Main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "CA = False\n",
    "total_episode = 3000\n",
    "TTC_threshold = 4.001\n",
    "base_name = f'origin_{TTC_threshold}_NOCA' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training samples: 938\n",
      "Number of validate samples: 403\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fcb9e25c7f57494990e04ab5522df852",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=3000), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Run origin_4.001_NOCA, Episode 101, Score: -0.01, Rolling score: -0.07, Max score: 0.30, Max rolling score: -0.00, collisions: 7   "
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-78f90b8bb4eb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m    184\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mddpg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpointer\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mMEMORY_CAPACITY\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    185\u001b[0m                 \u001b[0mvar\u001b[0m \u001b[1;33m*=\u001b[0m \u001b[1;36m.9995\u001b[0m  \u001b[1;31m# decay the action randomness\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 186\u001b[1;33m                 \u001b[0mddpg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlearn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    187\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    188\u001b[0m             \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ms_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-3-78f90b8bb4eb>\u001b[0m in \u001b[0;36mlearn\u001b[1;34m(self, replace)\u001b[0m\n\u001b[0;32m     86\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0matrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m{\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mS\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbs\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 88\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mctrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m{\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mS\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mba\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mR\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mS_\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbs_\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# {}place holder 处理输入\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     89\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     90\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mstore_transition\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\envs\\velocity\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    875\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    876\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 877\u001b[1;33m                          run_metadata_ptr)\n\u001b[0m\u001b[0;32m    878\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    879\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\envs\\velocity\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1098\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1099\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m-> 1100\u001b[1;33m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[0;32m   1101\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1102\u001b[0m       \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\envs\\velocity\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1270\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1271\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[1;32m-> 1272\u001b[1;33m                            run_metadata)\n\u001b[0m\u001b[0;32m   1273\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1274\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\envs\\velocity\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1276\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1277\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1278\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1279\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1280\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\envs\\velocity\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m   1261\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1262\u001b[0m       return self._call_tf_sessionrun(\n\u001b[1;32m-> 1263\u001b[1;33m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[0;32m   1264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1265\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\envs\\velocity\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[1;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[0;32m   1348\u001b[0m     return tf_session.TF_SessionRun_wrapper(\n\u001b[0;32m   1349\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1350\u001b[1;33m         run_metadata)\n\u001b[0m\u001b[0;32m   1351\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1352\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import random\n",
    "import numpy as np\n",
    "from simulation_env import Env\n",
    "import scipy.io as sio\n",
    "import pickle as pk\n",
    "import sys\n",
    "import os\n",
    "\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "get_ipython().run_line_magic('matplotlib', 'inline')\n",
    "\n",
    "#####################  hyper parameters  ####################\n",
    "LR_A = 0.001  # learning rate for actor\n",
    "LR_C = 0.001  # learning rate for critic\n",
    "GAMMA = 0.9  # reward discount\n",
    "REPLACE_ITER_A = 500\n",
    "REPLACE_ITER_C = 300\n",
    "MEMORY_CAPACITY = 20000  # 7000\n",
    "BATCH_SIZE = 1024\n",
    "\n",
    "###############################  Actor  ####################################\n",
    "class DDPG(object):\n",
    "    def __init__(self, a_dim, s_dim, a_bound, ):\n",
    "        self.memory = np.zeros((MEMORY_CAPACITY, s_dim * 2 + a_dim + 1), dtype=np.float32)\n",
    "        self.pointer = 0\n",
    "        self.sess = tf.Session()\n",
    "        self.a_replace_counter, self.c_replace_counter = 0, 0\n",
    "\n",
    "        self.a_dim, self.s_dim, self.a_bound = a_dim, s_dim, a_bound,\n",
    "        self.S = tf.placeholder(tf.float32, [None, s_dim], 's')\n",
    "        self.S_ = tf.placeholder(tf.float32, [None, s_dim], 's_')\n",
    "        self.R = tf.placeholder(tf.float32, [None, 1], 'r')\n",
    "\n",
    "        with tf.variable_scope('Actor'):\n",
    "            self.a = self._build_a(self.S, scope='eval', trainable=True)\n",
    "            a_ = self._build_a(self.S_, scope='target', trainable=False)\n",
    "        with tf.variable_scope('Critic'):\n",
    "            q = self._build_c(self.S, self.a, scope='eval', trainable=True)\n",
    "            q_ = self._build_c(self.S_, a_, scope='target', trainable=False)\n",
    "\n",
    "        # networks parameters\n",
    "        self.ae_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Actor/eval')\n",
    "        self.at_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Actor/target')\n",
    "        self.ce_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Critic/eval')\n",
    "        self.ct_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Critic/target')\n",
    "\n",
    "        q_target = self.R + GAMMA * q_\n",
    "        # in the feed_dic for the td_error, the self.a should change to actions in memory\n",
    "        td_error = tf.losses.mean_squared_error(labels=q_target, predictions=q)\n",
    "        self.ctrain = tf.train.AdamOptimizer(LR_C).minimize(td_error, var_list=self.ce_params)\n",
    "\n",
    "        a_loss = - tf.reduce_mean(q)  # maximize the q  \n",
    "        self.atrain = tf.train.AdamOptimizer(LR_A).minimize(a_loss, var_list=self.ae_params)\n",
    "\n",
    "        self.sess.run(tf.global_variables_initializer())\n",
    "\n",
    "    def choose_action(self, s):\n",
    "        return self.sess.run(self.a, {self.S: s[np.newaxis, :]})[0]\n",
    "\n",
    "    def learn(self, replace):\n",
    "        if self.a_replace_counter % REPLACE_ITER_A == 0 or replace:\n",
    "            self.sess.run([tf.assign(t, e) for t, e in zip(self.at_params, self.ae_params)])\n",
    "            \n",
    "        if self.c_replace_counter % REPLACE_ITER_C == 0 or replace:\n",
    "            self.sess.run([tf.assign(t, e) for t, e in zip(self.ct_params, self.ce_params)])\n",
    "            \n",
    "        self.a_replace_counter += 1;\n",
    "        self.c_replace_counter += 1\n",
    "\n",
    "        if self.pointer < MEMORY_CAPACITY:\n",
    "            indices = np.random.choice(self.pointer, size=BATCH_SIZE)\n",
    "        else:\n",
    "            indices = np.random.choice(MEMORY_CAPACITY, size=BATCH_SIZE)\n",
    "        bt = self.memory[indices, :]\n",
    "        bs = bt[:, :self.s_dim]\n",
    "        ba = bt[:, self.s_dim: self.s_dim + self.a_dim]\n",
    "        br = bt[:, -self.s_dim - 1: -self.s_dim]\n",
    "        bs_ = bt[:, -self.s_dim:]\n",
    "\n",
    "        self.sess.run(self.atrain, {self.S: bs})\n",
    "        self.sess.run(self.ctrain, {self.S: bs, self.a: ba, self.R: br, self.S_: bs_})  # {}place holder 处理输入\n",
    "\n",
    "    def store_transition(self, s, a, r, s_):\n",
    "        transition = np.hstack((s, a, [r], s_))\n",
    "        index = self.pointer % MEMORY_CAPACITY  # replace the old memory with new memory\n",
    "        self.memory[index, :] = transition\n",
    "        self.pointer += 1\n",
    "\n",
    "    def _build_a(self, s, scope, trainable):\n",
    "        with tf.variable_scope(scope):\n",
    "            net = tf.layers.dense(s, 30, activation=tf.nn.relu, name='l1', trainable=trainable)\n",
    "            a = tf.layers.dense(net, self.a_dim, activation=tf.nn.tanh, name='a', trainable=trainable)\n",
    "            return tf.multiply(a, self.a_bound, name='scaled_a')\n",
    "\n",
    "    def _build_c(self, s, a, scope, trainable):\n",
    "        with tf.variable_scope(scope):\n",
    "            n_l1 = 30\n",
    "            w1_s = tf.get_variable('w1_s', [self.s_dim, n_l1], trainable=trainable)\n",
    "            w1_a = tf.get_variable('w1_a', [self.a_dim, n_l1], trainable=trainable)\n",
    "            b1 = tf.get_variable('b1', [1, n_l1], trainable=trainable)\n",
    "            net = tf.nn.relu(tf.matmul(s, w1_s) + tf.matmul(a, w1_a) + b1)\n",
    "            return tf.layers.dense(net, 1, trainable=trainable)  # Q(s,a)\n",
    "\n",
    "    def savenet(self, file):\n",
    "        saver = tf.train.Saver()\n",
    "        saver.save(self.sess, 'save/' + file + '.ckpt')\n",
    "\n",
    "    def restore(self, file):\n",
    "        saver = tf.train.Saver()\n",
    "        saver.restore(self.sess, 'save/' + file + '.ckpt')\n",
    "\n",
    "env = Env(TTC_threshold)\n",
    "s_dim = env.n_features\n",
    "a_dim = env.n_actions\n",
    "a_bound = env.action_Bound\n",
    "\n",
    "# load training data\n",
    "train = sio.loadmat('trainSet.mat')['calibrationData']\n",
    "test = sio.loadmat('testSet.mat')['validationData']\n",
    "trainNum = train.shape[0]\n",
    "testNum = test.shape[0]\n",
    "print('Number of training samples:', trainNum)\n",
    "print('Number of validate samples:', testNum)\n",
    "\n",
    "# # Stop distance collision avoidance\n",
    "n_run = 3\n",
    "rolling_window = 100  # 100 car following events, average score\n",
    "result = []\n",
    "\n",
    "\n",
    "for run in [base_name]:\n",
    "    # name is the name of the experiment, CA is whether use collision avoidance\n",
    "    ddpg = DDPG(a_dim, s_dim, a_bound)\n",
    "\n",
    "    # training part\n",
    "    max_rolling_score = np.float('-inf')\n",
    "    max_score = np.float('-inf')\n",
    "    var = 3\n",
    "    collision_train = 0\n",
    "    episode_score = np.zeros(total_episode)  # average score of each car following event\n",
    "    rolling_score = np.zeros(total_episode)\n",
    "    cum_collision_num = np.zeros(total_episode)\n",
    "\n",
    "    score_safe = np.zeros(total_episode)\n",
    "    score_efficiency = np.zeros(total_episode)\n",
    "    score_comfort = np.zeros(total_episode)\n",
    "\n",
    "    for i in tqdm(range(total_episode)):\n",
    "        car_fol_id = random.randint(0, trainNum - 1)\n",
    "        data = train[car_fol_id, 0]\n",
    "        s = env.reset(data)\n",
    "        score = 0\n",
    "        score_s, score_e, score_c = 0, 0, 0  # part objective scores\n",
    "\n",
    "        while True:\n",
    "            # Add exploration noise\n",
    "            a = ddpg.choose_action(s)[0]\n",
    "            a = np.clip(np.random.normal(a, var), -a_bound, a_bound)  # add randomness to action selection for exploration\n",
    "\n",
    "            if CA:\n",
    "                # add collision avoidance guidance\n",
    "                state = env.s\n",
    "                space, svSpd, relSpd = state\n",
    "                lvSpd = svSpd + relSpd\n",
    "                RT = 1  # reaction time\n",
    "                SD = svSpd * RT + (svSpd ** 2 - lvSpd ** 2) / (2 * a_bound)\n",
    "\n",
    "                if space < SD:\n",
    "                    a = - a_bound\n",
    "\n",
    "            s_, r, done, r_info = env.step(a)\n",
    "            #             sys.stdout.write(f'\\r TTC: {r_info[0]}, hdw: {r_info[1]}, jerk: {r_info[2]}, fTTC: {r_info[3]}, fHdw: {r_info[4]}, fJerk: {r_info[5]}')\n",
    "            #             sys.stdout.flush()\n",
    "\n",
    "            ddpg.store_transition(s, a, r, s_)\n",
    "            replace = False\n",
    "            if ddpg.pointer > MEMORY_CAPACITY:\n",
    "                var *= .9995  # decay the action randomness\n",
    "                ddpg.learn(replace)\n",
    "\n",
    "            s = s_\n",
    "            score += r\n",
    "            score_s += r_info[3]\n",
    "            score_e += r_info[4]\n",
    "            score_c += r_info[5]\n",
    "\n",
    "            if done:\n",
    "                duration = data.shape[0]\n",
    "                score /= duration  # normalize with respect to car-following length\n",
    "                score_s /= duration\n",
    "                score_e /= duration\n",
    "                score_c /= duration\n",
    "\n",
    "                if env.isCollision == 1:\n",
    "                    collision_train += 1\n",
    "                break\n",
    "\n",
    "        # record episode results\n",
    "        episode_score[i] = score\n",
    "        score_safe[i] = score_s\n",
    "        score_efficiency[i] = score_e\n",
    "        score_comfort[i] = score_c\n",
    "        rolling_score[i] = np.mean(episode_score[max(0, i - rolling_window + 1):i + 1])\n",
    "        cum_collision_num[i] = collision_train\n",
    "\n",
    "        if max_score < score:\n",
    "            max_score = score\n",
    "\n",
    "        if rolling_score[i] > max_rolling_score:\n",
    "            max_rolling_score = rolling_score[i]\n",
    "            # save network parameters\n",
    "            ddpg.savenet(f'model_{run}')\n",
    "\n",
    "        sys.stdout.write(\n",
    "            f'''\\r Run {run}, Episode {i}, Score: {score:.2f}, Rolling score: {rolling_score[i]:.2f}, Max score: {max_score:.2f}, Max rolling score: {max_rolling_score:.2f}, collisions: {collision_train}   ''')\n",
    "        sys.stdout.flush()\n",
    "\n",
    "    # save results\n",
    "    result.append([episode_score, rolling_score, cum_collision_num, score_safe, score_efficiency, score_comfort])\n",
    "\n",
    "np.save(f'result_{run}.npy', result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14d65e5b208>]"
      ]
     },
     "execution_count": 12,
     "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": [
    "plt.plot(cum_collision_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14d6277a4a8>]"
      ]
     },
     "execution_count": 3,
     "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": [
    "plt.plot(rolling_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14d63ac4e48>]"
      ]
     },
     "execution_count": 4,
     "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": [
    "plt.plot(episode_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14d63b38e48>]"
      ]
     },
     "execution_count": 5,
     "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": [
    "plt.plot(score_safe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14d65ce97f0>]"
      ]
     },
     "execution_count": 6,
     "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": [
    "plt.plot(score_efficiency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14d65d4f828>]"
      ]
     },
     "execution_count": 7,
     "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": [
    "plt.plot(score_comfort)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot rolling score of three sub objectives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14d65dd5dd8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dPLOTDA33Y0piMFuPOzZSyt2RUpJVWkdSU3hldKA3ccHerE63X2/gp4wSArwMjI8NtHt+sOD6+26F07BYbRRXm7rlk/YxGrjt3BHc/0U6//ougwlxQdz41o6W89/ffgZDw/3Yn1vJL1/ZwiNfH2w5N/exHwBYNjOx19kHnUVcsDf5Fd3LtZ9ZUsvQcN9+3wyVGOLDFiX4PaKwykS92cqQsJN7Jn45I5F/fH2QvTkVjI87+T1sTqg3e1iY3cpWg4nB/endnJKaRmw9iDq5dvYQ5o+O5PkfjrYR+813n9MSYjk2NpDVt53BdbOHcOs5w7mpKePg1TMS+NtFKY7/EH0kJsi7Qxm5zsgsrWWICzbkRAd5UVDV4LDQ2MFA85pH65rIV81IJMDLwDPfHWnT9mhxLXmVDYPefw/KwndrWkIye+CTfvaKSfzx470I4PbzRtjdkRjm58lfLxrT8vrOBSPtRskMBGKCvMivrKe+0Yq3sfMKVtUNZkpqGl1SVD0q0BurTTbtAeif9YPTnczSJsFv9f308zRw3ZwhPL3mMNmldSSEatb/d01lJQfzhqtmlIXvxvQmzNDbqOfZKybxzBWTur39fKCKPWhJ0GxSKwBzKrKaNvG0dhH0FzFNd2B53bwTUWjuN6Ne1yGN9UUTYtAJePq7k6m/V+zJY2J8ULdLGbozSvDdmJYww0Dn7hwdyKQmapE6u7oQfHsugv4iOlATrfzKgVPXdyBTUNnAf9YdIyLAs4OxkRzux2VTE/h6XwF1jRYKKhtIy6ti4VjnFAU/3VCC78YUVDVg0AnCfAev4Af6aNkpj3UR5968azMxpP8FPyZIWfg94cPtWgjrwhT7In7R+GjqzVa+P1jUkl77zJHKfw/Kh+/WFFY2EOHviW4Au1z6g9hgb3LLTy2mx0triQrwOqWf31kEenvg7aFvccEpTs1PR0qYEB/Eny8cY/f89KGhxAZ589amLML8PIkM8GSkC8sKDiSUhe/GFFQ19EtemIFObDciddLzqhgZ5RpREELgY9Tz8vrjfJtW4JI5nE4cLaph9Cl+V3qd4JpZiWw5XsaX+/I5Y0S4qjvQhBJ8N6Y/Uv2eDsQEeVNQ2XnYo5SSzNJahkf0Pbtnb2neFXzLu7tcNofTgdIaE6W1jQzr4ne1bGYSU5qK7yzoxPUzGFGC76ZIKSms7N4uW3cnNtgbi022LGK3p7jGRIPZ5tIojicvnQCATofKnnkKjjRlPx3ehYvGy0PP29dP570bZ3D2qIFRT3YgoATfTamqt1DbaCXOScW4TyeaUz105tZpTq6W4ELB9/U08OwVk2gw2zhUUO2yeQx0jhZri+tdWfighRjPGBqq3DmtUILvppxoSnWsBP/kzyC71H7WzBNl2oXA1XHazWkpHF2BzJ3IKq3FaNARre5ce4USfDclpykqJc7J9VlPB5p3Y97+4R6757PLBsbFMT7Eh4QQHzaoKlidklVaR3yw96CPPOstSvDdlBxl4bdg0OuIPkW0UkZhNXHB3nh59H9IZnvmDA9jzYEiFj69rmVvgOIkWWV1g7YAuSNQgu+m5JTX4+dpINB78Fb3ac1V0xMAqG8qet2alXvzGeWikMz2zB2m5Xs5WFDNU2syumg9uNBSIte6dK3ldEcJvpuSU15PXLC3WrBqotk/376M45ZjpQBt0um6krNGRXDzmcmMiQ5gyzHly29NSU0jdY1WEkOV4PcWJfhuSk55nXLntKLZDZDZbuH2s915AFw7O6m/p2QXLw89f1o4il+kxlFQ1cDwe7+iqsHs6mkNCLLLmtJfKMHvNSq1ghsipSS3vJ4ZQ/uvkPhAJ6lJJB795iCf785ldHQAngYdH24/waWpcfgPsMLWkxK0TUNmq+Tp1YfbpKMerDRnNE1wQb4jd0EJvhuSVVpHtclCsgt3jg40gny0soVHimo4UlTDyr35AET4e/Kbs4a5cmp2GRMd0PJ8Ryd1WgcbWaV1CAHxIerOtbcowXdDmlP9thYNBUxLCmFrZhkHH1yIyWzjYEEV4+IC8TEOvD8Do0HH1nvO4aV1x3hjUyYmixVPg+ujiFxJdlkd0QFeg/7n0Bcc4sMXQiwUQhwSQhwRQtx1inY/F0JIIUSqI8ZV2KclD37A4E2LbI+3bpjGwQcX4uWhJ9DHg+lDQwek2DcTEeBFalIwZqtkb06lq6fjcrJKa1uqWCl6R58FXwihB54HzgfGAFcIITo4HIUQ/sDvgC19HVNxaoqqTQCE+yvBb42nQT8gYu17wszkMAw6wdf7VBbN7LI6l9QrcCccYeFPA45IKY9JKRuB94Aldto9CDwGqKTfTqawqoFgHw916+sGBHp7cObIcL7Ymzeoi5zXmCyU1DQqC7+POELwY4ETrV7nNB1rQQgxCYiXUq48VUdCiBuFENuFENuLi4sdMLXBSVG1iQh/lWvEXViQEkVxtaklU+RgpDkPkgrJ7BuOEHx7O3taTBEhhA54Cri9q46klC9JKVOllKnh4aokWW8pqmogQvnv3YbJTXnduyrE7s60xOArl06fcITg5wDxrV7HAXmtXvsDY4G1QohMYAawQi3cOg9l4bsXQ8N8iQzwZNPRUldPxWVkD4AU1u6AIwR/GzBcCDFECGEELgdWNJ+UUlZKKcOklElSyiRgM7BYSrndAWMr2mGzSYqrTSpCx40QQjAuNoj0/CpXT8VlZJXWEejtQaDPwNogd7rRZ8GXUlqAW4BVwAHgAyllmhDiASHE4r72r+gZZXWNWGySCBWh41aMivLnaHENNSaLq6fiErLL6pT/3gE4JA5fSvmVlHKElDJZSvlw07G/SilX2Gl7prLuncfJGHzl0nEnpg0JQUrYenxwunWySuuUO8cBqORpbkZzDL5atHUvUpOC0esEb2/OZs6j37eUZRwMmK02civqWwrZKHqPEnw3o6jJwleLtu6Fj9HAiEh/vj9YRE55Pe9syXb1lPqN3PJ6rDapYvAdgBJ8N6OoSu2ydVfGxpzMjfTetmye+e7woPDpZzXdzSgLv+8owXczCqsbCPLxOO1SCCi65pzREQCMiw2kos7MP1dn8PMXNpKWV0llnfvmzM8qVXnwHYUSfDejqMqkInTclIVjo9l273zeXz6DK5tKNh4sqOaCZ9Zz3tM/YnPT1AtZpXV4eejU99oBKMF3MwqrTSpCx40J9/fEx2jg70vH8e+rJrccL6wy8eTqQy6cmfPIKq0lMcRXlet0AErw3Yziqgblvx8kjIsNbHkeHejF8z8c5ZGvD7pdkrWs0jq1YOsglOC7ETabpEhZ+IOG5prFc4eH8dHNswB48cej/PKVLUjpHqJvsdrIKq1jaLhasHUESvDdiHK1y3ZQIYQg7f4FvHxNKrFB3qQ/sIAQXyMbj5Yy5O6vOFxY7eop9pmc8noarTaSw1W5TkegBN+NaN50pSz8wYOvp6Gl7oGP0cCmu89uOXfuU+uorD+9o3eOFmspoZXgOwYl+G6EKm2o8DTo2X//AsL8tO/A7R/scfGM+sZJwVcuHUegBN+NaEmroHbZDmr8PA1s//N8Lp4Uy5oDhbx7Gu/KPVpUS5ifkSAfo6un4hYowXcjmtMqqCgdBcA/LhkHaAu5pytHi2sYqtw5DkMJvhtRVG0i0FvtslVoeBr0XJYaT3ZZHUXVp2cp6aPFNcp/70CU4LsRhVUNyn+vaMNVM7QduVuPl7l4Jj2nrLaR8jqz8t87ECX4boQqbahoz8gof4x6Hbe8u4u/fb6f0hqTq6fUbVoWbCOUhe8olOC7EUVVJpUHX9EGT4OeP50/CoA3NmUx5aE1mCxWF8+qexwt0gR/mHLpOAwl+G6ClJKi6gZl4Ss6cN3sJKJa7c34YHuOC2fTfY4W1+Bp0BET5O3qqbgNSvDdhPI6M2arVD58RQeEEHzyf7NYcctsAPaeqOBIk/U8kDlaXMuQMF/0OpU0zVEowXcTmqMwlIWvsEdMkDfj44KYNiSED3fkMP+fP5KWV+nqaZ2S4yW1KoeOg1GC7yYUVjWnVVAWvqJzRkb6tzx/a1OWC2dyamw2SW55PfHBKkumI1GC7yaoWraK7jCsVcTLe9tOsC9nYFr5JTUmGq02YoOV/96RKMF3E1rSKigLX3EKzkuJBOCPC0cCcNFz62kwD7yonZyKegBi1YKtQ1GC7yYUVTUQ4GVQu2wVpyQ60Jujf1/E/505jJvOSAbgqpe3uHhWHckp1wQ/Trl0HIoSfDehsEoVPlF0j+aolz8tHMm0ISHsyCpndXqhi2fVltwmwVcuHceiBN9NKKpuUO4cRY8QQvDODdMZGenPnz/bR12jxdVTaiG3oo5Abw/8PA2unopboQTfTSisMhGpFmwVPcRDr+OhpWMprDLx2DcDpwh6bnm98t87ASX4boCUkuJqE+HKwlf0gqlJIYyPC+T1jZnc++k+V08HgLyKBrXD1gkowXcDKurMNFptKiRT0Wuev3IyAO9syW5JWtbfNJit3PvpPv757SEyS2tbirQrHIcSfDfgZESD+gNR9I74EB+23TsfgBvf3I7VJvt9DhuPlvDOlmye+f4IJotNFfJxAg4RfCHEQiHEISHEESHEXXbO3yaESBdC7BVCfCeESHTEuAqNzNJaAJJC1TZ0Re8J9/fkutlDOFpcy/vbTvDkt4fIr6zvt/F3n9A2gT24JIVfTIlj4diofht7sNDnJXAhhB54HjgXyAG2CSFWSCnTWzXbBaRKKeuEEDcDjwGX9XVshUZWk+AnhKiYZUXfuGPBCF7dcJx7mnz5aw8V88Vv5/TL2HtOVDAqyp9fzkzql/EGI46w8KcBR6SUx6SUjcB7wJLWDaSUP0gp65pebgbiHDCuoonM0joiAzzxNqpNV4q+4WNsawPuy63E1g/uHSkle3IqmBAX5PSxBjOOEPxY4ESr1zlNxzrjeuBreyeEEDcKIbYLIbYXFxc7YGqu5dX1x9mR5fzSclmltSQqd47CwSxM0Vwqhf1QDze7rI6KOjMT4pXgOxNHCL69ZNV2TQIhxNVAKvC4vfNSypeklKlSytTw8HAHTM15SCnJLq3r9HxpjYkHVqZzyQubnD6XrNI6kkKVO0fhGJpryDbn3cks6fx77ih2n6gAYEJ8oNPHGsw4QvBzgPhWr+OAvPaNhBDzgXuBxVLK06ewZid8sTefeY//wN8+34/FautwvnkhFU7mqncGdY0WiqpNysJXOIwXrp7CY5eMZ/rQUACOlTg/THPl3nxCfY2MaJW+WeF4HCH424DhQoghQggjcDmwonUDIcQk4D9oYl/kgDFdzuZjpYBWJ/SSFzZSXN32Gna48OQfiTOrC2U13WUkKgtf4SBGRPpz6dR4YgK98PM08PmuPKR0nh+/st7M2kNFXDw5Fg+9ihR3Jn3+6UopLcAtwCrgAPCBlDJNCPGAEGJxU7PHAT/gQyHEbiHEik66O23Ir6gnOtCLZTMT2ZNTydSH1/DAF+mYrTZKa0w8+/2RlrZ5Fc6z8LNUSKbCSQghuHhyLFszy/jvT8ecNs6q/QWYrZJF46KdNoZCwyGZiaSUXwFftTv211bP5ztinIHEkeIapiQG88CSsUT4e/LEtxm8uuE4r2443tImIcSH7LI68iucF8uc2WThJygLX+EE7rsohS/25PH3rw6ybGaSw9NvSyl5Zf1xRkb6M1Et2Doddf/UC2w2SX5FA/FNce+3nD2cAw8sZGxsQJt2t583ghBfI/lVzrTw6wj1NRLg5eG0MRSDF51OcN/iFADe25rt8P7XZhRzqLCaX88bihCqWLmzUblHe0FJjQmLTRITeDJ3jbdRz8rfzgU0q+VQYTUjI/15ad0xp1r4WaW1yrpXOJXFE2J4d0s2932RztLJcQR6O8a4kFLy3PdHiA70YvGEGIf0qTg1ysLvBfmVmsUeFWg/d40QglFRAQghiA70bmnvDLSQTOW/VzgPIQR/On8UALd/sLvX/WQUVtNoORnR9mNGMTuyyvnNWcMwGpQU9QfKwu8FzQIeHdh1dsqYIC+2Hi91yjxMFit5lfUqQkfhdCYnBDNvRDhrDhRxuLCa4d0Mn7TZJN+kFfDahuNsyyzn+jlD+PMFo7ngmfWk51cRG+TNpanxXXekcAjqstoLCpoSSliqhWgAACAASURBVHVH8KMDvalqsFBrcnw1oRNl9UipInQU/cPTl03E38vAXz7f360wzcKqBi56bj3/985OtmWWA7Auo5hPd+WSnl8FwBO/mKCs+35E/aR7QX5lA0a9jhBfY5dtY4K8Wt7jaFqSpikLX9EPhPgaueO8kWw+Vsb2rPJTtv02rYD5T/5IWl4VvztnOGn3L+CmM5LJLK3luR+OEBXgxbG/L2Jmcmg/zV4BSvB7RXZZHTFBXt2KKohu8vPnOWHhtjkkU1n4iv7iF6naou3rGzLtnpdS8tv/7eLGt3YwJNyXD2+ayW3njsDX08Cs5FDMVsmx4lp+PW8oOp2KyulvlOD3grS8KsbEBHTdkJMpi7NapVpwFFmltfh7GQj2USGZiv7Bx2hg6aRY1hwotOumXLk3ny/25BEZ4MnLy1KZmhTScm7akBDmDAsjPsRbReW4CCX4PaSy3kx2WR0pMd1L8hTZVGf2L5+n8dv/7SItr9Jhc8lsitBR8cuK/uT8sVGYLDY+3ZXb4dyKPVoarQ1/OpuIgLZrXF4eet6+YTrr7jxLVbNyEUrwe0h6nrbYlNJNC18IwaOXjGNWcihf7MnjgmfWk3TXlzz6zcE+5yfJLq1VETqKfmdqUggzhobw6NcH27gqcyvqWZ1eyDUzEzGcIieOMlBchxL8HtJsoXfXwge4bGoC79wwndvOHcGoKC2c7YW1R/nD+7sxWay9mofZaiOnXIVkKvofnU7w2CUTsErJXZ/sazFcnlqdAcCyWUkunJ3iVAwKwZdSsvFoCRarDatNsiOrnDMf/4Gd2aeONGhPRV0jD315AKDHt6RCCH53znC++f08jv59EWOiA/hsdx6pD67htVb5d7pLXkU9FptUaZEVLiEh1Ie7zh/Fuoxi1h4q5ut9+Xy0I4dRUf4kh/u5enqKTnDLjVeNFhtvbspkSmIwH+3I4ct9+VTUmTu0u/jfGznwwMJulQb8cm8+v3l3p/a+Sacq6NU1ep3gq1vn8m1aAX/+bD+PfH2Qq6Yn9igeWUXoKFzNz6fE8dfP0/jpcElL0sC/XZTi4lkpToXbCb7ZamPEn+1WULTL6L9+w44/zyfUr63F3mC28seP9rLhSAmltY0tx/964RiumzPEIXM9LyWKinozf/xoL/vzKpmcENzt9x4r1nLsK5eOwlX4GA1EBXi1iP3DS8equPoBjtsJfvtCJADXzEzk/iVjkVJS1WAh0NsDKSXzHv+BE2X1THloDb+alcS9F4xGAK9vzGxx3bTm/RtntFQBchRjorXF37Tc7gt+ZZ2Z97edYGiYLxEq2kHhQuKCvSmoamBMdABXTU909XQUXeB2gh8T5E3mIxfYPSeEaMn0J4Tgpz+eza3v7eLz3Xm8vjGT1zdmtml/7ewkFqREYTToemR994SUmABCfY3sz63qVvuMwmqueGkzpbWNPPbz8SriQeFS9E2bp+aPjnDxTBTdwe0Ev6f86/JJXDEtgctf2tzm+P77F+Dn6fwfjxCChFAfciq6LhQtpeSWd3ei0wk+vnkmUxJDunyPQuFMpg8NZcvxMuYMD3f1VBTdYNALPsCMoaFkPnIB+3Mr2ZtTyYUTovtF7JuJC/Zhb05Fl+2OFNWQUVjDPy4ep8ReMSD43dnDuGh8dLezZypcixL8VoyNDWRsbPfj6x1FXLA33+zPx2qTLbfI9lh/pASAWWphTDFAMOh1SuxPIwZFHP5AJy7YG7NVUlR96oyaO7LKiQ3yVrH3CoWiVygLfwDQvFFl5j++Z/7oSH4+JZakMF9GRQVQY7Lww8EivtiTx7fphSxMiXLxbBUKxemKEvwBwPQhIVw3ewirDxSw5kAhaw4UtpzT6wRW28mcO5dPU9WBFApF7xB9TeDlLFJTU+X27dtdPY1+Z1VaAavSCliXUYKvp56ZQ0NZMDYKvRBMHxqCp6HrXcEKhWLwIoTYIaVMtXdOWfgDjAUpUSxQbhuFQuEE1KKtQqFQDBKU4CsUCsUgQQm+QqFQDBKU4CsUCsUgQQm+QqFQDBIcIvhCiIVCiENCiCNCiLvsnPcUQrzfdH6LECLJEeMqFAqFovv0WfCFEHrgeeB8YAxwhRBiTLtm1wPlUsphwFPAo30dV6FQKBQ9wxFx+NOAI1LKYwBCiPeAJUB6qzZLgPuann8EPCeEEHKg7vpyV+rLwdwAQgAChK7V86akbaLpeMsxcYr2AnT6k+9txmYFcz1IGyBBSrCatfGdgacfBMQ4rj8poSoXLKaOx7Un7V7bO9bqXEgyGIyOm5+pBqyNdn4/vXjsz3oKtSVQV6qNHRgLRgflhLKaQWfo3mexWsBq0n63FlPTd7Qddvuxc8yR7dq30enB2/E1OBwh+LHAiVavc4DpnbWRUlqEEJVAKFDigPEHJpW58PoF7BwynRe9JPm1+Qgh0KHTHoUOndDhofPAoDNg0Bm0Y2jHm9uIpn8AFmnBbDXTaGuk0dpIo60Rs9WM2Wam0dqIrfnLazGBpb5FfLwkBEhJQGM9ATYbgVYr59bVM6v+1Mna2vNGgD8f+/thE9Ak5UghQOjxkBKDlHhIMEgrHtKGQYJBSgxoj8FWG9dVVpFgsXQ5lhl4MCyEXZ6eWIU2lkGCHomu6bkOiV5qX2KdZwB6vQfz9EEsM8ZoAqAzaH84fhEQOgw8A6BgL5iqtYuSzQKy6dFmg8ZqKM+E0qNUm2t5MjSYQ0YPLAgsAmwI7XM3fX4bos3Povm19lx7DWhthHYzbQO8EPgh8JOCKPT8xRZEpM4LbGZorG36vTVdKFtfNJFgaYTKbLs/MwtQotdTrdNRoxNU63Q0CNHye2q+BI1obCTEasMiaPlsFqHDKkTLo0kI8rz8mHjBc8QNW9Cj74ldijPgxdnahQrAJxSu/gRiJvat3y9+Dzte04wRgxcYPEFnYIeHjg1GPVKnxyol0mbG29LIrLpaxpsaHeLL3utp5N0Afyp0Omyt9Fq0utbbk3odMM5k4oaKKjw66zw2FX79nQNm2RZHCL69z9Tecu9OG4QQNwI3AiQkJPR9Zq5k55u8Zynm4fKNRHiHMzFiEkIIbNKGlBKbtGGTNszSjMVqwWwzY8HS9jza82Z0QodRb8SoM+Lr4YuHzqPltVFvRNckKhz6GurqwCcUKQQmnY5KIanyDuEIkjxzJR8F+PNA9DksDRxDG0FpebS1OVZhreeZrPeJMfgyxhiCQKADhM2CtDZiAcxILIBFCCw6PWZpowEbZqn932iu4tvgUP4z7JeM8+28EHyOqZz7s1eyufoYZwaOwEfniQBsSCzShrXpZ2Np+hlaa0uwmGsplI08bqthWEUesxptmojbzFBTRJuvm4dv08VA12QZ6rVHgxGCh0D8DN6RZXxcsoVpfon46DzRN12A9c0XbAQ6oV2M9U2vhdAhmn5P2muBzlSDqClCh0RIEEjqsVEjrVRLKz9SS4LOxB8ry6AqD+Kng2dTumF7d1RCwNil4B/d5vezoTabuwt+oNzWs4t4V3itv4PnvHyZHjenbx0dXKmJ/YVPa6L89Z/gx0fhiv+1NDlWeYxndz7LRckXcXbC2V33mbtDE/uhZ0FcKlgawGKi3FzLzRUbqMeGoel7qsNAI568EORPpN6XcwOSSfVN0P5+hAEPocNHZ2SEVxi69ta2HUdEkbmaG4+8gQCSPIPRN11CZEdZa+ri5PEGaeEnUyl5SbN4IPoc+1XrfJ1TUMYRgp8DtM7oFQfkddImRwhhAAKBsvYdSSlfAl4CLZeOA+bmGmxWvk57m0dDg5lbV88TY5fhM+VX/TO2lPDjy5ByMVz0tN0mteZabl5zM3/N/45dvgE8MPuBLrv9aN/LNGZaefL81xgRPKJXU8uryeO6Vdex7PDrhHhpBVwEAol2gbPYLJisJhosDXgZvLh/1v1cPPzibvdvspq4ZMUlPOwv+WTJJ3jqm+r9Wkya5V50AIISIHbyKfuRUvLlZ4uZGjWVVxa82qvP2l1uW3sbKwu284ffbMEDHei79yfZaG3kpb0vkVGeQUFtAQfLDpIQkMAtY5YR4BlAgEcAfkY/vAxe2sW56W7RbDOzp3gPVmlFL/R46DzQ6/TohV670xTa3aZep8eQs5OH9/6bG767GW+dkSDvUBL8E3hozkNE+fYw/UfuDs21lXqt9rr4IGx8DmpLwVer7/Do1kfZmLeRNdlrmBY1jQVJC1g0ZBF+Rj/7fe5+FwzecNlbJy+SwObjX1O/7ifeWfQO48PHtxyvaaxhbc5avs38lvdz1/N2+d4OXS5OXszDcx7u8uO8tPkhTNj4fMnnJAT03Dh9dtezvLT3JRZM/S1zYvt4Me0BjhD8bcBwIcQQIBe4HLiyXZsVwDXAJuDnwPfu6r83WU08vvp3fOAnSPGN45GyTHyO/wT9JfgV2dBQCVHjOm3i6+HLK+e9wuPbH+d/B//HxcMvZmJE57fWh8oO8Vb6W8yKmdVrsQeI8YvhlQWv8GbamzRYGzrcvRh0Bjz1nvgZ/Vg6bGmPRcVT78k90+5h+ZrlvJH2BjeOv1E7YfCE8JHa/y6QUvLUjqfIrMpkWcqyHo3fGxYnL2Z11mrW5vzIuYnndvt9L+55kf/u+y/JgclE+UVx8fCLuX7c9cT7d51NdWRI1z8HAGLn8Iremy83PUqprKLcGMFXRTt4Pe117prWIRjv1OTuhKTZJ1+PvQQ2/Euz/KdcQ6Wpks35m7k25VpCvUP538H/8eDmB1l7Yi3/nv/vjv3VlcGud2DM4jZiD7Alfwv+Hv6khKa0Oe5n9OPCoRdy4dALqWmsIbs6G7PN3OIm/TbzWz4+/DE/G/YzpkZNBaC4rphN+Zuobqym3lJPnbmOkvoSPjvyGZeOvLRXYg9w0/ibWHl0JW+kvXF6CX6TT/4WYBWgB16VUqYJIR4AtkspVwCvAG8JIY6gWfaX93XcgciG3A08vOVhTlSf4JJ6K7f+/A0CPv+tZs30FwX7tMeo8ads5qH34NbJt/LN8W94Pe11no6wfzfwzfFvuPunu/E3+vO7Sb/r8/Ri/WK5e/rdfe6nM2bFzmJ27GzeP/g+14+9Hr2uZ9lF9xTv4bW01zDqjCwasshJszzJnNg5xPvHc+/6e4nwiWBC+IQu33O04iiv7n+VJclLeGjOQ06dX0Tqr7l2zCWw+q+w6y1sKXP59PCn/Gbib/A3drPSVXUBVOdBTKs7q6jxEJQIB76AKdewKW8TNmnj7ISzmRgxkWVjlvH49sd5O/1tyhvKCfZqt4CZsUpbp5p2Y4fhtuRvITUq9ZS/ez+jH2NC2wYTTo6YzLqcdTyx/QmuHn01qzJXsSF3AxZ5cs1JL/T4GHyYEjmFWyff2r3PbwcPvQfnDzmfN9LeoKqxigBjQK/76gkOyZYppfwK+Krdsb+2et4A/MIRYzmDOnMdNeYaasw1lNaXMipkVPe/zGiuiie2P8HqrNUk+cXzn6IyZo39JfhFQsQoOPaDFkmg73SJxnEU7NN8v5EpXTb19fDlZ8N+xmtpr/Hb734LoK0p2CxYbVaqGqvIKM9gdMho/nvefwn07P/yj71h6bCl3PHjHWwp2MKsmFndfl9aaRrLVy/HS+/F2svW4uvh/MpiBp2BZ89+lutWXcc/t/+TN85/o8v3vJX+Fh46D25Pvd3p8wPAJwQWPwvSxtXpH7AyNpq309/m5ok3d+/9uTu1x9auNCE063zzi1Bfwfrc9QQYAxgbNrbptGBh0kLeSn+LLflbWDhkYds+932guedi2rrncmtyyanJ4eoxV/f4Y3oZvLhz6p38cd0fuWf9PUT7RnP1mKu5KPkiIrwj8Pbwxqgz2ve594Iz48/klf2vsCF3A+cPOd8hfXaF26VHNllNLF+9nGjfaKzSSqhXKAHGAIK8giitL6XWXEu8f3yLmOVU55BRnoFVWlv68NB5kBiQyMSIiQR7BjMkcAhDA4cS5x/XQfQOlB5g+erl1FnquGXiLVxbb8O4bwNMvEJrEDFGW6wqO9Ytl0KfKdirRaQYfbrV/KrRV7GneA85NTmaP7fJl6vX6Yn0ieSMuDO4bux1nftRByBnxp+Jv9GfL45+0SPB/+LoF9RZ6vjXWf/qF7FvJjkomRvG3cBj2x5jf8n+FtGzR1lDGSuPrWRx8uKOVq8zEQIufIqU4+uYIHxYcXQFN024qXvil7dTWxhvf9c54nzY+CwyayOb8jYxM2YmBt1JSUoJTcHf6M/GvI1tBT9/Dxz9HubdqS28t2Jr/lYApkVN69XHPH/I+QwNHEq9pZ5xYeN6fIfYE8aFjSPYM5i1J9Yqwe8txXXFVJoqOVZxjHJT13HfY0PHsmzMMkK9Q7FKKzF+Mewp2sPhisOsOr6KanN1S1u90CMQ3JZ6G1eMuoIPMz7k6R1PE+gZyFuL3iLRPwFenKv5z5u/3BGjtceiA/0j+IVpXS5KtibSN7JbVuXphKfek4VJC1l5bCW15tpuiXelqZJPDn/CvLh53YsQcTBLhy3l37v/zZtpb/LYGY/ZbVPdWM0ffvgDZpu5VxZsnzF4QuJsluau5T5ZR0Z5RvfWA/J2Q/iojkZI7GTQGTh+fA1F9UXMiJ7R5rRep2dG9Aw25m1ESqldXBpr4b2rtAYTr+ow1JaCLYR4hTAsaFhvP2X31zj6iF6nZ27cXNaeWIvFZmlzsXMWbif4cf5xfLrk05bXFQ0V6HQ68mvyifKNwtfDl7KGMsw2MxHeEXjYcbMsTDppTZhtZnYX7aa8oZytBVt5/9D7PLbtMZ7Z+QwN1gZmx8zmvln3aQuMOduhcB9c+NTJjRRhIzQXS9EBSPmZcz+8uUFbtJ3Yfs188LE4eTEfZnzI3zb+jcfnPd6lJfrx4Y+pt9Rz9WgXCCmaT/kXI37BG+lvcE3pNR0WHAHeTn+bnUU7uTP1ToYGDnXBLIH4aZy1/wPuD4jn+xPfd08ci9Ihyc7CpIc3RI1jS8F20MH0qPbbd2BWzCxWZ63meNVx7TNvewUqT8D8+yFkSJu2Ukq25m9letR0h7ldnM2Z8Wey4ugKdhXtalkodiZunzwtyCuIAGMAI0NGEugZiEFnIMIngli/WLti3x4PnQdTo6ZyXtJ5/HnGn9mzbA9PnfkU5yWdx13T7uL5c54/GU2y+x0txntcq+UKD28IGap96Z1N+XFAauFvg5wJ4RMYHzaeVZmrOFB24JRtj1Yc5c20NxkdMpqZMTP7aYYduW7sdQQYA7h85eUs+GgB966/l4qGCkATs0+PfMrs2Nn9Ej3UKfHTCLHZGOMTzZb8LV23ry/Xdi1HtM+20tzfDLY1FBDtE0Wcf1yH082/j015m7QDGd9A9ASY8/sObY9XHqe4vphp0b1z57iCWTGzMAgDG3I39Mt4bi/4jkYndMxPnM/Dcx7mqtFXnfTx2Wxw8EsYfm6HMDHCR2kWvrMpO6Y9hrrI+htACCF49pxn0QkdH2Z8eMq2z+x8htKGUte4SVoR5BXE24ve5tbJtzI2bCxfHvuSRZ8uYl3OOtZkryG/Np8Lhlzg0jkSMQaMfsywGdlTvIc6c92p2xcdPPm+dphtZh4W5az28WSaf5JdqzzWL5bEgEQ25m3Udkfn7Yb4GR3aAWzK1y4K7V1DAxlfD1/Gh49vmbuzUYLvKHK2QU0hjLqw47mIMVB2VHO5OJNmwQ9Rgg8Q4hXCjOgZfJzxMQW1BXbbVDdWsz53PVeOupLFyYv7eYYdSQxI5IZxN/DkmU/y4UUf4q335jff/Ybb1t5GiFcI8xPnu3aCOj0MPZMZhUex2CzsKNxx6vYFTZubmteyWrE1fyvvFW4kwmLhCo/ITruYET2DbQXbMBelg7m20zWqLflbiPWLtXunMJCZGTOTA6UHWu7mnIkSfEex/2PQG2HEeR3PRYzWUhWUHnbuHMqOaQmXnJB06XTl9tTbkUg+OPQB9vb6fZv5LY22Ri4Y6mLL2Q7Dg4fz8eKPef6c57ln+j28dO5LeBu8XT0tGHIGk8pz8dQZ2Zy/+dRtD66E0OEQ2FGEdxXtQid0rKg2kFJ6ws6bNWbGzKTeUs/eI19qB2ImdWhjsVnYXrD9tLLum5kZMxOJZHNBFz9LB6AE3xHYrJD2KYw8H7zsxKo33846261TdkxZ9+0YHjScBP8E/rvvv3x9/OsO5z858gnJgcmMC+t8Z7IrCfIKYl7cPK4YdUW/RY90ybBz8JQwySv81IJvaYTsLTBigd2MkbuLdzMieAS+sVO00M1OmBo1FZ3QsTlvExj9tQtIO9JL06k2V5+Wgp8SmoK/hz+b85Tgnx6c2AK1RTBmif3zocmg83D+wm2pEvz2CCF4dcGrRPpEcvf6u9ldtLvl3OHyw+wt3svFwy8+baI6BgShyRA8hOmNNjLKMyip7yTpbeY6LRVx4uwOp8w2M/uK92k7i2MmadFltaV2uwkwBpASmsKWmiwtu6auo2w1LyCfTgu2zRh0BqZFT2N97nrqzHU0Whux2qxdv7EXKMF3BOkrQO8Jw+24c0DbYRs23LkWvrleC1ezY/0MdiJ9I/nd5N9hkzae2P5Ey/GPD3+MQWfgouSLXDi705TIFGZWagLdvNmpA5nrtSykyWd1OLUhdwN1ljotj0zzbtkTnVu400PHs09noTbR/ka6LflbGBk8siUp3+nGhUMvpLCukOnvTmfK21NY9rVzIrHcLg6/35FSyweSfHbH6JzWRIzWFnadRVlTSGaoCsm0x+LkxZTUl/DUjqc4UX2CBksD7x96n0VDFvXvjlV3IWI0ow59TUDwaDbnb2bRUDt5h05s0zYgenRcd1iTtQZ/oz+zY2eDTYJ3CHx8A/whTUvl0I4ZOl9eFoIt/oG03xaXW5PLjsIdXDn69N1/cnbC2Tww6wHKGsqwSRth3mFOGUcJfl/J2wlVOXD2vaduFzFaW9g11WgVmhxNeab22G4ziuIkC5MW8vSOp3lw04MU1xcTYAzov3w07kbEGPTSyvSgkWzK33RyJ2wzVov2tzG5o6VqsVn4MedH5sbOxUPnofkZzn0AVtwC39wNF/+nw3smV5bga7OxriG/RfB3Fu7kye1PsrdkL3qhH5AL791FJ3QsHb7U6eMowe8r6Su029aRXeTCCG8KSys+BHFTHD+PZsEPVoLfGTF+MVw8/GI+Pvwxvh6+PHHGE6etC8DlRGr5fmZ4BLO6dhvZ1dkkBiSePF+4H8x1ENdx9+jOwp1UmCrahphO/iWkfQLpn8Oix8GrbfZIj5ztzJKefH7sS4pN5RTVFXGw7CDh3uEsH7+c+YnzGRUyyikf1Z1Qgt8XpIQDK2DIvK5DIVty6qQ7T/A9A1RIZhfcN+s+bku9DW+9d7d2Wis6IXQYGP2ZUVsLwOa8zW0Ff+/72mN8x3QJ7x16D38Pf2bHtFvMPfMeeGU+pH/W9s6grgxObObG1KvJtxVRXFdMmHcYy8cv5/px1w+MUNXTBCX4faFgnxYKOeu3XbcNTtKq8zgrUqf8OAQn9m9R6tOU/so97tbodBAzkfjCA8SExLA5fzOXjbpMO7f3Q9j8b0i9HoLaFmRJL01nTdYarh93PT4e7ZKpxaVquad2vdNW8A99DTYLoyYs43+xTjCWBhEqSqcv7PtQc+eM7iQcszU6vRZ+lu2kWNvSI5rVpVD0F7GTEQVpzIiaypaCLVgbqrRatZ8u10Ixz3+0w1v+ueOf+Hn4cdXojpkuEUJL/HdiM5QcOXk8/XMIjO+Q+17Rc5Tg9xYptS/i0LNaanJ2ScxELTTTZnPsXCyNUJ6lBF/Rv8RMBpuZmd4xVDdWs+yd2ZRve0mrBXHF/zoU/Kk117KjcAc/H/nzzqNQxl+uZZfd8672uq5My30/Zom6e3UASvB7S0kGVGR1vVjbmrARWlm2ys63kfeKiiyQViX4iv6lKafN/LoGrqi3stcAK874P1jyvN0d55vzN2OxWZgbO7fzPgOiYdh82PoybP0vrPw92Mww4QpnfYpBhRL83nJghfY4YkH339NcAKX4kGPnUtKUo0cJvqI/CYwHvygM393PPQW5DPeJZl2D/SR1AD/l/ISvhy8TIyaeut+5d2iG0Vd3aHfRk34JUZ1XAVN0H7Vo2xukhH0fQcJMu0mhOiW8KWysKN1+krXeUnZUe1RpFRT9iRBwxp2w5SWY9mvm6Wt5I+0Nqhur7daE3lG4g9TIVC32/lQkTIfbD2lhnUY/8A5y0gcYfCgLvzcUpkHxQRj38569zycEghIhd7tj59OcJdPODkWFwqlMvQFu2aoJftw8LNKi5a5vR0VDBZlVmV1b9834hGjGlBJ7h6IEvzfsfleLzhnTi51x8dPhxFbtLsFRqCyZigHA+PDxBBgDWJezrsO5vSVaXvwJ4RP6e1qKVriv4NtsUJmrFT3uDpZGTchfnAOr7oWMVWAxdWxnqoadb8KYn3U/Oqc1CdO1QikV2T1/b2cowVcMAAw6A7NjZ7M+dz022TYSbUfhDgzCYLdWr6L/cD8fvqUR/jka6lqlbB17CSx+DoxNGz0aqrS4eKOvlt9mxa3QWH2yfcE+2PRc2369AmHomVp5tcZqmPKr3s0vetLJMYITT922O1hMUJmjohgUA4Iz4s7g6+Nfs79kP+PDx7cc35q/lXHh4zputlL0K+4n+DUFEBDTVvD3f6z9F3oweGqLQfaY9TsYvVizmLM2aFZ+TVPUQUOlFjGQ/jl4BkInaVq7pDlSp+gAjLZTDrGnlB3XqmkpC18xAJgTOwed0LE+d32L4Nc01pBels4N425w8ewU7if4QQlw008nX1st8PWdsP1VLVa9vdiHjYRL34SIVomX4qfChMs0P7u1EY79qF1Eagph7weQeq12h9AbPP20hVtHpVhorhQU3c3FMIXCiQR6BjIyeCQv7HmBnOocHprzELuKdmGTNqZGdUykpuhf3E/w26M3wIVPaf8tJq3ubPOOPSlPvXtPCO2OoCWEciwMO6fvFIBV3gAAFLBJREFUc4oY47hiKDnbtKRpYSMc059C0UcWJC3gQNkBvjj2BRMjJpJTk4NBZ2BC+ATMZjM5OTk0NDS4epqnPV5eXsTFxeHh0f0kgO4v+K0xeLZ97aqt2pFj4Mhqbb3BYOxbXye2aTse7ZR9UyhcwfXjrufasdeyfPVyHtz8IF56LyaET8Db4M3x48fx9/cnKSlJlZXsA1JKSktLycnJYciQ7qdEVyrhCiLGgM0CpYf71k9tCRTug9hUx8xLoXAQOqHj3un34m3wxmKzcP3Y6wFoaGggNDRUiX0fEUIQGhra4zulwWXhDxSac+MXpkNkH8LUmlMqJMzo+5wUCgeTFJjEZ0s+Qy/0RPpGthxXYu8YevNz7JOFL4QIEUKsFkIcbnrsUH1DCDFRCLFJCJEmhNgrhLisL2O6BaHDtY1bfV24rcjSHlWEjmKAEuMX00bsFa6lry6du4DvpJTDge+aXrenDlgmpUwBFgJPCyEG935pg1FLdNZXwS/PBISWxEqhUHQLvV7PxIkTW/4/8sgjAPz000+kpKQwceJE6uvrufPOO0lJSeHOO+/kxRdf5M033+y0z7y8PH7+8x6mWnEBfXXpLAHObHr+BrAW+FPrBlLKjFbP84QQRUA4UNHHsU9vwkdCwf6+9VGeqeUb6evCr0IxiPD29mb37t0djr/zzjvccccdXHvttQD85z//obi4GE9Pzw5t2xMTE8NHH33k8Lk6mr4KfqSUMh9ASpkvhIg4VWMhxDTACBzt5PyNwI0ACQkJfZzaACd0GBxYCVZzh0IR3aY8UyudqFCchtz/RRrpeVUO7XNMTAB/u6jn62Ivv/wyH3zwAatWrWLNmjVUV1dTW1vL9OnTufvuuzlw4AB+fn7ccccdHDlyhJtuuoni4mL0ej0ffvgher2eCy+8kP3792O1WrnrrrtYu3YtJpOJ3/zmNyxfvpy1a9dy3333ERYWxv79+5kyZQpvv/02Qgi2bdvGrbfeSm1tLZ6ennz33XcsWrSIZ599lokTtT02s2fP5oUXXmD8+PFdfJrO6VLwhRBrgCg7p+7tyUBCiGjgLeAaKaXdkk9SypeAlwBSU1MdmF1sABI6TNsIVp4FYb3MY1+eCckO2BegUAwi6uvrW0QU4O677+aGG25g/fr1XHjhhS2uGT8/v5Y7gfvuu6+l/VVXXcVdd93F0qVLaWhowGazUVRU1HL+lVdeITAwkG3btmEymZg9ezbnnaft5dm1axdpaWnExMQwe/ZsNmzYwLRp07jssst4//33mTp1KlVVVXh7e3PDDTfw+uuv8/TTT5ORkYHJZOqT2EM3BF9KOb+zc0KIQiFEdJN1Hw0UddIuAPgS+LOU0klFXU8zQodrj6VHeif45nqozlcWvuK0pTeWuCPozKXTHaqrq8nNzWXpUi1TrpeXV4c23377LXv37m1x8VRWVnL48GGMRiPTpk0jLk6roTFx4kQyMzMJDAwkOjqaqVO1ncgBAQH8f3v3HhxVlSdw/PujSQhMkoUkGMKE5RFQTAyE8MjojkMCEgTKQnQcUGrAVQpZFvAxizqLhfjg6c4WSKkDzloLyErElajMogMYHrosNIHmMbxxoHACGEgYeUQEcvaPezuE0J2ku0n6kd+nqus++nT3+fXt/HL63NvnADzyyCO89tprvPHGG7z33ns8/vjjftW5ukC7dD4FxgJz7OUnNQuISDSwClhqjFkZ4OtFjsQ0a3n2SO3lvHGPtqkJX6lGY+oxrLkxhoULFzJ48I2z4W3YsOGG8wEOh4OrV69ijPF4iWWrVq0YNGgQn3zyCR9++CHbtwc+j0agV+nMAQaJyGFgkL2NiPQRkT/YZX4F/AJ4XERc9k0HfmmVAC0T/E/45cespSZ8pRpNfHw8qampFBYWAnD58mUuXbpxfK7BgwfzzjvvcOXKFQAOHTrExYveh2nv3r07JSUlOJ1OwPoWcfXqVQDGjRvHlClT6Nu3LwkJgU9wFFAL3xhzFripE9kYsx0YZ6+/D7wfyOtErIQu1sic/tCEr5Rfavbh33///VWXZtbHsmXLeOqpp5g+fTpRUVGsXLmSZtWGNhk3bhzHjh0jOzsbYwxt27at+gfhSXR0NAUFBUyePJmKigpatmzJunXriI2NpXfv3sTHx1ddORQoqc9XlGDo06ePuRVfYUJawa+tqRInOX1/7Oe/heIl8K9/Dd6YQEr5aP/+/dx5553BrkbYKCkpITc3lwMHDtzwT8XN0/spIsXGGI/jrehYOsEU3x6+P+nfY92XZGqyVyoiLV26lJycHGbOnOkx2ftDx9IJprgUa/asy+ehRZxvjy0/Bm3qP0qeUiq8jBkzhjFjxtzS59QWfjDFpVhLX1v5xuiPrpRSPtOEH0zuOW19PXF78Yw1c5cmfKWUDzThB5N7ftszh2ovV1OZPTKFJnyllA804QdTyzbWtfhlHocW8s49PWL1eXiVUqoOmvCDLaELlP3Ft8d8tx+iY3VYZKX8MHPmTDIyMujRowdZWVls3brVa9kDBw6QlZVFr169OHrUe8Ns1qxZDVHVW04TfrD58+Orsm+sx+klmUr5ZMuWLaxevZodO3awe/du1q1bR4cO3htOhYWFDB8+nJ07d5KWlua1XLgkfL0sM9gS02DPSrjyA0TdPBCTR+eOX+//VypcrXkRTu25tc/ZLhOGeP/V7MmTJ0lKSqoa0yYpKQmAV199lc8++4yKigruueceFi1axJo1a5g/fz4Oh4NNmzZRVFTE+++/z5tvvsmPP/5ITk4Ob7/9NtOmTav69W5GRgZdunQhKSmJp59+GoBp06aRnJzMlClTbm2sftAWfrAldAHM9cHQ6mLssq07Nmi1lIpE+fn5nDhxgttvv52JEyeyceNGACZNmoTT6WTv3r1UVFSwevVqhg4dyoQJE3j22WcpKipi//79FBQU8PXXX+NyuXA4HCxfvpw5c+ZUjcC5fPlynnzySZYsWQJAZWUlK1asYPTo0cEMu4q28IPNfaVN+TFoe3vd5c8ehas/aAtfhb9aWuINJTY2luLiYjZv3kxRUREjR45kzpw5xMXFMW/ePC5dukRZWRkZGRk88MADNzx2/fr1FBcXVw1jXFFRwW233TznU6dOnUhMTGTnzp2cPn2aXr16kZiY2Cjx1UUTfrBVT/j14e7vT9KEr5Q/HA4Hubm55ObmkpmZyaJFi9i9ezfbt2+nQ4cOzJgxgx9++OGmxxljGDt2LLNnz67zNdyTl5w6dYonnniiIcLwi3bpBNtP2kJUK6tfvj7c5dpol45Svjp48CCHDx+u2na5XNxxh9V4SkpK4sKFC17nph04cCAfffRR1exWZWVlHD9u/T1GRUVVDYcMMGLECD7//HOcTudN4+IHk7bwg03EauXXt4Vffgyax0BscgNWSqnIdOHCBSZPnsy5c+do3rw5Xbt2ZfHixbRu3ZrMzEw6depU1WVTU3p6Oq+//jr5+flUVlYSFRXFW2+9RceOHRk/fjw9evQgOzub5cuXEx0dTV5eHq1bt8bhcDRylN7p8Mih4INHrbltJ/5v3WVXjIYzh2HStoavl1K3WFMZHrmyspLs7GxWrlxJt27dGux1dHjkcORu4dfnn2/5ce3OUSqE7du3j65duzJw4MAGTfb+0C6dUNCmE1y5CBdLIfbms/5VjLH68Dve3WhVU0r5Jj09nW++8XMmuwamLfxQkNDFWtY1v21FOVz+Xq/BV0r5RRN+KLgt3Vqe2lt7OZ3HVikVAE34oSC+PbRKhFO7ai+nl2QqpQKgCT8UiEBqPzi8tvYTt+4WvnbpKKX8oAk/VHQdCBdOw7sDvCf98uPW+Pkx8Y1bN6UiyKlTpxg1ahRpaWmkp6czdOhQDh3ycRKiGkpLS8nJyaFXr15s3ry53o+bP38+ly5dCui1faEJP1T8vX3lTckOWP2M56R/Ti/JVCoQxhhGjBhBbm4uR48eZd++fcyaNYvTp08H9Lzr16+ne/fu7Ny5k3vvvbdej7l27VqjJ3y9LDNUtLsLfnMQfncHFP+nNcxrr19D8xbXy5Qfg5SewaqhUrfU3G1zOVB24JY+Z/eE7rzQ7wWv9xcVFREVFcWECROq9mVlZWGMYerUqaxZswYR4aWXXmLkyJFs2LCBl19+meTkZFwuFw899BCZmZksWLCAiooKCgsLOX/+PM8//3zVEMlbtmyhsLCQWbNmYYxh2LBhzJ07F7AGb3vuuef44osvGDZsGCUlJeTl5ZGUlERRUdEtfS880RZ+KIlrB/e9Yq3/8Tfw9t3XW/qV1+DcCe2/VyoAe/fupXfv3jft//jjj3G5XOzatYt169YxdepUTp48CcCuXbtYsGABe/bsYdmyZRw6dIht27Yxbtw4Fi5cSFZWFq+++iojR47E5XJRXl7OCy+8wJdffonL5cLpdFJYWAjAxYsXueuuu9i6dSvTp0+nffv2FBUVNUqyB23hh56fPwMntsHBP1pz3b7SGqadhtIDUHlFu3RUxKitJd7YvvrqKx599FEcDgfJycn0798fp9NJfHw8ffv2JSUlBYC0tDTy8/MByMzM9JionU4nubm5tG3bFoDRo0ezadMmHnzwQRwOBw8//HDjBVaDtvBD0UOLYNyX17d3LIXF/a11ncdWKb9lZGRQXFx80/7axhRzz44F0KxZs6rtZs2acfXqVZ+eKyYmJqiDqWnCD0Ut4iC1Nzxn92+umXr9vs79g1MnpSLAgAEDuHz5Mu+++27VPqfTSZs2bSgoKODatWuUlpayadMm+vXr59dr5OTksHHjRs6cOcO1a9f44IMP6N/f899tXFwc58+f9+t1/KEJP5TFp8CQede3H1gAzaODVx+lwpyIsGrVKtauXUtaWhoZGRnMmDGDxx57jB49etCzZ08GDBjAvHnzaNeunV+vkZKSwuzZs8nLy6Nnz55kZ2czfPhwj2XHjx/PkCFDyMvLCySsegtoeGQRSQAKgE7AMeBXxphyL2Xjgf3AKmPMpLqeu0kNj1wbY6whkePbw9A3rB9pKRWmmsrwyI2lsYdHfhFYb4zpBqy3t715DdgY4Os1PSLw6H/BsH/TZK+UCkigCX84sMReXwI86KmQiPQGkoE/Bfh6Siml/BRowk82xpwEsJc3DeYuIs2A3wFTa97noex4EdkuIttLS0sDrJpSKhSF6ix74caf97HO6/BFZB3g6ezFtHq+xkTgf4wxJ6SOLgljzGJgMVh9+PV8fqVUmIiJieHs2bMkJiZSVz5Q3hljOHv2LDExMT49rs6Eb4y5z9t9InJaRFKMMSdFJAX4zkOxu4F7RWQiEAtEi8gFY0xt/f1KqQiUmprKt99+i36DD1xMTAypqak+PSbQX9p+CowF5tjLT2oWMMaMdq+LyONAH032SjVNUVFRdO7cOdjVaLIC7cOfAwwSkcPAIHsbEekjIn8ItHJKKaVunYCuw29Ieh2+Ukr5riGvw1dKKRUmQraFLyKlwPEau5OAM0GoTkOKtJgiLR6IvJgiLR6IvJgCiaejMaatpztCNuF7IiLbvX1VCVeRFlOkxQORF1OkxQORF1NDxaNdOkop1URowldKqSYi3BL+4mBXoAFEWkyRFg9EXkyRFg9EXkwNEk9Y9eErpZTyX7i18JVSSvlJE75SSjURYZPwReR+ETkoIkdEJGzG4hGRYyKyR0RcIrLd3pcgImtF5LC9bGPvFxF5045xt4hkB7f2FhF5T0S+E5G91fb5HIOIjLXLHxaRscGIxa6Hp3hmiMhf7ePkEpGh1e77rR3PQREZXG1/SHwmRaSDiBSJyH4R+bOIPG3vD+dj5C2msDxOIhIjIttEZJcdzyv2/s4istV+vwtEJNre38LePmLf36nac3mMs16MMSF/AxzAUaALEA3sAtKDXa961v0YkFRj3zzgRXv9RWCuvT4UWAMI8DNga7Drb9frF0A2sNffGIAE4Bt72cZebxNC8cwA/sVD2XT789YC6Gx/Dh2h9JkEUoBsez0OOGTXO5yPkbeYwvI42e91rL0eBWy13/sPgVH2/t8D/2SvTwR+b6+PAgpqi7O+9QiXFn4/4Igx5htjzI/ACqzZtsKVt5nChgNLjeX/gNZiDTsdVMaYTUBZjd2+xjAYWGuMKTPWvMdrgfsbvvY38xKPN8OBFcaYy8aYvwBHsD6PIfOZNMacNMbssNfPY80d/VPC+xh5i8mbkD5O9nt9wd6Msm8GGAB8ZO+veYzcx+4jYKCICN7jrJdwSfg/BU5U2/6W2g9+KDHAn0SkWETG2/u8zRQWTnH6GkM4xDbJ7uJ4z939QZjFY3/174XVgoyIY1QjJgjT4yQiDhFxYc0bshardX7OGHPVQ92q6m3f/zcgkQDjCZeE72lqnHC5nvQfjDHZwBDgn0XkF7WUDec43bzFEOqxvQOkAVnASaxpOSGM4hGRWOC/gWeMMd/XVtTDvnCJKWyPkzHmmjEmC0jFapXf6amYvWyQeMIl4X8LdKi2nQqUBKkuPjHGlNjL74BVWAf6tLurRm6cKSyc4vQ1hpCOzRhz2v6DrATe5frX5LCIR0SisBLjcmPMx/busD5GnmIK9+MEYIw5B2zA6sNvLSLuiaiq162q3vb9f4fVDRlQPOGS8J1AN/uMdjTWSYxPg1ynOonIT0Qkzr0O5AN7uT5TGNw4U9inwBj7KoqfAX9zfyUPQb7G8AWQLyJt7K/h+fa+kFDjXMkIrOMEVjyj7KsmOgPdgG2E0GfS7tv9D2C/Mebfq90VtsfIW0zhepxEpK2ItLbXWwL3YZ2XKAJ+aRereYzcx+6XwJfGOmvrLc76aeyz1f7esK4sOITV7zUt2PWpZ527YJ1R3wX82V1vrL649cBhe5lgrp/Jf8uOcQ/WdJChEMcHWF+fr2C1MJ70JwbgCayTTEeAfwyxeJbZ9d1t/1GlVCs/zY7nIDAk1D6TwM+xvtbvBlz2bWiYHyNvMYXlcQJ6ADvteu8Fptv7u2Al7CPASqCFvT/G3j5i39+lrjjrc9OhFZRSqokIly4dpZRSAdKEr5RSTYQmfKWUaiI04SulVBOhCV8ppZoITfhKKdVEaMJXSqkm4v8BgfTyzCGi+MUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "score_df = pd.DataFrame()\n",
    "score_df['efficiency'] = score_efficiency\n",
    "score_df['safe'] = score_safe\n",
    "score_df['comfort'] = score_comfort\n",
    "\n",
    "# rolling_window = 20\n",
    "# conduct rolling window\n",
    "%matplotlib inline\n",
    "plt.plot(score_df.efficiency.rolling(window=rolling_window).mean(), label = 'Efficiency')\n",
    "plt.plot(score_df.safe.rolling(window=rolling_window).mean(), label = 'Safety')\n",
    "plt.plot(score_df.comfort.rolling(window=rolling_window).mean(), label = 'Comfort')\n",
    "plt.legend()"
   ]
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}