Q-learning-prices.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameters of the true (unknown) demand model\n",
    "true_slop = 30\n",
    "true_intercept = -5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "eta = 0.999\n",
    "num_episodes = 1000\n",
    "stock = 200\n",
    "prices = [1.99, 2.49, 2.99, 3.49, 3.99, 4.49]\n",
    "horizon_steps = 5\n",
    "initial_price = 5\n",
    "lambda2 = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def retrieve_linear_demand(a): \n",
    "    demand = true_slop + true_intercept * prices[a]\n",
    "    return np.random.poisson(demand, 1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q = np.zeros((stock+1,len(prices)),dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_visited = np.zeros((stock+1,len(prices)), dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x29f0f8ac6a0>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rList = []\n",
    "dList = []\n",
    "for k in range(num_episodes):\n",
    "    x = stock\n",
    "    a = initial_price\n",
    "    num_visited[x][a] = num_visited[x][a] + 1\n",
    "    r_acum = 0\n",
    "    d_acum = 0\n",
    "    for t in range(horizon_steps):\n",
    "        observed_demand = retrieve_linear_demand(a)\n",
    "        x_ = x - observed_demand\n",
    "        if (x_ < 0):\n",
    "            x_ = 0\n",
    "            observed_demand = x\n",
    "        r = observed_demand * a\n",
    "        r_acum = r_acum + r\n",
    "        d_acum = d_acum + observed_demand\n",
    "        if np.random.uniform(0, 1) <= (1/(k+1)):\n",
    "            a_ = np.random.choice(len(prices))\n",
    "            greedy = False\n",
    "        else:\n",
    "            a_ = np.argmax(Q, axis=1)[x_]\n",
    "            greedy = True\n",
    "        num_visited[x_][a_] = num_visited[x_][a_] + 1\n",
    "        delta = r + eta * Q[x_][a_] - Q[x][a]\n",
    "        for xx in range(stock):\n",
    "            for aa in range(len(prices)):\n",
    "                Q[xx][aa] = Q[xx][aa] + (1 / (num_visited[xx][aa] + 1)) * delta\n",
    "        x = x_\n",
    "        a = a_\n",
    "    rList.append(r_acum)\n",
    "    dList.append(d_acum)\n",
    "plt.plot(rList)\n",
    "plt.plot(dList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[134,\n",
       " 84,\n",
       " 30,\n",
       " 55,\n",
       " 20,\n",
       " 88,\n",
       " 25,\n",
       " 60,\n",
       " 103,\n",
       " 67,\n",
       " 112,\n",
       " 78,\n",
       " 70,\n",
       " 61,\n",
       " 89,\n",
       " 95,\n",
       " 133,\n",
       " 94,\n",
       " 139,\n",
       " 148,\n",
       " 136,\n",
       " 105,\n",
       " 147,\n",
       " 46,\n",
       " 145,\n",
       " 117,\n",
       " 109,\n",
       " 108,\n",
       " 102,\n",
       " 151,\n",
       " 126,\n",
       " 67,\n",
       " 201,\n",
       " 154,\n",
       " 152,\n",
       " 217,\n",
       " 132,\n",
       " 158,\n",
       " 176,\n",
       " 159,\n",
       " 151,\n",
       " 140,\n",
       " 128,\n",
       " 124,\n",
       " 141,\n",
       " 162,\n",
       " 103,\n",
       " 196,\n",
       " 187,\n",
       " 122,\n",
       " 151,\n",
       " 144,\n",
       " 157,\n",
       " 143,\n",
       " 125,\n",
       " 175,\n",
       " 182,\n",
       " 248,\n",
       " 161,\n",
       " 161,\n",
       " 212,\n",
       " 176,\n",
       " 162,\n",
       " 115,\n",
       " 114,\n",
       " 145,\n",
       " 185,\n",
       " 185,\n",
       " 185,\n",
       " 189,\n",
       " 135,\n",
       " 153,\n",
       " 217,\n",
       " 126,\n",
       " 180,\n",
       " 171,\n",
       " 116,\n",
       " 211,\n",
       " 139,\n",
       " 147,\n",
       " 154,\n",
       " 133,\n",
       " 146,\n",
       " 122,\n",
       " 129,\n",
       " 150,\n",
       " 182,\n",
       " 212,\n",
       " 151,\n",
       " 173,\n",
       " 216,\n",
       " 230,\n",
       " 118,\n",
       " 166,\n",
       " 212,\n",
       " 142,\n",
       " 183,\n",
       " 198,\n",
       " 125,\n",
       " 179,\n",
       " 136,\n",
       " 174,\n",
       " 132,\n",
       " 124,\n",
       " 160,\n",
       " 98,\n",
       " 189,\n",
       " 121,\n",
       " 90,\n",
       " 146,\n",
       " 157,\n",
       " 227,\n",
       " 134,\n",
       " 216,\n",
       " 154,\n",
       " 105,\n",
       " 143,\n",
       " 195,\n",
       " 124,\n",
       " 209,\n",
       " 184,\n",
       " 134,\n",
       " 106,\n",
       " 175,\n",
       " 176,\n",
       " 115,\n",
       " 209,\n",
       " 155,\n",
       " 206,\n",
       " 200,\n",
       " 189,\n",
       " 152,\n",
       " 83,\n",
       " 126,\n",
       " 159,\n",
       " 89,\n",
       " 121,\n",
       " 180,\n",
       " 115,\n",
       " 204,\n",
       " 92,\n",
       " 185,\n",
       " 146,\n",
       " 160,\n",
       " 145,\n",
       " 98,\n",
       " 182,\n",
       " 88,\n",
       " 169,\n",
       " 111,\n",
       " 141,\n",
       " 180,\n",
       " 163,\n",
       " 136,\n",
       " 132,\n",
       " 101,\n",
       " 133,\n",
       " 148,\n",
       " 85,\n",
       " 114,\n",
       " 150,\n",
       " 152,\n",
       " 106,\n",
       " 161,\n",
       " 84,\n",
       " 93,\n",
       " 79,\n",
       " 110,\n",
       " 183,\n",
       " 161,\n",
       " 97,\n",
       " 141,\n",
       " 111,\n",
       " 210,\n",
       " 168,\n",
       " 183,\n",
       " 123,\n",
       " 164,\n",
       " 82,\n",
       " 147,\n",
       " 173,\n",
       " 113,\n",
       " 189,\n",
       " 94,\n",
       " 94,\n",
       " 158,\n",
       " 170,\n",
       " 83,\n",
       " 169,\n",
       " 161,\n",
       " 132,\n",
       " 174,\n",
       " 173,\n",
       " 136,\n",
       " 124,\n",
       " 173,\n",
       " 156,\n",
       " 95,\n",
       " 184,\n",
       " 171,\n",
       " 155,\n",
       " 227,\n",
       " 80,\n",
       " 140,\n",
       " 153,\n",
       " 67,\n",
       " 180,\n",
       " 105,\n",
       " 130,\n",
       " 116,\n",
       " 79,\n",
       " 125,\n",
       " 191,\n",
       " 125,\n",
       " 125,\n",
       " 131,\n",
       " 113,\n",
       " 165,\n",
       " 136,\n",
       " 122,\n",
       " 160,\n",
       " 213,\n",
       " 185,\n",
       " 222,\n",
       " 162,\n",
       " 137,\n",
       " 98,\n",
       " 90,\n",
       " 157,\n",
       " 154,\n",
       " 144,\n",
       " 90,\n",
       " 194,\n",
       " 52,\n",
       " 173,\n",
       " 117,\n",
       " 179,\n",
       " 134,\n",
       " 143,\n",
       " 184,\n",
       " 176,\n",
       " 155,\n",
       " 145,\n",
       " 146,\n",
       " 146,\n",
       " 148,\n",
       " 154,\n",
       " 82,\n",
       " 155,\n",
       " 204,\n",
       " 141,\n",
       " 160,\n",
       " 201,\n",
       " 168,\n",
       " 124,\n",
       " 121,\n",
       " 133,\n",
       " 149,\n",
       " 179,\n",
       " 181,\n",
       " 148,\n",
       " 177,\n",
       " 97,\n",
       " 143,\n",
       " 139,\n",
       " 161,\n",
       " 135,\n",
       " 95,\n",
       " 145,\n",
       " 129,\n",
       " 130,\n",
       " 142,\n",
       " 154,\n",
       " 135,\n",
       " 132,\n",
       " 193,\n",
       " 143,\n",
       " 155,\n",
       " 220,\n",
       " 127,\n",
       " 146,\n",
       " 175,\n",
       " 128,\n",
       " 166,\n",
       " 132,\n",
       " 107,\n",
       " 97,\n",
       " 113,\n",
       " 179,\n",
       " 125,\n",
       " 158,\n",
       " 125,\n",
       " 159,\n",
       " 73,\n",
       " 141,\n",
       " 132,\n",
       " 125,\n",
       " 155,\n",
       " 140,\n",
       " 155,\n",
       " 170,\n",
       " 99,\n",
       " 111,\n",
       " 101,\n",
       " 149,\n",
       " 124,\n",
       " 212,\n",
       " 157,\n",
       " 144,\n",
       " 132,\n",
       " 122,\n",
       " 50,\n",
       " 159,\n",
       " 201,\n",
       " 217,\n",
       " 106,\n",
       " 108,\n",
       " 158,\n",
       " 171,\n",
       " 214,\n",
       " 148,\n",
       " 105,\n",
       " 173,\n",
       " 91,\n",
       " 195,\n",
       " 100,\n",
       " 189,\n",
       " 228,\n",
       " 168,\n",
       " 154,\n",
       " 191,\n",
       " 199,\n",
       " 168,\n",
       " 174,\n",
       " 119,\n",
       " 132,\n",
       " 129,\n",
       " 149,\n",
       " 154,\n",
       " 104,\n",
       " 155,\n",
       " 142,\n",
       " 190,\n",
       " 81,\n",
       " 85,\n",
       " 152,\n",
       " 169,\n",
       " 197,\n",
       " 154,\n",
       " 169,\n",
       " 109,\n",
       " 175,\n",
       " 198,\n",
       " 133,\n",
       " 130,\n",
       " 104,\n",
       " 190,\n",
       " 104,\n",
       " 108,\n",
       " 172,\n",
       " 135,\n",
       " 203,\n",
       " 93,\n",
       " 97,\n",
       " 66,\n",
       " 101,\n",
       " 166,\n",
       " 166,\n",
       " 78,\n",
       " 160,\n",
       " 87,\n",
       " 117,\n",
       " 130,\n",
       " 145,\n",
       " 153,\n",
       " 162,\n",
       " 97,\n",
       " 152,\n",
       " 214,\n",
       " 108,\n",
       " 191,\n",
       " 168,\n",
       " 143,\n",
       " 200,\n",
       " 171,\n",
       " 147,\n",
       " 149,\n",
       " 193,\n",
       " 204,\n",
       " 183,\n",
       " 158,\n",
       " 180,\n",
       " 126,\n",
       " 188,\n",
       " 159,\n",
       " 114,\n",
       " 89,\n",
       " 120,\n",
       " 199,\n",
       " 164,\n",
       " 164,\n",
       " 136,\n",
       " 159,\n",
       " 142,\n",
       " 176,\n",
       " 134,\n",
       " 115,\n",
       " 141,\n",
       " 112,\n",
       " 194,\n",
       " 135,\n",
       " 143,\n",
       " 157,\n",
       " 127,\n",
       " 179,\n",
       " 199,\n",
       " 151,\n",
       " 169,\n",
       " 158,\n",
       " 167,\n",
       " 162,\n",
       " 139,\n",
       " 161,\n",
       " 221,\n",
       " 156,\n",
       " 100,\n",
       " 188,\n",
       " 187,\n",
       " 165,\n",
       " 98,\n",
       " 136,\n",
       " 172,\n",
       " 149,\n",
       " 88,\n",
       " 125,\n",
       " 141,\n",
       " 100,\n",
       " 186,\n",
       " 122,\n",
       " 125,\n",
       " 170,\n",
       " 193,\n",
       " 180,\n",
       " 110,\n",
       " 122,\n",
       " 171,\n",
       " 119,\n",
       " 201,\n",
       " 180,\n",
       " 126,\n",
       " 160,\n",
       " 178,\n",
       " 202,\n",
       " 121,\n",
       " 108,\n",
       " 234,\n",
       " 115,\n",
       " 188,\n",
       " 171,\n",
       " 150,\n",
       " 138,\n",
       " 178,\n",
       " 233,\n",
       " 145,\n",
       " 112,\n",
       " 141,\n",
       " 113,\n",
       " 184,\n",
       " 105,\n",
       " 123,\n",
       " 67,\n",
       " 100,\n",
       " 142,\n",
       " 162,\n",
       " 143,\n",
       " 60,\n",
       " 145,\n",
       " 243,\n",
       " 144,\n",
       " 157,\n",
       " 146,\n",
       " 162,\n",
       " 96,\n",
       " 154,\n",
       " 116,\n",
       " 85,\n",
       " 133,\n",
       " 116,\n",
       " 91,\n",
       " 117,\n",
       " 119,\n",
       " 141,\n",
       " 152,\n",
       " 122,\n",
       " 151,\n",
       " 163,\n",
       " 150,\n",
       " 233,\n",
       " 172,\n",
       " 93,\n",
       " 170,\n",
       " 91,\n",
       " 188,\n",
       " 149,\n",
       " 219,\n",
       " 160,\n",
       " 239,\n",
       " 176,\n",
       " 225,\n",
       " 145,\n",
       " 168,\n",
       " 92,\n",
       " 125,\n",
       " 99,\n",
       " 167,\n",
       " 143,\n",
       " 211,\n",
       " 96,\n",
       " 128,\n",
       " 134,\n",
       " 115,\n",
       " 140,\n",
       " 223,\n",
       " 158,\n",
       " 136,\n",
       " 207,\n",
       " 146,\n",
       " 141,\n",
       " 106,\n",
       " 222,\n",
       " 126,\n",
       " 151,\n",
       " 171,\n",
       " 194,\n",
       " 128,\n",
       " 132,\n",
       " 167,\n",
       " 183,\n",
       " 216,\n",
       " 147,\n",
       " 182,\n",
       " 180,\n",
       " 209,\n",
       " 194,\n",
       " 109,\n",
       " 196,\n",
       " 194,\n",
       " 165,\n",
       " 170,\n",
       " 85,\n",
       " 148,\n",
       " 143,\n",
       " 165,\n",
       " 92,\n",
       " 171,\n",
       " 93,\n",
       " 166,\n",
       " 153,\n",
       " 88,\n",
       " 48,\n",
       " 205,\n",
       " 123,\n",
       " 146,\n",
       " 142,\n",
       " 131,\n",
       " 129,\n",
       " 160,\n",
       " 130,\n",
       " 141,\n",
       " 64,\n",
       " 186,\n",
       " 147,\n",
       " 107,\n",
       " 238,\n",
       " 147,\n",
       " 122,\n",
       " 167,\n",
       " 268,\n",
       " 166,\n",
       " 116,\n",
       " 144,\n",
       " 53,\n",
       " 101,\n",
       " 123,\n",
       " 96,\n",
       " 165,\n",
       " 127,\n",
       " 157,\n",
       " 163,\n",
       " 169,\n",
       " 156,\n",
       " 152,\n",
       " 144,\n",
       " 160,\n",
       " 199,\n",
       " 106,\n",
       " 160,\n",
       " 91,\n",
       " 134,\n",
       " 142,\n",
       " 54,\n",
       " 154,\n",
       " 82,\n",
       " 135,\n",
       " 164,\n",
       " 184,\n",
       " 141,\n",
       " 224,\n",
       " 105,\n",
       " 202,\n",
       " 141,\n",
       " 134,\n",
       " 167,\n",
       " 128,\n",
       " 133,\n",
       " 120,\n",
       " 144,\n",
       " 153,\n",
       " 59,\n",
       " 153,\n",
       " 126,\n",
       " 119,\n",
       " 140,\n",
       " 146,\n",
       " 170,\n",
       " 171,\n",
       " 162,\n",
       " 199,\n",
       " 91,\n",
       " 130,\n",
       " 177,\n",
       " 196,\n",
       " 177,\n",
       " 152,\n",
       " 122,\n",
       " 158,\n",
       " 137,\n",
       " 120,\n",
       " 176,\n",
       " 155,\n",
       " 109,\n",
       " 130,\n",
       " 147,\n",
       " 138,\n",
       " 146,\n",
       " 157,\n",
       " 164,\n",
       " 173,\n",
       " 99,\n",
       " 186,\n",
       " 184,\n",
       " 153,\n",
       " 152,\n",
       " 147,\n",
       " 89,\n",
       " 133,\n",
       " 173,\n",
       " 84,\n",
       " 152,\n",
       " 85,\n",
       " 101,\n",
       " 210,\n",
       " 186,\n",
       " 173,\n",
       " 178,\n",
       " 91,\n",
       " 70,\n",
       " 81,\n",
       " 177,\n",
       " 188,\n",
       " 167,\n",
       " 170,\n",
       " 107,\n",
       " 125,\n",
       " 112,\n",
       " 165,\n",
       " 182,\n",
       " 207,\n",
       " 114,\n",
       " 158,\n",
       " 213,\n",
       " 112,\n",
       " 79,\n",
       " 114,\n",
       " 130,\n",
       " 153,\n",
       " 127,\n",
       " 148,\n",
       " 218,\n",
       " 113,\n",
       " 154,\n",
       " 206,\n",
       " 148,\n",
       " 188,\n",
       " 177,\n",
       " 140,\n",
       " 95,\n",
       " 127,\n",
       " 162,\n",
       " 134,\n",
       " 71,\n",
       " 115,\n",
       " 234,\n",
       " 122,\n",
       " 164,\n",
       " 114,\n",
       " 214,\n",
       " 95,\n",
       " 125,\n",
       " 120,\n",
       " 158,\n",
       " 186,\n",
       " 194,\n",
       " 161,\n",
       " 138,\n",
       " 191,\n",
       " 68,\n",
       " 144,\n",
       " 204,\n",
       " 147,\n",
       " 175,\n",
       " 74,\n",
       " 142,\n",
       " 115,\n",
       " 123,\n",
       " 170,\n",
       " 155,\n",
       " 165,\n",
       " 98,\n",
       " 202,\n",
       " 154,\n",
       " 94,\n",
       " 133,\n",
       " 142,\n",
       " 187,\n",
       " 169,\n",
       " 145,\n",
       " 113,\n",
       " 169,\n",
       " 121,\n",
       " 145,\n",
       " 129,\n",
       " 191,\n",
       " 134,\n",
       " 199,\n",
       " 235,\n",
       " 121,\n",
       " 173,\n",
       " 221,\n",
       " 141,\n",
       " 82,\n",
       " 195,\n",
       " 78,\n",
       " 106,\n",
       " 128,\n",
       " 142,\n",
       " 152,\n",
       " 205,\n",
       " 115,\n",
       " 156,\n",
       " 168,\n",
       " 67,\n",
       " 90,\n",
       " 224,\n",
       " 164,\n",
       " 152,\n",
       " 180,\n",
       " 197,\n",
       " 155,\n",
       " 166,\n",
       " 115,\n",
       " 167,\n",
       " 84,\n",
       " 179,\n",
       " 149,\n",
       " 104,\n",
       " 101,\n",
       " 144,\n",
       " 184,\n",
       " 140,\n",
       " 203,\n",
       " 100,\n",
       " 123,\n",
       " 127,\n",
       " 175,\n",
       " 164,\n",
       " 114,\n",
       " 160,\n",
       " 117,\n",
       " 179,\n",
       " 191,\n",
       " 120,\n",
       " 124,\n",
       " 106,\n",
       " 202,\n",
       " 193,\n",
       " 234,\n",
       " 90,\n",
       " 209,\n",
       " 149,\n",
       " 114,\n",
       " 134,\n",
       " 131,\n",
       " 145,\n",
       " 143,\n",
       " 202,\n",
       " 118,\n",
       " 160,\n",
       " 163,\n",
       " 134,\n",
       " 138,\n",
       " 211,\n",
       " 137,\n",
       " 180,\n",
       " 171,\n",
       " 131,\n",
       " 136,\n",
       " 123,\n",
       " 168,\n",
       " 118,\n",
       " 162,\n",
       " 101,\n",
       " 118,\n",
       " 133,\n",
       " 71,\n",
       " 154,\n",
       " 160,\n",
       " 174,\n",
       " 139,\n",
       " 181,\n",
       " 138,\n",
       " 144,\n",
       " 119,\n",
       " 142,\n",
       " 168,\n",
       " 98,\n",
       " 162,\n",
       " 155,\n",
       " 103,\n",
       " 145,\n",
       " 148,\n",
       " 154,\n",
       " 121,\n",
       " 149,\n",
       " 121,\n",
       " 119,\n",
       " 58,\n",
       " 183,\n",
       " 125,\n",
       " 110,\n",
       " 137,\n",
       " 100,\n",
       " 110,\n",
       " 123,\n",
       " 105,\n",
       " 176,\n",
       " 125,\n",
       " 123,\n",
       " 94,\n",
       " 197,\n",
       " 148,\n",
       " 134,\n",
       " 167,\n",
       " 146,\n",
       " 184,\n",
       " 196,\n",
       " 183,\n",
       " 123,\n",
       " 143,\n",
       " 157,\n",
       " 152,\n",
       " 146,\n",
       " 155,\n",
       " 151,\n",
       " 127,\n",
       " 193,\n",
       " 119,\n",
       " 94,\n",
       " 125,\n",
       " 101,\n",
       " 203,\n",
       " 46,\n",
       " 251,\n",
       " 179,\n",
       " 115,\n",
       " 162,\n",
       " 165,\n",
       " 169,\n",
       " 188,\n",
       " 192,\n",
       " 98,\n",
       " 203,\n",
       " 159,\n",
       " 175,\n",
       " 200,\n",
       " 142,\n",
       " 155,\n",
       " 143,\n",
       " 168,\n",
       " 179,\n",
       " 131,\n",
       " 124,\n",
       " 105,\n",
       " 117,\n",
       " 63,\n",
       " 166,\n",
       " 207,\n",
       " 173,\n",
       " 167,\n",
       " 122,\n",
       " 172,\n",
       " 150,\n",
       " 130,\n",
       " 128,\n",
       " 226,\n",
       " 121,\n",
       " 129,\n",
       " 148,\n",
       " 125,\n",
       " 184,\n",
       " 142,\n",
       " 181,\n",
       " 138,\n",
       " 157,\n",
       " 102,\n",
       " 203,\n",
       " 149,\n",
       " 139,\n",
       " 148,\n",
       " 109,\n",
       " 168,\n",
       " 125,\n",
       " 107,\n",
       " 101,\n",
       " 132,\n",
       " 149,\n",
       " 211,\n",
       " 168,\n",
       " 189,\n",
       " 147,\n",
       " 150,\n",
       " 83,\n",
       " 166,\n",
       " 159,\n",
       " 139,\n",
       " 133,\n",
       " 120,\n",
       " 190,\n",
       " 139,\n",
       " 129,\n",
       " 153,\n",
       " 146,\n",
       " 173,\n",
       " 121,\n",
       " 149,\n",
       " 74,\n",
       " 111,\n",
       " 113,\n",
       " 168,\n",
       " 133,\n",
       " 166,\n",
       " 95,\n",
       " 157,\n",
       " 180,\n",
       " 156,\n",
       " 124,\n",
       " 142,\n",
       " 166,\n",
       " 132,\n",
       " 118,\n",
       " 175,\n",
       " 145,\n",
       " 173,\n",
       " 130,\n",
       " 115,\n",
       " 191,\n",
       " 151,\n",
       " 161,\n",
       " 191,\n",
       " 158,\n",
       " 142,\n",
       " 151,\n",
       " 102,\n",
       " 127,\n",
       " 160,\n",
       " 222,\n",
       " 101,\n",
       " 199,\n",
       " 184,\n",
       " 117,\n",
       " 101,\n",
       " 155,\n",
       " 135,\n",
       " 138,\n",
       " 131,\n",
       " 144,\n",
       " 150,\n",
       " 165,\n",
       " 141,\n",
       " 115]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}