env_Gamma_get_ACF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sql module is not an IPython extension.\n",
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "from Vissim_env_class import environment\n",
    "from MasterDQN_Agent import MasterDQN_Agent\n",
    "from MasterMO_Agent import MasterMO_Agent\n",
    "# Network Specific Libraries\n",
    "from Balance_Functions import balance_dictionary\n",
    "\n",
    "# General Libraries\n",
    "import numpy as np \n",
    "import pickle\n",
    "import matplotlib.pylab as plt\n",
    "import os\n",
    "import shutil\n",
    "import csv\n",
    "import pandas as pd\n",
    "import json\n",
    "import sqlite3\n",
    "\n",
    "%load_ext sql\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name  = 'Single_Cross_Triple'\n",
    "vissim_working_directory = 'C:\\\\Users\\\\acabrejasegea\\\\Desktop\\\\15_Timescales_utc\\\\gamma_code'\n",
    "sim_length = 3601\n",
    "random_seed = 10\n",
    "timesteps_per_second = 1\n",
    "\n",
    "Session_ID = \"Single_Cross_Triple8_MO\"\n",
    "\n",
    "# all controller actions\n",
    "Single_Cross_Triple_dictionary8 =\\\n",
    "{\\\n",
    "    # Controller Number 0 \n",
    "   'junctions' : {0 : {'default_actions' :  {    0 : [1,1,1,0,0,0,0,0,0,0,0,0],\n",
    "                                             1 : [0,0,0,1,1,1,0,0,0,0,0,0],\n",
    "                                             2 : [0,0,0,0,0,0,1,1,1,0,0,0],\n",
    "                                             3 : [0,0,0,0,0,0,0,0,0,1,1,1],\n",
    "                                             4 : [1,0,0,0,0,0,1,0,0,0,0,0],\n",
    "                                             5 : [0,0,0,1,0,0,0,0,0,1,0,0],\n",
    "                                             6 : [0,1,1,0,0,0,0,1,1,0,0,0],\n",
    "                                             7 : [0,0,0,0,1,1,0,0,0,0,1,1]},\n",
    "         \n",
    "         'all_actions' :        {            0 : [1,1,1,0,0,0,0,0,0,0,0,0],\n",
    "                                             1 : [0,0,0,1,1,1,0,0,0,0,0,0],\n",
    "                                             2 : [0,0,0,0,0,0,1,1,1,0,0,0],\n",
    "                                             3 : [0,0,0,0,0,0,0,0,0,1,1,1],\n",
    "                                             4 : [1,0,0,0,0,0,1,0,0,0,0,0],\n",
    "                                             5 : [0,0,0,1,0,0,0,0,0,1,0,0],\n",
    "                                             6 : [0,1,1,0,0,0,0,1,1,0,0,0],\n",
    "                                             7 : [0,0,0,0,1,1,0,0,0,0,1,1]},\n",
    " \n",
    "         'link' : [1, 3, 5, 7],\n",
    "         'lane' : ['1-1', '1-2', '1-3', '3-1', '3-2', '3-3', '5-1', '5-2', '5-3', '7-1', '7-2', '7-3'],\n",
    "         \n",
    "         'controled_by_com' : True,\n",
    "         'agent_type' : agent_type,\n",
    "         'green_time' : 6,\n",
    "         'redamber_time' : 0,\n",
    "         'amber_time' : 3, \n",
    "         'red_time' : 0,\n",
    "         'state_size' : [13],\n",
    "         'state_type' : 'QueuesSig',\n",
    "         'reward_type' : 'Queues' ,\n",
    "         'queues_counter_ID' : [1,2,3,4,5,6,7,8,9,10,11,12]             \n",
    "         }\n",
    "    },\n",
    "   'demand' : { 'default' : [400, 400, 400, 400],\n",
    "             \n",
    "             0 : [300,300,300,300],\n",
    "             1 : [600,600,600,600],\n",
    "             2 : [1350,750,1350,750],\n",
    "             3 : [1500,750,1500,750],\n",
    "             4 : [1050,750,1050,750],\n",
    "             5 : [750,1050,750,1050],\n",
    "             6 : [750,1500,750,1500],\n",
    "             7 : [750,1350,750,1350],\n",
    "             8 : [600,600,600,600],\n",
    "             9 : [300,300,300,300]\n",
    "            }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "## DQN Hyperaramenters\n",
    "episodes = 400 \n",
    "copy_weights_frequency = 20 # On a successfull run I copied the weight every 50\n",
    "\n",
    "PER_activated = True\n",
    "memory_size = 1000\n",
    "batch_size = 128\n",
    "\n",
    "gamma = 0.95\n",
    "alpha = 0.005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Exploration Schedule (\"linear\" or \"geometric\")\n",
    "exploration_schedule = \"linear\"\n",
    "epsilon_start = 1\n",
    "epsilon_end   = 0.01\n",
    "\n",
    "def choose_schedule(exploration_schedule, espilon_start, epsilon_end, episodes):\n",
    "    if exploration_schedule == \"linear\":\n",
    "        epsilon_decay = 1.2*(epsilon_end - epsilon_start)/(episodes-1)\n",
    "        epsilon_sequence = [1 + epsilon_decay * entry for entry in range(episodes+1)]\n",
    "        epsilon_sequence = [0.01 if entry < 0.01 else entry for entry in epsilon_sequence]\n",
    "    elif exploration_schedule == \"geometric\":\n",
    "        epsilon_decay = np.power(epsilon_end/epsilon_start, 1./(episodes-1)) # Geometric decay\n",
    "        epsilon_sequence = [epsilon_start * epsilon_decay ** entry for entry in range(episodes+1)]\n",
    "        epsilon_sequence = [0.01 if entry < 0.01 else entry for entry in epsilon_sequence]\n",
    "    elif exploration_schedule == \"entropy\":\n",
    "        pass\n",
    "    else:\n",
    "        print(\"ERROR: Unrecognized choice of exploration schedule.\")\n",
    "        \n",
    "    # Plotting exploration schedule\n",
    "    plt.figure(figsize=(8,4.5))\n",
    "    x_series = np.array(range(1,episodes+1))\n",
    "    y_series = epsilon_sequence[0:episodes]\n",
    "    plt.plot(x_series, y_series, '-b')\n",
    "    plt.xlabel('Episodes')\n",
    "    plt.ylabel('Ratio of random exploration')\n",
    "    plt.title('Exploration schedule')\n",
    "    plt.show()\n",
    "    return(epsilon_sequence)\n",
    "\n",
    "epsilon_sequence = choose_schedule(exploration_schedule, epsilon_start, epsilon_end, episodes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INTERSECTION 0: SETTING UP AGENT\n"
     ]
    }
   ],
   "source": [
    "# Calculate ACF decay\n",
    "Triple_MO_Agent = MasterMO_Agent(model_name, vissim_working_directory, sim_length,\\\n",
    "                                 Single_Cross_Triple_dictionary8, actions_set, random_seed,\\\n",
    "                                 timesteps_per_second, Session_ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working Directory set to: C:\\Users\\acabrejasegea\\Desktop\\15_Timescales_utc\\gamma_code\n",
      "Generating Cache...\n",
      "Cache generated.\n",
      "\n",
      "****************************\n",
      "*   COM Server dispatched  *\n",
      "****************************\n",
      "\n",
      "Attempting to load Model File: Single_Cross_Triple.inpx ...\n",
      "Model File load process successful.\n",
      "Simulation length set to 3601 seconds.\n",
      "Results from Previous Simulations: Deleted. Fresh Start Available.\n",
      "Fetched and containerized Network Object \n",
      "\n",
      "*******************************************************\n",
      "*                                                     *\n",
      "*                COM SETUP COMPLETE                   *\n",
      "*                                                     *\n",
      "*******************************************************\n",
      "\n",
      "Random seed set in simulator. Random Seed = 10\n",
      "Deploying Network Parser...\n",
      "Successful Network Crawl: Identified SignalControllers, Links, Lanes and Vehicle Inputs.\n",
      "\n",
      "Setting Simulation mode to: training\n",
      "Starting Deployments of Signal Control Units...\n",
      "SCUs successfully deployed. Elapsed time 0.13 seconds.\n",
      "\n",
      "start\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 1 is out of bounds for axis 0 with size 1",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-23-52cd6c7f5289>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mTriple_MO_Agent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_data\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[0m",
      "\u001b[1;32m~\\Desktop\\15_Timescales_utc\\gamma_code\\MasterMO_Agent.py\u001b[0m in \u001b[0;36mget_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     60\u001b[0m         \u001b[0mactions\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[0;32m     61\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mstart_state\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\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;32m---> 62\u001b[1;33m             \u001b[0mactions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAgents\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchoose_action\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[0m\n\u001b[0m\u001b[0;32m     63\u001b[0m         \u001b[1;31m# Simulation Loop, Run until end of simulation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Desktop\\15_Timescales_utc\\gamma_code\\MOAgents.py\u001b[0m in \u001b[0;36mchoose_action\u001b[1;34m(self, state)\u001b[0m\n\u001b[0;32m     26\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mqueues_over_time\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     27\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 28\u001b[1;33m         action_values = [state[0]+state[1]+state[2],\n\u001b[0m\u001b[0;32m     29\u001b[0m                          \u001b[0mstate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m5\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     30\u001b[0m                          \u001b[0mstate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m8\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;31mIndexError\u001b[0m: index 1 is out of bounds for axis 0 with size 1"
     ]
    }
   ],
   "source": [
    "Triple_MO_Agent.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Balance_int_MultiDQN_Agents = MasterDQN_Agent(model_name, vissim_working_directory, sim_length, partial_dictionary, actions,\\\n",
    "                gamma, alpha, agent_type, memory_size, PER_activated, batch_size, batches_per_episode, copy_weights_frequency, epsilon_sequence,\\\n",
    "                Random_Seed, timesteps_per_second, Session_ID, verbose = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Balance_int_MultiDQN_Agents.prepopulate_memory()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Balance_int_MultiDQN_Agents.train(episodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Balance_int_MultiDQN_Agents.save(episodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Balance_int_MultiDQN_Agents.demo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "## ACF STUFF\n",
    "import statsmodels.tsa.stattools\n",
    "folder = \"C:\\\\Users\\\\acabrejasegea\\Desktop\\\\15_Timescales_utc\\\\gamma_code\\\\Single_Cross_Triple\\\\\"\n",
    "file = \"as_Q400.pkl\"\n",
    "\n",
    "with open(folder+file, 'rb') as pfile:\n",
    "    q = pickle.load(pfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "q[0] = np.asarray([q[0]])\n",
    "queues = [sum(a[0]) for a in q]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[autoreload of DQNAgents failed: Traceback (most recent call last):\n",
      "  File \"C:\\Users\\acabrejasegea\\AppData\\Local\\Continuum\\anaconda3\\envs\\vissim\\lib\\site-packages\\IPython\\extensions\\autoreload.py\", line 245, in check\n",
      "    superreload(m, reload, self.old_objects)\n",
      "  File \"C:\\Users\\acabrejasegea\\AppData\\Local\\Continuum\\anaconda3\\envs\\vissim\\lib\\site-packages\\IPython\\extensions\\autoreload.py\", line 394, in superreload\n",
      "    module = reload(module)\n",
      "  File \"C:\\Users\\acabrejasegea\\AppData\\Local\\Continuum\\anaconda3\\envs\\vissim\\lib\\imp.py\", line 314, in reload\n",
      "    return importlib.reload(module)\n",
      "  File \"C:\\Users\\acabrejasegea\\AppData\\Local\\Continuum\\anaconda3\\envs\\vissim\\lib\\importlib\\__init__.py\", line 169, in reload\n",
      "    _bootstrap._exec(spec, module)\n",
      "  File \"<frozen importlib._bootstrap>\", line 604, in _exec\n",
      "  File \"<frozen importlib._bootstrap_external>\", line 779, in exec_module\n",
      "  File \"<frozen importlib._bootstrap_external>\", line 916, in get_code\n",
      "  File \"<frozen importlib._bootstrap_external>\", line 846, in source_to_code\n",
      "  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\n",
      "  File \"C:\\Users\\acabrejasegea\\Desktop\\15_Timescales_utc\\gamma_code\\DQNAgents.py\", line 98\n",
      "    self.memory2 = deque(maxlen=)\n",
      "                                ^\n",
      "SyntaxError: invalid syntax\n",
      "]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1120"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(queues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22f8788da60>]"
      ]
     },
     "execution_count": 222,
     "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(queues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACF reaches < 10% after 7 actions.\n",
      "This means γ = 0.72\n",
      "\n",
      "\n",
      "ACF reaches < 1% after 12 actions.\n",
      "This means γ = 0.825\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acabrejasegea\\AppData\\Local\\Continuum\\anaconda3\\envs\\vissim\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:568: FutureWarning: fft=True will become the default in a future version of statsmodels. To suppress this warning, explicitly set fft=False.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "acf_q = statsmodels.tsa.stattools.acf(queues[600:len(queues)], nlags = 3600)\n",
    "\n",
    "lim10 = next(x[0] for x in enumerate(acf_q) if x[1] < 0.1)\n",
    "lim01 = next(x[0] for x in enumerate(acf_q) if x[1] < 0.01)\n",
    "\n",
    "print(\"ACF reaches < 10% after {} actions.\".format(lim10))\n",
    "print(\"This means \\N{greek small letter gamma} = {}\".format(np.round(np.power(0.1, 1/lim10),3)))\n",
    "print(\"\\n\")\n",
    "print(\"ACF reaches < 1% after {} actions.\".format(lim01))\n",
    "print(\"This means \\N{greek small letter gamma} = {}\".format(np.round(np.power(0.1, 1/lim01),3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22f87981160>]"
      ]
     },
     "execution_count": 224,
     "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(acf_q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}