787199199461426\n",
      "Epoch: 28 P Loss: 8.921316734534907\n",
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      "Epoch: 29 P Loss: 8.878128402575735\n",
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      "Epoch: 30 P Loss: 8.779328739187214\n",
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      "Epoch: 31 P Loss: 8.779328739187214\n",
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      "Epoch: 32 P Loss: 8.755667426021635\n",
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      "Epoch: 33 P Loss: 8.711197092915238\n",
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      "Epoch: 34 P Loss: 8.664628426608148\n",
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      "Epoch: 35 P Loss: 8.576773149399246\n",
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      "Epoch: 36 P Loss: 8.56873017525639\n",
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      "Epoch: 37 P Loss: 8.587860015230468\n",
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      "Epoch: 38 P Loss: 8.582904265071686\n",
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      "Epoch: 39 P Loss: 8.55052642251324\n",
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      "Epoch: 40 P Loss: 8.602634371120935\n",
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      "Epoch: 41 P Loss: 8.604180074031033\n",
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      "Epoch: 42 P Loss: 8.631606484348366\n",
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      "Epoch: 43 P Loss: 8.614660525732203\n",
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      "Epoch: 44 P Loss: 8.569567797361275\n",
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      "Epoch: 45 P Loss: 8.6269119950822\n",
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      "Epoch: 46 P Loss: 8.567449946084817\n",
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      "Epoch: 47 P Loss: 8.572211078042738\n",
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      "Epoch: 48 P Loss: 8.559339385361033\n",
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      "Epoch: 49 P Loss: 8.555331750089895\n",
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      "Epoch: 50 P Loss: 8.530288997732791\n",
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      "Epoch: 51 P Loss: 8.528658165406597\n",
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      "Epoch: 52 P Loss: 8.528658165406597\n",
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      "Epoch: 53 P Loss: 8.494349272701424\n",
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      "Epoch: 54 P Loss: 8.400684956039914\n",
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      "Epoch: 55 P Loss: 8.400684956039914\n",
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      "Epoch: 56 P Loss: 8.35860864943463\n",
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      "Epoch: 57 P Loss: 8.357260205780925\n",
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      "Epoch: 58 P Loss: 8.302796919081024\n",
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      "Epoch: 59 P Loss: 8.283065850949134\n",
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      "Epoch: 60 P Loss: 8.284461570005297\n",
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      "Epoch: 61 P Loss: 8.274157247568008\n",
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      "Epoch: 62 P Loss: 8.280712606140677\n",
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      "Epoch: 63 P Loss: 8.314683042805008\n",
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      "Epoch: 64 P Loss: 8.310464029577949\n",
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      "Epoch: 65 P Loss: 8.250184535652227\n",
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      "Epoch: 66 P Loss: 8.106901807238014\n",
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      "Epoch: 68 P Loss: 8.10836299109657\n",
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      "Epoch: 69 P Loss: 8.090126052030127\n",
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      "Epoch: 70 P Loss: 8.041703840102727\n",
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      "Epoch: 71 P Loss: 8.02584781432511\n",
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      "Epoch: 92 P Loss: 7.895340032977389\n",
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      "Epoch: 93 P Loss: 7.894936238326313\n",
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      "Epoch: 95 P Loss: 7.769898578785702\n",
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      "Epoch: 96 P Loss: 7.769898578785702\n",
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      "Epoch: 98 P Loss: 7.768976449405629\n",
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      "Epoch: 100 P Loss: 7.7679076182093665\n",
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      "Epoch: 102 P Loss: 7.662206860444664\n",
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      "Epoch: 105 P Loss: 7.642136496854513\n",
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      "Epoch: 106 P Loss: 7.513031056078554\n",
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      "Epoch: 107 P Loss: 7.510157504731372\n",
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      "Epoch: 109 P Loss: 7.54219667039287\n",
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      "Epoch: 113 P Loss: 7.5432303212151\n",
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      "Epoch: 114 P Loss: 7.54332511256309\n",
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      "Epoch: 116 P Loss: 7.543300949830835\n",
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      "Epoch: 117 P Loss: 7.5207978705275895\n",
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      "Epoch: 150 P Loss: 7.100653545682237\n",
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      "Epoch: 151 P Loss: 7.100385341887997\n",
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      "Epoch: 152 P Loss: 7.100092391740518\n",
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      "Epoch: 153 P Loss: 7.100092391740518\n",
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      "Epoch: 154 P Loss: 7.100068841810229\n",
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      "Epoch: 155 P Loss: 7.100068770772723\n",
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      "Epoch: 156 P Loss: 7.099278402126464\n",
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      "Epoch: 157 P Loss: 7.094365806995925\n",
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      "Epoch: 158 P Loss: 7.09473159514347\n",
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      "Epoch: 159 P Loss: 7.072035677533741\n",
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      "Epoch: 160 P Loss: 7.072062854564869\n",
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      "Epoch: 161 P Loss: 7.063594889449304\n",
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      "Epoch: 162 P Loss: 7.026828795987131\n",
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      "Epoch: 163 P Loss: 7.024261816718547\n",
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      "Epoch: 164 P Loss: 7.024261816718547\n",
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      "Epoch: 165 P Loss: 7.024654153826681\n",
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      "Epoch: 166 P Loss: 7.024706332473864\n",
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      "Epoch: 167 P Loss: 7.024081734175319\n",
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      "Epoch: 168 P Loss: 7.024588459177374\n",
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      "Epoch: 169 P Loss: 7.025375458775661\n",
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      "Epoch: 170 P Loss: 7.021613158031086\n",
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      "Epoch: 171 P Loss: 7.0215944300289\n",
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      "Epoch: 172 P Loss: 7.020867910222859\n",
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      "Epoch: 173 P Loss: 7.019937041171707\n",
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      "Epoch: 174 P Loss: 7.019149152215983\n",
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      "Epoch: 175 P Loss: 7.017686820537358\n",
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      "Epoch: 176 P Loss: 7.017021129050712\n",
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      "Epoch: 177 P Loss: 7.017934625943704\n",
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      "Epoch: 178 P Loss: 7.016318455688692\n",
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      "Epoch: 179 P Loss: 7.015073536691632\n",
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      "Epoch: 180 P Loss: 7.001231360423611\n",
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      "Epoch: 181 P Loss: 7.002372245803906\n",
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      "Epoch: 182 P Loss: 7.002262924604752\n",
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      "Epoch: 183 P Loss: 7.0022590582237765\n",
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      "Epoch: 184 P Loss: 7.001978658522931\n",
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      "Epoch: 185 P Loss: 7.000948193152817\n",
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      "Epoch: 186 P Loss: 7.000559843842536\n",
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      "Epoch: 187 P Loss: 6.950398991094919\n",
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      "Epoch: 188 P Loss: 6.942485753008004\n",
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      "Epoch: 189 P Loss: 6.944010452706319\n",
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      "Epoch: 190 P Loss: 6.944010452706319\n",
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      "Epoch: 191 P Loss: 6.875225681429693\n",
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      "Epoch: 192 P Loss: 6.868542662591943\n",
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      "Epoch: 193 P Loss: 6.8628772203776585\n",
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      "Epoch: 195 P Loss: 6.846908275385545\n",
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      "Epoch: 196 P Loss: 6.848077315720092\n",
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      "Epoch: 198 P Loss: 6.850870300855702\n",
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      "Epoch: 199 P Loss: 6.850324359411196\n",
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      "======Train id: 4 ======\n",
      "Epoch: 0 P Loss: 10.285128380328741\n",
      "Social Welfare of Episode  0 , (nu, random): 23.239749823698364\n",
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      "Epoch: 1 P Loss: 10.342552257401312\n",
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      "Epoch: 2 P Loss: 10.344633083169233\n",
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      "Epoch: 3 P Loss: 10.233479523538104\n",
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      "Epoch: 4 P Loss: 10.1466164998556\n",
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      "Epoch: 5 P Loss: 10.021579334074579\n",
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      "Epoch: 6 P Loss: 9.947497434530433\n",
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      "Epoch: 7 P Loss: 9.880702694774339\n",
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      "Epoch: 8 P Loss: 9.848105521081393\n",
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      "Social Welfare of Episode  8 , (nu*(nu), pi*(nu*(nu))): 34.79838796674183\n",
      "Epoch: 9 P Loss: 9.776093232820228\n",
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      "Social Welfare of Episode  9 , (nu*(nu), pi*(nu*(nu))): 34.764088138439085\n",
      "Epoch: 10 P Loss: 9.67681067170363\n",
      "Social Welfare of Episode  10 , (nu, random): 23.222618791546008\n",
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      "Social Welfare of Episode  10 , (nu*(nu), pi*(nu*(nu))): 34.796800782522794\n",
      "Epoch: 11 P Loss: 9.722678333385549\n",
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      "Social Welfare of Episode  11 , (nu*(nu), pi*(nu*(nu))): 34.709890516919785\n",
      "Epoch: 12 P Loss: 9.689089955260279\n",
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      "Social Welfare of Episode  12 , (nu*(nu), pi*(nu*(nu))): 34.82616528965782\n",
      "Epoch: 13 P Loss: 9.694003128993524\n",
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      "Social Welfare of Episode  13 , (nu*(nu), pi*(nu*(nu))): 35.04790416068463\n",
      "Epoch: 14 P Loss: 9.6263465930976\n",
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      "Social Welfare of Episode  14 , (nu*(nu), pi*(nu*(nu))): 35.05965407981596\n",
      "Epoch: 15 P Loss: 9.473445030406554\n",
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      "Social Welfare of Episode  15 , (nu*(nu), pi*(nu*(nu))): 34.92215245560911\n",
      "Epoch: 16 P Loss: 9.394068765110209\n",
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      "Social Welfare of Episode  16 , (nu*(nu), pi*(nu*(nu))): 34.93795605541617\n",
      "Epoch: 17 P Loss: 9.252783399490873\n",
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      "Epoch: 18 P Loss: 9.187155612989699\n",
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      "Epoch: 19 P Loss: 9.133313904884526\n",
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      "Epoch: 20 P Loss: 9.034887636005816\n",
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      "Epoch: 21 P Loss: 9.034887636005816\n",
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      "Epoch: 22 P Loss: 9.008547341885695\n",
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      "Epoch: 23 P Loss: 8.993059677825991\n",
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      "Epoch: 24 P Loss: 8.993059677825991\n",
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      "Epoch: 25 P Loss: 8.993059677825991\n",
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      "Epoch: 26 P Loss: 8.968582109588674\n",
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      "Epoch: 27 P Loss: 8.931934010397951\n",
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      "Epoch: 28 P Loss: 8.892784415984364\n",
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      "Epoch: 29 P Loss: 8.800560994195353\n",
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      "Epoch: 30 P Loss: 8.752523544394595\n",
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      "Epoch: 31 P Loss: 8.693236234041265\n",
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      "Epoch: 32 P Loss: 8.692227524570582\n",
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      "Epoch: 33 P Loss: 8.65969244073265\n",
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      "Epoch: 34 P Loss: 8.65969244073265\n",
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      "Epoch: 35 P Loss: 8.715290468175542\n",
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      "Epoch: 36 P Loss: 8.60936410087854\n",
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      "Epoch: 37 P Loss: 8.519773246364233\n",
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      "Epoch: 38 P Loss: 8.530275854121557\n",
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      "Epoch: 39 P Loss: 8.476371902482471\n",
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      "Epoch: 40 P Loss: 8.412046580979753\n",
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      "Epoch: 41 P Loss: 8.362036908407404\n",
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      "Epoch: 42 P Loss: 8.35674781702989\n",
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      "Epoch: 43 P Loss: 8.35674781702989\n",
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      "Epoch: 44 P Loss: 8.355308275170442\n",
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      "Epoch: 45 P Loss: 8.388165072620453\n",
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      "Epoch: 46 P Loss: 8.341870744501868\n",
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      "Epoch: 47 P Loss: 8.238593748129016\n",
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      "Epoch: 48 P Loss: 8.199517713547646\n",
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      "Epoch: 49 P Loss: 8.199517713547646\n",
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      "Epoch: 50 P Loss: 8.241956081331763\n",
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      "Epoch: 51 P Loss: 8.240913380889817\n",
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      "Epoch: 52 P Loss: 8.173419040328348\n",
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      "Epoch: 53 P Loss: 8.163253615487644\n",
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      "Epoch: 54 P Loss: 8.141153817133166\n",
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      "Epoch: 55 P Loss: 8.07851076555247\n",
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      "Epoch: 56 P Loss: 8.07402254512998\n",
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      "Epoch: 57 P Loss: 8.082986278281657\n",
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      "Epoch: 58 P Loss: 8.082986278281657\n",
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      "Epoch: 60 P Loss: 8.081771194736438\n",
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      "Epoch: 63 P Loss: 8.000732827673243\n",
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      "Epoch: 66 P Loss: 7.942565044469031\n",
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      "Epoch: 67 P Loss: 7.905837587149686\n",
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      "Epoch: 68 P Loss: 7.905837587149686\n",
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      "Epoch: 69 P Loss: 7.905939921905411\n",
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      "Epoch: 70 P Loss: 7.864505396863136\n",
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      "Epoch: 71 P Loss: 7.801573043469963\n",
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      "Epoch: 72 P Loss: 7.8011020540844696\n",
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      "Epoch: 73 P Loss: 7.798854412295038\n",
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      "Epoch: 74 P Loss: 7.700838493800699\n",
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      "Epoch: 75 P Loss: 7.601942385429295\n",
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      "Epoch: 76 P Loss: 7.600248502257809\n",
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      "Epoch: 77 P Loss: 7.608409620534011\n",
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      "Epoch: 78 P Loss: 7.588521095757354\n",
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      "Epoch: 79 P Loss: 7.522878898196331\n",
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      "Epoch: 80 P Loss: 7.537322482202413\n",
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      "Epoch: 81 P Loss: 7.533858937678923\n",
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      "Epoch: 82 P Loss: 7.508863175850673\n",
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      "Epoch: 83 P Loss: 7.501587267618408\n",
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      "Epoch: 84 P Loss: 7.479143928210103\n",
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      "Epoch: 85 P Loss: 7.480067015120186\n",
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      "Epoch: 86 P Loss: 7.479974945172483\n",
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      "Epoch: 87 P Loss: 7.477674382070819\n",
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      "Epoch: 88 P Loss: 7.415839071127435\n",
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      "Epoch: 89 P Loss: 7.352239035229137\n",
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      "Epoch: 90 P Loss: 7.385867741771318\n",
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      "Epoch: 91 P Loss: 7.385647132573574\n",
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      "Epoch: 92 P Loss: 7.346873871485384\n",
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      "Epoch: 93 P Loss: 7.348837270810112\n",
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      "Epoch: 94 P Loss: 7.348304351191325\n",
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      "Epoch: 95 P Loss: 7.331764230639853\n",
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      "Epoch: 96 P Loss: 7.321481120801708\n",
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      "Epoch: 97 P Loss: 7.319747002981872\n",
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      "Epoch: 98 P Loss: 7.319473397887555\n",
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      "Epoch: 99 P Loss: 7.319473397887555\n",
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      "Epoch: 101 P Loss: 7.319301428624224\n",
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      "Epoch: 102 P Loss: 7.329061165335374\n",
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      "Epoch: 103 P Loss: 7.33024097353719\n",
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      "Epoch: 104 P Loss: 7.33024097353719\n",
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      "Epoch: 105 P Loss: 7.286120931437126\n",
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      "Epoch: 106 P Loss: 7.227716624365233\n",
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      "Epoch: 107 P Loss: 7.214319610033285\n",
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      "Epoch: 109 P Loss: 7.209334523002821\n",
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      "Epoch: 124 P Loss: 7.091129942729985\n",
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      "Epoch: 133 P Loss: 7.00829129558535\n",
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      "Epoch: 136 P Loss: 6.998370372517891\n",
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      "Epoch: 138 P Loss: 7.0003280817884095\n",
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      "Epoch: 139 P Loss: 7.009320404849349\n",
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      "Epoch: 142 P Loss: 6.896709134151975\n",
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      "Epoch: 143 P Loss: 6.895264730523506\n",
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      "Epoch: 144 P Loss: 6.894132518927277\n",
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      "Epoch: 146 P Loss: 6.894292003947149\n",
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      "Epoch: 147 P Loss: 6.897100489978352\n",
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      "Epoch: 148 P Loss: 6.894631943139352\n",
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      "Epoch: 150 P Loss: 6.89324820907992\n",
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      "Epoch: 151 P Loss: 6.893549289937291\n",
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      "Epoch: 152 P Loss: 6.901016295569787\n",
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      "Epoch: 153 P Loss: 6.899755582589388\n",
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      "Epoch: 155 P Loss: 6.878107967734656\n",
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      "Epoch: 156 P Loss: 6.8396929631606325\n",
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      "Epoch: 160 P Loss: 6.826293401112545\n",
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      "Social Welfare of Episode  181 , (nu, pi): 28.991898214796727\n",
      "Social Welfare of Episode  181 , (nu, pi*(nu)): 34.75816816851196\n",
      "Social Welfare of Episode  181 , (nu*(nu), pi*(nu*(nu))): 34.931616789530466\n",
      "Epoch: 182 P Loss: 6.605391641517825\n",
      "Social Welfare of Episode  182 , (nu, random): 23.206523918446727\n",
      "Social Welfare of Episode  182 , (nu, pi): 29.061794136211315\n",
      "Social Welfare of Episode  182 , (nu, pi*(nu)): 34.83807270335178\n",
      "Social Welfare of Episode  182 , (nu*(nu), pi*(nu*(nu))): 34.83811738905375\n",
      "Epoch: 183 P Loss: 6.605424011374422\n",
      "Social Welfare of Episode  183 , (nu, random): 23.236641733062115\n",
      "Social Welfare of Episode  183 , (nu, pi): 29.05447854966575\n",
      "Social Welfare of Episode  183 , (nu, pi*(nu)): 34.78054811214588\n",
      "Social Welfare of Episode  183 , (nu*(nu), pi*(nu*(nu))): 34.78059590301593\n",
      "Epoch: 184 P Loss: 6.605425744416711\n",
      "Social Welfare of Episode  184 , (nu, random): 23.21295711474099\n",
      "Social Welfare of Episode  184 , (nu, pi): 29.071078055136567\n",
      "Social Welfare of Episode  184 , (nu, pi*(nu)): 34.79421233443834\n",
      "Social Welfare of Episode  184 , (nu*(nu), pi*(nu*(nu))): 34.79422168457502\n",
      "Epoch: 185 P Loss: 6.4440171775799024\n",
      "Social Welfare of Episode  185 , (nu, random): 23.216293168794607\n",
      "Social Welfare of Episode  185 , (nu, pi): 29.10314221016703\n",
      "Social Welfare of Episode  185 , (nu, pi*(nu)): 34.81674875365297\n",
      "Social Welfare of Episode  185 , (nu*(nu), pi*(nu*(nu))): 34.8168410020265\n",
      "Epoch: 186 P Loss: 6.444919641398318\n",
      "Social Welfare of Episode  186 , (nu, random): 23.185703312490073\n",
      "Social Welfare of Episode  186 , (nu, pi): 29.070318860186447\n",
      "Social Welfare of Episode  186 , (nu, pi*(nu)): 34.75153575400216\n",
      "Social Welfare of Episode  186 , (nu*(nu), pi*(nu*(nu))): 34.751668058671996\n",
      "Epoch: 187 P Loss: 6.443253713035102\n",
      "Social Welfare of Episode  187 , (nu, random): 23.17428831937409\n",
      "Social Welfare of Episode  187 , (nu, pi): 29.069812500464018\n",
      "Social Welfare of Episode  187 , (nu, pi*(nu)): 34.74342069461841\n",
      "Social Welfare of Episode  187 , (nu*(nu), pi*(nu*(nu))): 34.8678658175756\n",
      "Epoch: 188 P Loss: 6.443116417927628\n",
      "Social Welfare of Episode  188 , (nu, random): 23.191605808807626\n",
      "Social Welfare of Episode  188 , (nu, pi): 29.116633264238434\n",
      "Social Welfare of Episode  188 , (nu, pi*(nu)): 34.766815941537274\n",
      "Social Welfare of Episode  188 , (nu*(nu), pi*(nu*(nu))): 34.766825968610654\n",
      "Epoch: 189 P Loss: 6.440017304954117\n",
      "Social Welfare of Episode  189 , (nu, random): 23.200413226921043\n",
      "Social Welfare of Episode  189 , (nu, pi): 29.121729841468955\n",
      "Social Welfare of Episode  189 , (nu, pi*(nu)): 34.74456886010125\n",
      "Social Welfare of Episode  189 , (nu*(nu), pi*(nu*(nu))): 34.74471472761914\n",
      "Epoch: 190 P Loss: 6.439847808515285\n",
      "Social Welfare of Episode  190 , (nu, random): 23.206692232986867\n",
      "Social Welfare of Episode  190 , (nu, pi): 29.171274869799312\n",
      "Social Welfare of Episode  190 , (nu, pi*(nu)): 34.796300302969\n",
      "Social Welfare of Episode  190 , (nu*(nu), pi*(nu*(nu))): 34.796302669878216\n",
      "Epoch: 191 P Loss: 6.4429896122796215\n",
      "Social Welfare of Episode  191 , (nu, random): 23.195021043445003\n",
      "Social Welfare of Episode  191 , (nu, pi): 29.14502282058339\n",
      "Social Welfare of Episode  191 , (nu, pi*(nu)): 34.73920882185617\n",
      "Social Welfare of Episode  191 , (nu*(nu), pi*(nu*(nu))): 34.73976436215599\n",
      "Epoch: 192 P Loss: 6.442008201509464\n",
      "Social Welfare of Episode  192 , (nu, random): 23.19348996961227\n",
      "Social Welfare of Episode  192 , (nu, pi): 29.16409153069634\n",
      "Social Welfare of Episode  192 , (nu, pi*(nu)): 34.7485822475275\n",
      "Social Welfare of Episode  192 , (nu*(nu), pi*(nu*(nu))): 34.74860806922362\n",
      "Epoch: 193 P Loss: 6.4515439475177985\n",
      "Social Welfare of Episode  193 , (nu, random): 23.185148021918348\n",
      "Social Welfare of Episode  193 , (nu, pi): 29.1618672031086\n",
      "Social Welfare of Episode  193 , (nu, pi*(nu)): 34.73164824528633\n",
      "Social Welfare of Episode  193 , (nu*(nu), pi*(nu*(nu))): 34.746172784840326\n",
      "Epoch: 194 P Loss: 6.451508235451541\n",
      "Social Welfare of Episode  194 , (nu, random): 23.2144785075225\n",
      "Social Welfare of Episode  194 , (nu, pi): 29.26792427521121\n",
      "Social Welfare of Episode  194 , (nu, pi*(nu)): 34.845012234588665\n",
      "Social Welfare of Episode  194 , (nu*(nu), pi*(nu*(nu))): 34.8450906656185\n",
      "Epoch: 195 P Loss: 6.393927665556547\n",
      "Social Welfare of Episode  195 , (nu, random): 23.216833532064527\n",
      "Social Welfare of Episode  195 , (nu, pi): 29.278211525687784\n",
      "Social Welfare of Episode  195 , (nu, pi*(nu)): 34.84033034154705\n",
      "Social Welfare of Episode  195 , (nu*(nu), pi*(nu*(nu))): 34.840335583327445\n",
      "Epoch: 196 P Loss: 6.298246105656042\n",
      "Social Welfare of Episode  196 , (nu, random): 23.18189837938821\n",
      "Social Welfare of Episode  196 , (nu, pi): 29.19273641084637\n",
      "Social Welfare of Episode  196 , (nu, pi*(nu)): 34.72697121517908\n",
      "Social Welfare of Episode  196 , (nu*(nu), pi*(nu*(nu))): 34.72699413420102\n",
      "Epoch: 197 P Loss: 6.298405662676056\n",
      "Social Welfare of Episode  197 , (nu, random): 23.197420623395786\n",
      "Social Welfare of Episode  197 , (nu, pi): 29.255883799002287\n",
      "Social Welfare of Episode  197 , (nu, pi*(nu)): 34.78327506555959\n",
      "Social Welfare of Episode  197 , (nu*(nu), pi*(nu*(nu))): 34.79465546249651\n",
      "Epoch: 198 P Loss: 6.298491538533922\n",
      "Social Welfare of Episode  198 , (nu, random): 23.200181462277722\n",
      "Social Welfare of Episode  198 , (nu, pi): 29.259821093810906\n",
      "Social Welfare of Episode  198 , (nu, pi*(nu)): 34.76647586014653\n",
      "Social Welfare of Episode  198 , (nu*(nu), pi*(nu*(nu))): 34.88151131929318\n",
      "Epoch: 199 P Loss: 6.298404291294314\n",
      "Social Welfare of Episode  199 , (nu, random): 23.202188372885608\n",
      "Social Welfare of Episode  199 , (nu, pi): 29.26651989299903\n",
      "Social Welfare of Episode  199 , (nu, pi*(nu)): 34.76966144240306\n",
      "Social Welfare of Episode  199 , (nu*(nu), pi*(nu*(nu))): 34.80312668128802\n",
      "======End======\n"
     ]
    }
   ],
   "source": [
    "from main import train_loop\n",
    "num = 5\n",
    "full_result = train_loop(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n",
      "['nu_pi_perform', 'nu_random_perform', 'nu_piopt_perform', 'nuopt_pioptnuopt_perform', 'R_planner', 'R_random', 'R_total']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Regret')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4), dpi = 400)\n",
    "sns.set(style=\"darkgrid\")\n",
    "plt.gca().xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "plt.gca().yaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "\n",
    "print(len(full_result))\n",
    "\n",
    "keys = list(full_result.keys())\n",
    "print(keys)\n",
    "\n",
    "full_result['nu_random_perform'] = AVERAGE(full_result['nu_random_perform'])\n",
    "full_result['nu_pi_perform'] = AVERAGE(full_result['nu_pi_perform'])\n",
    "full_result['nuopt_pioptnuopt_perform'] = AVERAGE(full_result['nuopt_pioptnuopt_perform'])\n",
    "\n",
    "sd_format_plot(full_result['R_random'],'--')\n",
    "sd_format_plot(full_result['R_total'])\n",
    "\n",
    "plt.legend(['Uniformly Random Policy', 'MOLM'], ncol = len(keys), loc='upper left', bbox_to_anchor=(0.055, 1.15))\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Regret')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "8c7f13ab35733b16e3f284fb40dfd4323bf9128490545d13fc7dccc4ddefc647"
  },
  "kernelspec": {
   "display_name": "Python 3.6.13 ('layer_peeled')",
   "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": "3.6.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}