RLbased_FleetManagement.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "RLbased_FleetManagement.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X5epTwJAegOa",
        "outputId": "febfb267-939a-4469-944d-f0f7071782b8"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TcMs4heCD9cm"
      },
      "source": [
        "### Required Packages:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7fQNa1IYfrLh"
      },
      "source": [
        "import sys\n",
        "sys.path.insert(0,'/content/gdrive/MyDrive/Drone_Delivery/')\n",
        "\n",
        "from Coordinates import Polygon_points, IDs, Pr_sub, Pr_region, IDs_dict\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt \n",
        "import math\n",
        "import random\n",
        "from tqdm import tqdm\n",
        "import gym\n",
        "from gym import spaces\n",
        "import copy\n",
        "from shapely.geometry import Polygon, Point\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "\n",
        "from collections import namedtuple\n",
        "\n",
        "import warnings\n",
        "#warnings.simplefilter(\"error\")\n",
        "warnings.simplefilter(\"ignore\", UserWarning)\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0JVOyzQeEBUl"
      },
      "source": [
        "### Parameters of the system:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OpJHDV2zTpGy"
      },
      "source": [
        "Total_area = 36.3\n",
        "\n",
        "Initial_Energy = 100\n",
        "v = 5\n",
        "\n",
        "arriv_rate_avg = np.zeros(4)\n",
        "for d in range(4):\n",
        "  arriv_rate_avg[d] = (Pr_region[d]*55)/Pr_region[0]\n",
        "\n",
        "Central_scheduler_Loc = [6678.899082568796, -4568.807339449534]\n",
        "delta_t = 60\n",
        "depots_Loc = [[3669.724770642196, -3633.027522935774],\n",
        "              [7229.357798165125, -2311.926605504583],\n",
        "              [5834.862385321091, -6788.990825688062],\n",
        "              [9100.917431192645, -5834.862385321091], \n",
        "              [6678.899082568796, -4568.807339449534]]\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "def Dec2Bin(x, L):\n",
        "  x = min(x, 2**L - 1)\n",
        "  out = []\n",
        "  while x > 0:\n",
        "    r = x % 2\n",
        "    out.append(r)\n",
        "    x = x // 2\n",
        "  if len(out) < L:\n",
        "    delta_l = L - len(out)\n",
        "    for _ in range(delta_l):\n",
        "      out.append(0)\n",
        "  out.reverse()\n",
        "  return out \n",
        "\n",
        "\n",
        "\n",
        "def Dec2Onehot(x, L, max_):\n",
        "  out = np.zeros(L)\n",
        "  m = max_ // L\n",
        "  divisor = x // m\n",
        "  if int(divisor) < L:\n",
        "    out[int(divisor)] = 1\n",
        "  else:\n",
        "    out[L-1] = 1\n",
        "  \n",
        "  return list(out) \n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "def Points_division(L, Pr):\n",
        "  out = []\n",
        "  for i in range(len(Pr)):\n",
        "    out.append(int(L * Pr[i]))\n",
        "  #print(out)\n",
        "  return out\n",
        "    \n",
        "\n",
        "def get_random_point_in_polygon(poly):\n",
        "     minx, miny, maxx, maxy = poly.bounds\n",
        "     while True:\n",
        "         p = Point(random.uniform(minx, maxx), random.uniform(miny, maxy))\n",
        "         if poly.contains(p):\n",
        "             return p\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e-0jWgEioafV",
        "outputId": "a32be401-af8c-4fe1-ef52-5bbdbbf0f8ea"
      },
      "source": [
        "arriv_rate_avg"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([55.   , 51.15 , 76.725, 92.125])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GIUMb-03EHfk"
      },
      "source": [
        "### Environment:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LaVBrJkKUsOV"
      },
      "source": [
        "class Drone_Delivery(gym.Env):\n",
        "  def __init__(self,n_depots, n_UAVs, m_0):\n",
        "    self.epsilon = 0.1\n",
        "    self.dual_multiplier = 4\n",
        "    self.n_depots = n_depots\n",
        "    self.time = 0 \n",
        "    self.n_UAVs = n_UAVs\n",
        "    self.UAVs = [UAV(i) for i in range(self.n_UAVs)]\n",
        "    self.depots = [depot(id) for id in range(self.n_depots)]\n",
        "    self.depots.append(depot(-1))\n",
        "    self.T_action = 120\n",
        "    self.n_Q_bits = 20\n",
        "    self.n_UAV_bits = 10\n",
        "    self.state = np.zeros((self.n_depots, self.n_Q_bits + self.n_UAV_bits ))\n",
        "    self.q_thr = [80, 80, 120, 150]\n",
        "    # [80, 80, 120, 150]\n",
        "    #[120, 110, 165, 200]\n",
        "    self.p_arrival = [0.65, 0.65, 0.65, 0.65]\n",
        "    self.actionspace = {0: -5, 1: 0, 2: 5}\n",
        "    self.m_0 = m_0 \n",
        "\n",
        "    #self.action_space = spaces.Discrete(3)\n",
        "    # The observation will be the coordinate of the agent\n",
        "    # this can be described both by Discrete and Box space\n",
        "    #self.observation_space = spaces.Box(low=0, high=1,\n",
        "    #                                    shape=(1,self.n_Q_bits + self.n_UAV_bits), dtype=np.float32)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    #Initialize UAVs (equally) to the depots:\n",
        "    for d in range(self.n_depots):\n",
        "      if d == 0: \n",
        "        l_b = 0\n",
        "      else:\n",
        "        l_b = np.sum(self.m_0[0: d])\n",
        "      for i in range(l_b, np.sum(self.m_0[0:d+1])):\n",
        "        self.UAVs[i].depot = d \n",
        "        self.UAVs[i].status = 0\n",
        "        self.UAVs[i].Loc = self.depots[d].Loc\n",
        "        self.depots[d].UAV_ids.append(self.UAVs[i].id)\n",
        "        self.depots[d].empty_UAVs.append(self.UAVs[i].id)\n",
        "\n",
        "\n",
        "    for i in range(np.sum(self.m_0), self.n_UAVs):\n",
        "      self.UAVs[i].depot = -1\n",
        "      self.UAVs[i].status = 0\n",
        "      self.UAVs[i].Loc = self.depots[-1].Loc\n",
        "      self.depots[-1].UAV_ids.append(self.UAVs[i].id)\n",
        "      self.depots[-1].empty_UAVs.append(self.UAVs[i].id)\n",
        "\n",
        "\n",
        "\n",
        "    for d in range(self.n_depots):\n",
        "      q_len = len(self.depots[d].Q)\n",
        "      uav_len = len(self.depots[d].UAV_ids)\n",
        "      #print(q_len)\n",
        "      #self.state[d] = [q_len, uav_len]\n",
        "      self.state[d] = Dec2Bin(q_len, self.n_Q_bits) + Dec2Bin(uav_len, self.n_UAV_bits)\n",
        "      #self.state[d] = Dec2Onehot(q_len, self.n_Q_bits, 1000) + Dec2Onehot(uav_len, self.n_UAV_bits, 500)\n",
        "      \n",
        "\n",
        "\n",
        "\n",
        "  def reset(self):\n",
        "    self.time = 0\n",
        "    self.p_arrival = [0.65, 0.65, 0.65, 0.65]\n",
        "\n",
        "    for d in range(self.n_depots):\n",
        "      self.depots[d].delivered = 0\n",
        "      self.depots[d].Q = []\n",
        "      self.depots[d].UAV_ids = []\n",
        "      self.depots[d].returning_UAVs = []\n",
        "      self.depots[d].delivering_UAVs = []\n",
        "      self.depots[d].empty_UAVs = []\n",
        "      self.depots[d].status = 1\n",
        "\n",
        "      \n",
        "      \n",
        "      if d ==0:\n",
        "        l_b = 0\n",
        "      else:\n",
        "        l_b = np.sum(self.m_0[0: d])\n",
        "\n",
        "      for i in range(l_b, np.sum(self.m_0[0:d+1])):\n",
        "        self.UAVs[i].depot = d \n",
        "        self.UAVs[i].status = 0\n",
        "        self.UAVs[i].transit_flag = False\n",
        "        self.UAVs[i].Loc = self.depots[d].Loc\n",
        "        self.depots[d].UAV_ids.append(self.UAVs[i].id)\n",
        "        self.depots[d].empty_UAVs.append(self.UAVs[i].id)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    \n",
        "    self.depots[-1].returning_UAVs = []\n",
        "    self.depots[-1].delivering_UAVs = []\n",
        "    self.depots[-1].empty_UAVs = []\n",
        "    self.depots[-1].UAV_ids = []\n",
        "    for i in range(np.sum(self.m_0), self.n_UAVs):\n",
        "      \n",
        "      self.UAVs[i].depot = -1\n",
        "      self.UAVs[i].status = 0\n",
        "      self.UAVs[i].transit_flag = False\n",
        "      self.UAVs[i].Loc = self.depots[-1].Loc\n",
        "      self.depots[-1].UAV_ids.append(self.UAVs[i].id)\n",
        "      self.depots[-1].empty_UAVs.append(self.UAVs[i].id)\n",
        "\n",
        "\n",
        "    \n",
        "    for d in range(self.n_depots):\n",
        "      q_len = len(self.depots[d].Q)\n",
        "      uav_len = len(self.depots[d].UAV_ids)\n",
        "      #self.state[d] = [q_len, uav_len]\n",
        "      self.state[d] = Dec2Bin(q_len, self.n_Q_bits) + Dec2Bin(uav_len, self.n_UAV_bits)\n",
        "      #self.state[d] = Dec2Onehot(q_len, self.n_Q_bits, 1000) + Dec2Onehot(uav_len, self.n_UAV_bits, 500)\n",
        "    \n",
        "    s = copy.deepcopy(self.state)\n",
        "    return s\n",
        "\n",
        "##############################################################\n",
        "\n",
        "\n",
        "  def step(self, action):\n",
        "    N_Q_violate = np.zeros(self.n_depots)\n",
        "    act = []\n",
        "    for dd in range(self.n_depots):\n",
        "      act.append(self.actionspace[action[dd]])\n",
        "      #act.append(action[dd])\n",
        "    \n",
        "    \n",
        "    #act_0 = self.actionspace[action]\n",
        "    #act = [act_0]\n",
        "    #act = [0]\n",
        "    #for _ in range(1, self.n_depots):\n",
        "    #  act.append(np.random.randint(-5,6))\n",
        "      #act.append(0)\n",
        "\n",
        "    #act = np.random.randint(-5,6, size= self.n_depots)\n",
        "    self.central_scheduller_2(act)\n",
        "    #print(act)\n",
        "    reward = np.zeros(self.n_depots)\n",
        "    reward_sub = np.zeros(self.n_depots) \n",
        "    q_len = np.zeros(self.n_depots)\n",
        "    uav_len = np.zeros(self.n_depots)\n",
        "\n",
        "    #if self.time % 300 ==0:\n",
        "    #  self.p_arrival[0] = 0.1\n",
        "    #  self.p_arrival[2] = 0.1\n",
        "    #  self.p_arrival[1] = 0.9\n",
        "    #  self.p_arrival[3] = 0.9\n",
        "    #if self.time % 600 ==0:\n",
        "    #  self.p_arrival[0] = 0.9\n",
        "    #  self.p_arrival[2] = 0.9\n",
        "    #  self.p_arrival[1] = 0.1\n",
        "    #  self.p_arrival[3] = 0.1\n",
        "\n",
        "\n",
        "    for tt in range(self.T_action):\n",
        "      self.time += 1\n",
        "      for d in range(self.n_depots):\n",
        "        arr = np.random.uniform() > self.p_arrival[d]\n",
        "        if arr and tt % 45 == 0:\n",
        "\n",
        "          ar_rate = int(1.2*arriv_rate_avg[d])\n",
        "          self.customer_generation(d, self.time, ar_rate)\n",
        "        \n",
        "        delivered_old = self.depots[d].delivered\n",
        "        self.depots[d].UAV_customer_assignment(self.UAVs, self.time)\n",
        "      \n",
        "\n",
        "\n",
        "        \n",
        "\n",
        "\n",
        "      # for depot 0:\n",
        "      delivered_new = self.depots[0].delivered\n",
        "\n",
        "\n",
        "\n",
        "      self.UAVs_move()\n",
        "\n",
        "      delta_delivery = delivered_new - delivered_old \n",
        "\n",
        "      for d in range(self.n_depots):\n",
        "        q_len[d] = len(self.depots[d].Q)\n",
        "        uav_len[d] = len(self.depots[d].UAV_ids)\n",
        "        #self.state[d] = [q_len[d], uav_len[d]]\n",
        "        self.state[d] = Dec2Bin(q_len[d], self.n_Q_bits) + Dec2Bin(uav_len[d], self.n_UAV_bits)\n",
        "        #self.state[d] = Dec2Onehot(q_len, self.n_Q_bits, 1000) + Dec2Onehot(uav_len, self.n_UAV_bits, 500)\n",
        "        #Q[d].append(q_len[d])\n",
        "\n",
        "        #reward_sub[d] += max(delta_delivery, 0)\n",
        "\n",
        "\n",
        "        if q_len[d] > self.q_thr[d]:\n",
        "          N_Q_violate[d] += 1\n",
        "          reward_sub[d] += self.epsilon * self.dual_multiplier - self.dual_multiplier\n",
        "        elif q_len[d] <= self.q_thr[d]:\n",
        "          reward_sub[d] += self.epsilon * self.dual_multiplier\n",
        "        #else: \n",
        "          #reward_sub[d] += -200000\n",
        "\n",
        "\n",
        "      \n",
        "\n",
        "    for d in range(self.n_depots):\n",
        "      reward[d] = reward_sub[d]  - 1*len(self.depots[d].UAV_ids)\n",
        "      #reward[d] = - len(self.depots[d].UAV_ids)\n",
        "\n",
        "    #reward = reward/100\n",
        "    s = copy.deepcopy(self.state)\n",
        "\n",
        "    \n",
        "    return s, reward, False, N_Q_violate\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "  def central_scheduller_2(self, delta_n):\n",
        "    \"\"\"\n",
        "    delta_n is the incremental action\n",
        "    \"\"\"\n",
        "    N = np.zeros((self.n_depots,), dtype=int)\n",
        "    delta = np.zeros((self.n_depots,), dtype=int)\n",
        "    UnAssigned_UAVs = []\n",
        "    Awaiting_depots = []\n",
        "    for d in range(self.n_depots):\n",
        "      N[d] = len(self.depots[d].UAV_ids) + delta_n[d]\n",
        "      if N[d] < 0:\n",
        "        delta_n[d] = 0\n",
        "    if sum(delta_n) > 0:\n",
        "      if sum(delta_n) <= len(self.depots[-1].UAV_ids):\n",
        "        for _ in range( min(sum(delta_n),len(self.depots[-1].UAV_ids)) ):\n",
        "          u = self.depots[-1].UAV_ids.pop(0)\n",
        "          self.UAVs[u].transit_flag = True\n",
        "          #self.UAVs[u].Loc = [np.random.uniform(1,8000), np.random.uniform(4000,8000)]\n",
        "          self.UAVs[u].Loc = [6678.899082568796, -4568.807339449534]\n",
        "          self.depots[-1].empty_UAVs.remove(u)\n",
        "          UnAssigned_UAVs.append(u)\n",
        "      #else:\n",
        "      #  for d in range(self.n_depots):\n",
        "      #    N[d] = max(len(self.depots[d].UAV_ids) + delta_n[d], 0)\n",
        "      #    N[d] = min(len(self.depots[d].UAV_ids) + delta_n[d],self.n_UAVs)\n",
        "      #  N_tilde = (N*self.n_UAVs)/np.sum(N)\n",
        "        \n",
        "      #  for d in range(self.n_depots):\n",
        "      #    N_tilde[d] = int(N_tilde[d] + 1) if (N_tilde[d] % 1 >=0.5) else int(N_tilde[d])\n",
        "      #    delta[d] = N_tilde[d] - len(self.depots[d].UAV_ids)\n",
        "      #  print(N_tilde)\n",
        "    delta = delta_n\n",
        "    \n",
        "    for d in range(self.n_depots):\n",
        "      #delta[d] = N[d] - len(self.depots[d].UAV_ids)\n",
        "      if delta[d] > 0:\n",
        "        Awaiting_depots.append([d,delta[d]])\n",
        "      elif delta[d] < 0:\n",
        "        l_1 = len(self.depots[d].empty_UAVs)\n",
        "        if l_1 >= abs(delta[d]):\n",
        "          for _ in range(abs(delta[d])):\n",
        "            u = self.depots[d].empty_UAVs.pop(0)\n",
        "            self.UAVs[u].transit_flag = True\n",
        "            self.depots[d].UAV_ids.remove(u)\n",
        "            UnAssigned_UAVs.append(u)\n",
        "            self.UAVs[u].depot = -1\n",
        "        elif l_1 < abs(delta[d]):\n",
        "          for _ in range(max(l_1,0)):\n",
        "            u = self.depots[d].empty_UAVs.pop(0)\n",
        "            self.UAVs[u].transit_flag = True\n",
        "            self.depots[d].UAV_ids.remove(u)\n",
        "            UnAssigned_UAVs.append(u)\n",
        "            self.UAVs[u].depot = -1\n",
        "          l_2 = abs(delta[d]) - l_1\n",
        "          l_r = len(self.depots[d].returning_UAVs)\n",
        "          if l_r >= l_2: \n",
        "            for _ in range(l_2):\n",
        "              u = self.depots[d].returning_UAVs.pop()\n",
        "              self.UAVs[u].transit_flag = True\n",
        "              #print('returning')\n",
        "              #print(u)\n",
        "              #print(self.depots[d].returning_UAVs)\n",
        "              #print(self.depots[d].UAV_ids)\n",
        "              self.depots[d].UAV_ids.remove(u)\n",
        "              UnAssigned_UAVs.append(u)\n",
        "              self.UAVs[u].depot = -1\n",
        "          elif l_r < l_2:\n",
        "            for _ in range(max(l_r,0)):\n",
        "              #print(d)\n",
        "              u = self.depots[d].returning_UAVs.pop()\n",
        "              self.UAVs[u].transit_flag = True\n",
        "              self.depots[d].UAV_ids.remove(u)\n",
        "              UnAssigned_UAVs.append(u)\n",
        "              self.UAVs[u].depot = -1\n",
        "            l_3 = l_2 - l_r\n",
        "            #l_d = len(self.depots[d].delivering_UAVs)\n",
        "            for _ in range(l_3):\n",
        "              #List_d = self.depots[d].delivering_UAVs.copy()\n",
        "              #List_d.reverse() \n",
        "              #u = List_d.pop()\n",
        "              u = self.depots[d].delivering_UAVs.pop(0)\n",
        "              self.UAVs[u].transit_flag = True\n",
        "              #self.depots[d].delivering_UAVs.remove(u)\n",
        "              self.depots[d].UAV_ids.remove(u)\n",
        "              UnAssigned_UAVs.append(u)\n",
        "              self.UAVs[u].depot = -1\n",
        "    #print(\"Originally unassigned {}\".format(len(UnAssigned_UAVs)))\n",
        "    if len(Awaiting_depots) != 0:\n",
        "      for d_ in Awaiting_depots:\n",
        "        depot_id = d_[0]\n",
        "        if len(UnAssigned_UAVs) != 0:\n",
        "          distances_ = [ np.linalg.norm( np.subtract(self.UAVs[u].Loc, self.depots[depot_id].Loc) ) for u in UnAssigned_UAVs]\n",
        "\n",
        "          sorted_UAVs = [x for _,x in sorted(zip(distances_, UnAssigned_UAVs))]\n",
        "          selected_UAVs = sorted_UAVs[0:min(d_[1],len(sorted_UAVs)) ]\n",
        "          for u in selected_UAVs:\n",
        "            self.UAVs[u].depot = depot_id\n",
        "            self.UAVs[u].transit_flag = True\n",
        "            self.depots[depot_id].UAV_ids.append(u)\n",
        "            UnAssigned_UAVs.remove(u)\n",
        "        #self.depots[depot_id].UAV_ids.extend(selected_UAVs)\n",
        "      #UnAssigned_UAVs = []\n",
        "      #Awaiting_depots = []\n",
        "\n",
        "    if len(UnAssigned_UAVs) != 0:\n",
        "      #print(len(UnAssigned_UAVs))\n",
        "      for u in UnAssigned_UAVs:\n",
        "        self.UAVs[u].depot = -1 \n",
        "        self.UAVs[u].transit_flag = True\n",
        "        self.depots[-1].UAV_ids.append(u)\n",
        "\n",
        "\n",
        "    #return Awaiting_depots, UnAssigned_UAVs\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    \n",
        "\n",
        "\n",
        "  \n",
        "\n",
        "  def customer_generation(self, depot_id, arrival_time, n_customers):\n",
        "    \n",
        "    N_customer_each_subzone = Points_division(n_customers, Pr_sub[depot_id])\n",
        "    for i in range(5):\n",
        "      id = IDs_dict[depot_id][i]\n",
        "      poly = Polygon(Polygon_points[id])\n",
        "      L = N_customer_each_subzone[i]\n",
        "      if L > 0:\n",
        "        LL = np.copy(L)\n",
        "        while LL > 0 :\n",
        "          destination = get_random_point_in_polygon(poly)\n",
        "          self.depots[depot_id].Q.append(Customer(depot_id, arrival_time, destination))\n",
        "          LL -= 1\n",
        "      \n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "  def UAVs_move(self):\n",
        "\n",
        "    #for d in self.depots:\n",
        "    #  d.UAV_customer_assignment(self.UAVs)\n",
        "\n",
        "    for u in self.UAVs:\n",
        "\n",
        "      if u.status == 2 and u.transit_flag == False:\n",
        "        u.movement()\n",
        "\n",
        "        if np.linalg.norm(np.subtract(u.Loc,u.destination)) ==0: \n",
        "          u.status =0\n",
        "          self.depots[u.depot].empty_UAVs.append(u.id)\n",
        "          self.depots[u.depot].returning_UAVs.remove(u.id)\n",
        "          \n",
        "\n",
        "\n",
        "      elif u.status == 2 and u.transit_flag == True:\n",
        "        u.transit_flag = False\n",
        "        u.destination = self.depots[u.depot].Loc\n",
        "        self.depots[u.depot].returning_UAVs.append(u.id)\n",
        "        u.movement()\n",
        "\n",
        "        if np.linalg.norm(np.subtract(u.Loc,u.destination)) ==0: \n",
        "          u.status =0\n",
        "          self.depots[u.depot].empty_UAVs.append(u.id)\n",
        "          self.depots[u.depot].returning_UAVs.remove(u.id)\n",
        "\n",
        "      elif u.status == 1:\n",
        "        u.movement()\n",
        "        if np.linalg.norm(np.subtract(u.Loc,u.destination)) ==0:\n",
        "          u.status =2\n",
        "          u.destination = self.depots[u.depot].Loc\n",
        "          if u.transit_flag == False:\n",
        "            self.depots[u.depot].delivering_UAVs.remove(u.id)\n",
        "            self.depots[u.depot].returning_UAVs.append(u.id)\n",
        "\n",
        "          #elif u.transit_flag == True:\n",
        "          #  self.depots[u.depot].returning_UAVs.append(u.id)\n",
        "          #  u.transit_flag = False\n",
        "          #if u.transit_flag == False:\n",
        "          #  self.depots[u.depot].returning_UAVs.append(u.id)\n",
        "\n",
        "          \n",
        "      \n",
        "      elif u.status == 0 and u.transit_flag == True:\n",
        "        u.status = 2\n",
        "        self.depots[u.depot].returning_UAVs.append(u.id)\n",
        "        u.transit_flag = False\n",
        "        u.destination = self.depots[u.depot].Loc\n",
        "        u.movement()\n",
        "        if np.linalg.norm(np.subtract(u.Loc,u.destination)) ==0: \n",
        "          u.status =0\n",
        "          self.depots[u.depot].returning_UAVs.remove(u.id)\n",
        "          self.depots[u.depot].empty_UAVs.append(u.id)\n",
        "\n",
        "\n",
        "\n",
        "        \n",
        "\n",
        "\n",
        "########################\n",
        "class UAV(object):\n",
        "  def __init__(self, id):\n",
        "    self.id = id\n",
        "    \n",
        "    self.depot = -1\n",
        "    self.status = 0 # 0: idle, 1: towards delivery, 2: back from delivery\n",
        "    self.energy = Initial_Energy\n",
        "    self.Loc = Central_scheduler_Loc\n",
        "    self.destination = []\n",
        "    self.transit_flag = False\n",
        "\n",
        "\n",
        "  def movement(self):\n",
        "    normal_direction = np.subtract(self.destination, self.Loc)/np.linalg.norm(np.subtract(self.destination, self.Loc))\n",
        "    V = v*normal_direction\n",
        "    if np.linalg.norm(np.subtract(self.destination, self.Loc)) <= v*delta_t:\n",
        "      self.Loc = self.destination\n",
        "    else: \n",
        "      self.Loc += V * delta_t \n",
        "    \n",
        "    self.energy -= 1\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "##########################\n",
        "class depot(object):\n",
        "  def __init__(self, depot_id):\n",
        "    self.depot_id = depot_id\n",
        "    self.Loc = depots_Loc[self.depot_id]\n",
        "    self.UAV_ids = [] #Total UAVs of the depot. UAV_ids \n",
        "    self.Q = []\n",
        "    self.empty_UAVs = []\n",
        "    self.returning_UAVs = []\n",
        "    self.delivering_UAVs = []\n",
        "    self.delivered = 0\n",
        "    self.served_customers = []\n",
        "    self.status = 1\n",
        "\n",
        "\n",
        "  def UAV_customer_assignment(self, UAVs, current_time):\n",
        "    l_e = len(self.empty_UAVs)\n",
        "    l_q = len(self.Q)\n",
        "    self.delivered += min(l_e,l_q)\n",
        "    for _ in range(min(l_e,l_q)):\n",
        "      c = self.Q.pop(0)\n",
        "      c.departure_time = current_time\n",
        "      self.served_customers.append(c)\n",
        "      uav_id = self.empty_UAVs.pop(0)\n",
        "      self.delivering_UAVs.append(uav_id)\n",
        "      UAVs[uav_id].destination = c.address\n",
        "      UAVs[uav_id].status = 1\n",
        "\n",
        "\n",
        "\n",
        "class Customer(object):\n",
        "  def __init__(self, depot_id, arrival_time, destination):\n",
        "    self.depot = depot_id\n",
        "    self.address = destination\n",
        "    self.delivered = False\n",
        "    self.arrival_time = arrival_time\n",
        "    self.departure_time = arrival_time\n",
        "    self.deliver_time = 0\n",
        "    self.customer_id = []"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hDwNY63Txblq"
      },
      "source": [
        "# Parameters of the Neural Network:\n",
        "\n",
        "### We use DDQN for our learning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6nQJwPFbbPlO"
      },
      "source": [
        "Transition = namedtuple('Transition',\n",
        "                        ('state', 'action', 'next_state', 'reward', 'done'))\n",
        "\n",
        "TARGET_UPDATE = 5\n",
        "num_episodes = 120\n",
        "hidden = 256\n",
        "gamma = 0.99\n",
        "replay_buffer_size = 1000000\n",
        "batch_size = 25\n",
        "eps_stop = 0.05\n",
        "epsilon = 0.5\n",
        "eps = 0.5\n",
        "Start_epsilon_decaying=0\n",
        "End_epsilon_decaying=num_episodes//2\n",
        "epsilon_decaying= epsilon/(End_epsilon_decaying-Start_epsilon_decaying)\n",
        "\n",
        "\n",
        "class ReplayMemory(object):\n",
        "    def __init__(self, capacity):\n",
        "        self.capacity = capacity\n",
        "        self.memory = []\n",
        "        self.position = 0\n",
        "\n",
        "    def push(self, *args):\n",
        "        \"\"\"Saves a transition.\"\"\"\n",
        "        if len(self.memory) < self.capacity:\n",
        "            self.memory.append(None)\n",
        "        self.memory[self.position] = Transition(*args)\n",
        "        self.position = (self.position + 1) % self.capacity\n",
        "\n",
        "    def sample(self, batch_size):\n",
        "        return random.sample(self.memory, batch_size)\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.memory)\n",
        "\n",
        "\n",
        "class DDQN(nn.Module):\n",
        "    def __init__(self, state_space_dim, action_space_dim, hidden):\n",
        "        super(DDQN, self).__init__()\n",
        "        #self.hidden = hidden\n",
        "        self.fc1 = nn.Linear(state_space_dim, 32)\n",
        "        self.fc2 = nn.Linear(32,32)\n",
        "        self.fc3 = nn.Linear(32, action_space_dim)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.relu(self.fc1(x))\n",
        "        #x = F.dropout(x, p=0.3)\n",
        "        #x = F.relu(x)\n",
        "        x = F.relu(self.fc2(x))\n",
        "        #x = self.fc2(x)\n",
        "        #x = F.relu(x)\n",
        "        x = self.fc3(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "class DDQNAgent(object):\n",
        "    def __init__(self, state_space, n_actions, replay_buffer_size,\n",
        "                 batch_size, hidden_size, gamma):\n",
        "        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "        self.n_actions = n_actions\n",
        "        self.state_space_dim = state_space\n",
        "        self.policy_net = DDQN(state_space, n_actions, hidden_size).to(self.device)\n",
        "        self.target_net = DDQN(state_space, n_actions, hidden_size).to(self.device)\n",
        "        self.target_net.load_state_dict(self.policy_net.state_dict())\n",
        "        self.target_net.eval()\n",
        "\n",
        "        self.optimizer = optim.Adam(self.policy_net.parameters(), lr=1e-3)\n",
        "        self.memory = ReplayMemory(replay_buffer_size)\n",
        "        self.batch_size = batch_size\n",
        "        self.gamma = gamma\n",
        "\n",
        "    def update_network(self, updates=1):\n",
        "        for _ in range(updates):\n",
        "            self._do_network_update()\n",
        "\n",
        "    def _do_network_update(self):\n",
        "        if len(self.memory) < self.batch_size:\n",
        "            return\n",
        "        transitions = self.memory.sample(self.batch_size)\n",
        "        batch = Transition(*zip(*transitions))\n",
        "\n",
        "        non_final_mask = 1 - torch.tensor(batch.done, dtype=torch.uint8)\n",
        "        non_final_next_states = [s for nonfinal, s in zip(non_final_mask,\n",
        "                                                          batch.next_state) if nonfinal > 0]\n",
        "        non_final_next_states = torch.stack(non_final_next_states).to(self.device)\n",
        "        state_batch = torch.stack(batch.state).to(self.device)\n",
        "        action_batch = torch.cat(batch.action).to(self.device)\n",
        "        reward_batch = torch.cat(batch.reward).to(self.device)\n",
        "\n",
        "        self.optimizer.zero_grad()\n",
        "        state_action_values = self.policy_net(state_batch).gather(1, action_batch)\n",
        "\n",
        "        next_state_values = torch.zeros(self.batch_size).to(self.device)\n",
        "        next_state_values[non_final_mask] = self.target_net(non_final_next_states).max(1)[0].detach()\n",
        "        expected_state_action_values = reward_batch + self.gamma * next_state_values\n",
        "\n",
        "        # Compute Huber loss\n",
        "        loss = F.smooth_l1_loss(state_action_values.squeeze(),\n",
        "                                expected_state_action_values)\n",
        "\n",
        "        # Optimize the model\n",
        "        loss.backward()\n",
        "        for param in self.policy_net.parameters():\n",
        "            param.grad.data.clamp_(-1e-1, 1e-1)\n",
        "        self.optimizer.step()\n",
        "\n",
        "    def get_action(self, state, epsilon):\n",
        "        sample = random.random()\n",
        "        if sample > epsilon:\n",
        "            with torch.no_grad():\n",
        "                state = torch.from_numpy(state).float().to(self.device)\n",
        "                q_values = self.policy_net(state).to(self.device)\n",
        "                return torch.argmax(q_values).item()\n",
        "        else:\n",
        "            return random.randrange(self.n_actions)\n",
        "\n",
        "    def update_target_network(self):\n",
        "        self.target_net.load_state_dict(self.policy_net.state_dict())\n",
        "\n",
        "    def store_transition(self, state, action, next_state, reward, done):\n",
        "        action = torch.Tensor([[action]]).long()\n",
        "        reward = torch.tensor([reward], dtype=torch.float32)\n",
        "        next_state = torch.from_numpy(next_state).float()\n",
        "        state = torch.from_numpy(state).float()\n",
        "        self.memory.push(state, action, next_state, reward, done)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wEd0z_g0V89_"
      },
      "source": [
        "n_actions = 3\n",
        "state_space_dim = 30\n",
        "agent = DDQNAgent(state_space_dim, n_actions, replay_buffer_size, batch_size,\n",
        "                  hidden, gamma)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H5UPrQEEbm75"
      },
      "source": [
        "env = Drone_Delivery(1,4000)\n",
        "env.reset()\n",
        "random.seed(8)\n",
        "np.random.seed(8)\n",
        "n_actions = 3\n",
        "state_space_dim = 30\n",
        "agent = DDQNAgent(state_space_dim, n_actions, replay_buffer_size, batch_size,\n",
        "                  hidden, gamma)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "average_rewards = []\n",
        "#Q_len = [[], [], [], []]\n",
        "#N_violation_per_episode = [[] for _ in range(env.n_depots)]\n",
        "Violation_prob = [[] for _ in range(env.n_depots)]\n",
        "Q_end_episode = []\n",
        "n_UAVs = []\n",
        "for ep in tqdm(range(num_episodes)):\n",
        "\n",
        "    count = 0\n",
        "    state = env.reset()\n",
        "    done = False\n",
        "    eps -= epsilon_decaying\n",
        "    epsilon = max(0.05 , eps)\n",
        "    cum_reward = 0\n",
        "    max_episode = 1000\n",
        "    step_counter = np.copy(max_episode)\n",
        "    NN_p = np.zeros(env.n_depots) #Number of queue length violation per episode (1000 hours)\n",
        "\n",
        "\n",
        "    test_n_UAVs = []\n",
        "    test_Q_len = []\n",
        "\n",
        "    while step_counter > 0 and len(env.depots[0].Q) < 2000:\n",
        "      count +=1\n",
        "      step_counter -= 1\n",
        "      action = agent.get_action(state, epsilon)\n",
        "      #print(\"state_be=\", state, \"action=\", action)\n",
        "      next_state, reward, done, NN = env.step(action) #NN: number of Q length violation per step (60 min)\n",
        "      cum_reward += reward\n",
        "      #print(\"state_af=\",next_state)\n",
        "      #print(\"------------------------\") \n",
        "      agent.store_transition(state, action, next_state, reward, done)\n",
        "      \n",
        "      agent.update_network()\n",
        "\n",
        "\n",
        "      Q_end_episode.append(len(env.depots[0].Q))\n",
        "      for d in range(env.n_depots):\n",
        "        NN_p[d] = NN_p[d] + NN[d]\n",
        "        #N_violation_per_episode[d].append(NN[d])\n",
        "\n",
        "      state = next_state\n",
        "      n_UAVs.append(len(env.depots[0].UAV_ids))\n",
        "      test_n_UAVs.append(len(env.depots[0].UAV_ids))\n",
        "      test_Q_len.append(len(env.depots[0].Q))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    if ep % TARGET_UPDATE == 0:\n",
        "      agent.update_target_network()\n",
        "    \n",
        "    \n",
        "    for d in range(env.n_depots):\n",
        "\n",
        "      Violation_prob[d].append(NN_p/(count*env.T_action))\n",
        "\n",
        "    \n",
        "    print(\"reward: {}, prob: {}\".format(cum_reward/count, NN_p/(count*env.T_action)))\n",
        "\n",
        "    average_rewards.append(cum_reward/count)\n",
        "\n",
        "    plt.subplot(1,2,1)\n",
        "    plt.plot(range(len(test_n_UAVs)), test_n_UAVs, label ='number of UAVs', color = 'b')\n",
        "    plt.subplot(1,2,2)\n",
        "    plt.plot(range(len(test_Q_len)), test_Q_len, label ='length of Queue', color = 'r')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "    \n",
        "#env.close()\n",
        "plt.plot(range(num_episodes),average_rewards,label='cumulative rewards', color = 'b')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "plt.plot(range(num_episodes),Violation_prob[0],label='violation prob', color = 'r')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "#plt.plot(range(len(Q_len)), Q_len, color = 'b')\n",
        "#plt.show()\n",
        "#print('Complete')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rv05OdsFFki0"
      },
      "source": [
        "Save the results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UciY_4EJETcF"
      },
      "source": [
        "np.save('Reward_d0_Feb12_seed_08', average_rewards)\n",
        "np.save('Pr_d0_Feb12_seed_08',Violation_prob[0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tUJHzI_ouTki"
      },
      "source": [
        "plt.plot(range(len(average_rewards)),average_rewards,label='average rewards', color = 'b')\n",
        "\n",
        "plt.show()\n",
        "W = 10\n",
        "windowed_reward = np.zeros(len(average_rewards) - W)\n",
        "for j in range(len(windowed_reward)):\n",
        "  windowed_reward[j] = np.mean(average_rewards[j:j+W]) \n",
        "\n",
        "plt.plot(range(len(windowed_reward)),windowed_reward,label='windowed cumulative rewards', color = 'b')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "plt.plot(range(len(Violation_prob[0])), Violation_prob[0], color = 'b')\n",
        "plt.plot(range(len(Violation_prob[0])), 0.1*np.ones(len(Violation_prob[0])), color ='r')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W46TuTs7OExv"
      },
      "source": [
        "# Test the code:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i56yR8rSeOvd"
      },
      "source": [
        "### DDQN based"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "vReYK2haOH5p",
        "outputId": "a439d17d-9e2b-4e35-d73e-1eddabf5eb8f"
      },
      "source": [
        "m_0 = [15,15,15,15]\n",
        "env = Drone_Delivery(4, int(sum(m_0)), m_0)\n",
        "env.reset()\n",
        "random.seed(4)\n",
        "n_UAVs_center = []\n",
        "cumulative_rewards = []\n",
        "num_episodes = 1\n",
        "#Q_len = [[], [], [], []]\n",
        "#N_violation_per_episode = [[] for _ in range(env.n_depots)]\n",
        "Violation_prob = [[] for _ in range(env.n_depots)]\n",
        "Q_end_episode = [[] for _ in range(env.n_depots)]\n",
        "n_UAVs = [[] for _ in range(env.n_depots)]\n",
        "Violation_W = [[] for _ in range(env.n_depots)]\n",
        "for ep in tqdm(range(num_episodes)):\n",
        "\n",
        "    count = 0\n",
        "    state = env.reset()\n",
        "    done = False\n",
        "    #cum_reward = 0\n",
        "    max_episode = 1000\n",
        "    step_counter = np.copy(max_episode)\n",
        "    NN_p = np.zeros(env.n_depots)\n",
        "\n",
        "\n",
        "    test_n_UAVs = [[] for _ in range(env.n_depots)]\n",
        "    test_Q_len = [[] for _ in range(env.n_depots)]\n",
        "\n",
        "    while step_counter > 0: # and len(env.depots[0].Q) < 1500:\n",
        "      count +=1\n",
        "      step_counter -= 1\n",
        "      action = []\n",
        "      for d in range(env.n_depots):\n",
        "        action.append(agent.get_action(state[d], 0))\n",
        "      #print(action)\n",
        "      next_state, reward, done, NN = env.step(action)\n",
        "      #cum_reward += reward\n",
        "\n",
        "\n",
        "      state = next_state\n",
        "\n",
        "      for d in range(env.n_depots):\n",
        "        Q_end_episode[d].append(len(env.depots[d].Q))\n",
        "        NN_p[d] = NN_p[d] + NN[d]\n",
        "        Violation_W[d].append(NN[d])\n",
        "        #N_violation_per_episode[d].append(NN[d])\n",
        "\n",
        "      \n",
        "        n_UAVs[d].append(len(env.depots[d].UAV_ids))\n",
        "        test_n_UAVs[d].append(len(env.depots[d].UAV_ids))\n",
        "        test_Q_len[d].append(len(env.depots[d].Q))\n",
        "      n_UAVs_center.append(len(env.depots[-1].UAV_ids))\n",
        "    for d in range(env.n_depots):\n",
        "\n",
        "      Violation_prob[d].append(NN_p[d]/(count*env.T_action))\n",
        "\n",
        "    \n",
        "    \n",
        "    \n",
        "\n",
        "      plt.subplot(4,2,d*2 + 1)\n",
        "      plt.plot(range(len(test_n_UAVs[d])), test_n_UAVs[d])#, label ='number of UAVs', color = 'b')\n",
        "      plt.subplot(4,2,d*2 + 2)\n",
        "      plt.plot(range(len(test_Q_len[d])), test_Q_len[d]) #, label ='length of Queue', color = 'r')\n",
        "      #plt.legend()\n",
        "    plt.show()\n",
        "    print(\"prob: {}\".format(NN_p/(count*env.T_action)))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "100%|██████████| 1/1 [03:36<00:00, 216.07s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "prob: [0.03275    0.0246     0.09064167 0.13639167]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IsiRMVhRdKkZ"
      },
      "source": [
        "### DDQN Statistics: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rVF6EgEGdJ4w",
        "outputId": "e2a6b8a1-f2d2-423a-c2d3-e379b5b3c981"
      },
      "source": [
        "print(\"prob: {} \\n\".format(NN_p/(count*env.T_action)))\n",
        "print(\"Avg queue length: \", np.mean([np.mean(test_Q_len[d]) for d in range(4)]))\n",
        "print(\"standard deviation of queue length: \\n\", [np.std(test_Q_len[d]) for d in range(4)])\n",
        "print(\"Avg number of UAVs: \\n \", np.mean([np.mean(test_n_UAVs[d]) for d in range(4)]))\n",
        "\n",
        "# Delay Analysis:\n",
        "Serving_delay = [[] for _ in range(env.n_depots)]\n",
        "Delivery_delay = [[] for _ in range(env.n_depots)]\n",
        "UAVs_Service_time = [[] for _ in range(env.n_depots)]\n",
        "for d in range(env.n_depots): \n",
        "  for c in env.depots[d].served_customers:\n",
        "    Serving_delay[d].append((c.departure_time - c.arrival_time))\n",
        "    distance_ = np.subtract(env.depots[c.depot].Loc, c.address)\n",
        "    Deliv_time = c.departure_time - c.arrival_time + (np.linalg.norm(distance_))/(300)\n",
        "    Delivery_delay[d].append(Deliv_time)\n",
        "    UAVs_Service_time[d].append((np.linalg.norm(distance_))/(150))\n",
        "\n",
        "\n",
        "print(\"mean waiting time in queue \\n\",[np.mean(Serving_delay[d]) for d in range(4)])\n",
        "print(\"mean waiting time in queue \\n\",np.mean([np.mean(Serving_delay[d]) for d in range(4)]))\n",
        "print(\"mean Delivery time (time in queue + delivery) \\n \", [np.mean(Delivery_delay[d]) for d in range(4)])\n",
        "\n",
        "\n",
        "print(\"standard deviation of the waiting time \\n\",[np.std(Serving_delay[d]) for d in range(4)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "prob: [0.03275    0.0246     0.09064167 0.13639167] \n",
            "\n",
            "Avg queue length:  29.395\n",
            "standard deviation of queue length: \n",
            " [20.291030037925623, 19.002339619110064, 44.31825172544603, 66.96725796238039]\n",
            "Avg number of UAVs: \n",
            "  12.60375\n",
            "mean waiting time in queue \n",
            " [37.30337186897881, 35.76551794662031, 55.45328684132029, 71.28753809478069]\n",
            "mean waiting time in queue \n",
            " 49.95242868792502\n",
            "mean Delivery time (time in queue + delivery) \n",
            "  [42.1213011219098, 40.94472011260965, 60.3549228905666, 76.49376848351626]\n",
            "standard deviation of the waiting time \n",
            " [23.284914700287633, 24.29076880822363, 33.534926619000764, 45.81394005688499]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wI8wVPN6eYld"
      },
      "source": [
        "### Fixed Assignment (Proportional to the density):\n",
        "\n",
        "The number of UAVs each region has is proportional to its density ratio. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "2zV9cGSlAZvR",
        "outputId": "ffcd754e-2631-440a-ed01-98e58a71a867"
      },
      "source": [
        "m_0 = [12,12,17,19]\n",
        "env = Drone_Delivery(4, int(sum(m_0)), m_0)\n",
        "env.reset()\n",
        "random.seed(4)\n",
        "n_UAVs_center = []\n",
        "cumulative_rewards = []\n",
        "num_episodes = 1\n",
        "#Q_len = [[], [], [], []]\n",
        "#N_violation_per_episode = [[] for _ in range(env.n_depots)]\n",
        "Violation_prob = [[] for _ in range(env.n_depots)]\n",
        "Q_end_episode = [[] for _ in range(env.n_depots)]\n",
        "n_UAVs = [[] for _ in range(env.n_depots)]\n",
        "Violation_W = [[] for _ in range(env.n_depots)]\n",
        "for ep in tqdm(range(num_episodes)):\n",
        "\n",
        "    count = 0\n",
        "    state = env.reset()\n",
        "    done = False\n",
        "    #cum_reward = 0\n",
        "    max_episode = 1000\n",
        "    step_counter = np.copy(max_episode)\n",
        "    NN_p = np.zeros(env.n_depots)\n",
        "\n",
        "\n",
        "    test_n_UAVs = [[] for _ in range(env.n_depots)]\n",
        "    test_Q_len = [[] for _ in range(env.n_depots)]\n",
        "\n",
        "    while step_counter > 0: # and len(env.depots[0].Q) < 1500:\n",
        "      count +=1\n",
        "      step_counter -= 1\n",
        "      action = [1,1,1,1]\n",
        "      #action = [0,0,0,0]\n",
        "      next_state, reward, done, NN = env.step(action)\n",
        "      #cum_reward += reward\n",
        "\n",
        "\n",
        "      state = next_state\n",
        "\n",
        "      for d in range(env.n_depots):\n",
        "        Q_end_episode[d].append(len(env.depots[d].Q))\n",
        "        NN_p[d] = NN_p[d] + NN[d]\n",
        "        Violation_W[d].append(NN[d])\n",
        "        #N_violation_per_episode[d].append(NN[d])\n",
        "\n",
        "      \n",
        "        n_UAVs[d].append(len(env.depots[d].UAV_ids))\n",
        "        test_n_UAVs[d].append(len(env.depots[d].UAV_ids))\n",
        "        test_Q_len[d].append(len(env.depots[d].Q))\n",
        "      n_UAVs_center.append(len(env.depots[-1].UAV_ids))\n",
        "    for d in range(env.n_depots):\n",
        "\n",
        "      Violation_prob[d].append(NN_p[d]/(count*env.T_action))\n",
        "\n",
        "    \n",
        "    \n",
        "    \n",
        "\n",
        "      plt.subplot(4,2,d*2 + 1)\n",
        "      plt.plot(range(len(test_n_UAVs[d])), test_n_UAVs[d])#, label ='number of UAVs', color = 'b')\n",
        "      plt.subplot(4,2,d*2 + 2)\n",
        "      plt.plot(range(len(test_Q_len[d])), test_Q_len[d]) #, label ='length of Queue', color = 'r')\n",
        "      #plt.legend()\n",
        "    plt.show()\n",
        "    print(\"prob: {}\".format(NN_p/(count*env.T_action)))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "100%|██████████| 1/1 [03:31<00:00, 211.93s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "prob: [0.02856667 0.01103333 0.02125    0.05018333]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V1bq5SsWeTI4"
      },
      "source": [
        "### - Fixed statistics:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "udw90_pWeWP-",
        "outputId": "ac4d43d1-0bb1-46c0-be67-1336b7ba3333"
      },
      "source": [
        "print(\"prob: {} \\n\".format(NN_p/(count*env.T_action)))\n",
        "print(\"Avg queue length: \", np.mean([np.mean(test_Q_len[d]) for d in range(4)]))\n",
        "print(\"standard deviation of queue length: \\n\", [np.std(test_Q_len[d]) for d in range(4)])\n",
        "print(\"Avg number of UAVs: \\n \", np.mean([np.mean(test_n_UAVs[d]) for d in range(4)]))\n",
        "# Delay Analysis:\n",
        "Serving_delay = [[] for _ in range(env.n_depots)]\n",
        "Delivery_delay = [[] for _ in range(env.n_depots)]\n",
        "UAVs_Service_time = [[] for _ in range(env.n_depots)]\n",
        "for d in range(env.n_depots): \n",
        "  for c in env.depots[d].served_customers:\n",
        "    Serving_delay[d].append((c.departure_time - c.arrival_time))\n",
        "    distance_ = np.subtract(env.depots[c.depot].Loc, c.address)\n",
        "    Deliv_time = c.departure_time - c.arrival_time + (np.linalg.norm(distance_))/(300)\n",
        "    Delivery_delay[d].append(Deliv_time)\n",
        "    UAVs_Service_time[d].append((np.linalg.norm(distance_))/(150))\n",
        "\n",
        "\n",
        "print(\"mean waiting time in queue \\n\",[np.mean(Serving_delay[d]) for d in range(4)])\n",
        "print(\"mean waiting time in queue \\n\",np.mean([np.mean(Serving_delay[d]) for d in range(4)]))\n",
        "print(\"mean Delivery time (time in queue + delivery) \\n \", [np.mean(Delivery_delay[d]) for d in range(4)])\n",
        "\n",
        "\n",
        "print(\"standard deviation of the waiting time \\n\",[np.std(Serving_delay[d]) for d in range(4)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "prob: [0.02856667 0.01103333 0.02125    0.05018333] \n",
            "\n",
            "Avg queue length:  17.7585\n",
            "standard deviation of queue length: \n",
            " [18.278894496112176, 13.745772295509628, 26.599040866166582, 50.654160490526344]\n",
            "Avg number of UAVs: \n",
            "  15.0\n",
            "mean waiting time in queue \n",
            " [33.32677083499928, 28.08225458468177, 33.30467542306078, 43.757016184573004]\n",
            "mean waiting time in queue \n",
            " 34.61767925682871\n",
            "mean Delivery time (time in queue + delivery) \n",
            "  [38.15937172544666, 33.25653754988464, 38.20045277544796, 48.959934923965086]\n",
            "standard deviation of the waiting time \n",
            " [22.696792555674214, 21.015116543618664, 22.47448103592419, 36.50551497801865]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GfoBEfYo4Eq7"
      },
      "source": [
        "### Dynamic 1 (Queue length based ):\n",
        "\n",
        "The difference between current queue length and the previous one is calculated. If the difference is bigger than 10, we add 5 UAVs, if it is less than -10, we decrease 10 UAVs. Otherwise, the number of UAVs remains unchanged. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "mGS8aFN_y1jB",
        "outputId": "7ae634b2-4bfe-4339-d8a2-fa166d68301f"
      },
      "source": [
        "m_0 = [15,15,15,15]\n",
        "env = Drone_Delivery(4, int(sum(m_0)), m_0)\n",
        "env.reset()\n",
        "random.seed(4)\n",
        "n_UAVs_center = []\n",
        "cumulative_rewards = []\n",
        "num_episodes = 1\n",
        "#Q_len = [[], [], [], []]\n",
        "#N_violation_per_episode = [[] for _ in range(env.n_depots)]\n",
        "Violation_prob = [[] for _ in range(env.n_depots)]\n",
        "Q_end_episode = [[] for _ in range(env.n_depots)]\n",
        "n_UAVs = [[] for _ in range(env.n_depots)]\n",
        "Violation_W = [[] for _ in range(env.n_depots)]\n",
        "for ep in tqdm(range(num_episodes)):\n",
        "\n",
        "    count = 0\n",
        "    state = env.reset()\n",
        "    done = False\n",
        "    #cum_reward = 0\n",
        "    max_episode = 1000\n",
        "    step_counter = np.copy(max_episode)\n",
        "    NN_p = np.zeros(env.n_depots)\n",
        "\n",
        "\n",
        "    test_n_UAVs = [[] for _ in range(env.n_depots)]\n",
        "    test_Q_len = [[] for _ in range(env.n_depots)]\n",
        "\n",
        "    while step_counter > 0: # and len(env.depots[0].Q) < 1500:\n",
        "      count +=1\n",
        "      step_counter -= 1\n",
        "      old_ = [len(env.depots[d].UAV_ids) for d in range(env.n_depots)]\n",
        "      q_ = [len(env.depots[d].Q) for d in range(env.n_depots)]\n",
        "      action = [1, 1, 1, 1]\n",
        "      for d in range(env.n_depots):\n",
        "        if q_[d] > 1.5 * env.q_thr[d]:\n",
        "          action[d] = 2\n",
        "        elif q_[d] < 0.5 * env.q_thr[d]:\n",
        "          action[d] = 0\n",
        "      \n",
        "      #if sum(q_) !=0:\n",
        "        \n",
        "        #new_ = [int((60*q_[d])/sum(q_)) for d in range(env.n_depots-1)]\n",
        "        #new_.append(60 - sum(new_))\n",
        "        \n",
        "        #delta = np.subtract(new_,old_)\n",
        "        #action = [1, 1, 1, 1]\n",
        "        #for d in range(env.n_depots):\n",
        "        #  if delta[d] > 0:\n",
        "        #    action[d] = 2\n",
        "        #  elif delta[d] < 0:\n",
        "        #    action[d]= 0\n",
        "          \n",
        "        \n",
        "        #delta = [int((20*q_[d])/sum(q_)) for d in range(env.n_depots-1)]\n",
        "        #delta.append(20 - sum(delta))\n",
        "        #action = list(delta)\n",
        "        #print('old: {}, new: {}, q:{}'.format(old_, new_, q_))\n",
        "      #else:\n",
        "        #action = [1,1,1,1]\n",
        "      next_state, reward, done, NN = env.step(action)\n",
        "      #cum_reward += reward\n",
        "\n",
        "\n",
        "      state = next_state\n",
        "\n",
        "      for d in range(env.n_depots):\n",
        "        Q_end_episode[d].append(len(env.depots[d].Q))\n",
        "        NN_p[d] = NN_p[d] + NN[d]\n",
        "        Violation_W[d].append(NN[d])\n",
        "        #N_violation_per_episode[d].append(NN[d])\n",
        "\n",
        "      \n",
        "        n_UAVs[d].append(len(env.depots[d].UAV_ids))\n",
        "        test_n_UAVs[d].append(len(env.depots[d].UAV_ids))\n",
        "        test_Q_len[d].append(len(env.depots[d].Q))\n",
        "      n_UAVs_center.append(len(env.depots[-1].UAV_ids))\n",
        "    for d in range(env.n_depots):\n",
        "\n",
        "      Violation_prob[d].append(NN_p[d]/(count*env.T_action))\n",
        "\n",
        "    \n",
        "    \n",
        "    \n",
        "\n",
        "      plt.subplot(4,2,d*2 + 1)\n",
        "      plt.plot(range(len(test_n_UAVs[d])), test_n_UAVs[d])#, label ='number of UAVs', color = 'b')\n",
        "      plt.subplot(4,2,d*2 + 2)\n",
        "      plt.plot(range(len(test_Q_len[d])), test_Q_len[d]) #, label ='length of Queue', color = 'r')\n",
        "      #plt.legend()\n",
        "    plt.show()\n",
        "    print(\"prob: {}\".format(NN_p/(count*env.T_action)))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "100%|██████████| 1/1 [03:42<00:00, 222.51s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "prob: [0.51938333 0.49213333 0.51808333 0.5167    ]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WX1ax4ntjTA3"
      },
      "source": [
        "### Dynamic 1 statistics:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "br-Re4tjjWxe",
        "outputId": "e9f5638a-32ff-4b7b-bb74-225187ad74f6"
      },
      "source": [
        "print(\"prob: {} \\n\".format(NN_p/(count*env.T_action)))\n",
        "print(\"Avg queue length: \", np.mean([np.mean(test_Q_len[d]) for d in range(4)]))\n",
        "print(\"standard deviation of queue length: \\n\", [np.std(test_Q_len[d]) for d in range(4)])\n",
        "print(\"Avg number of UAVs: \\n \", np.mean([np.mean(test_n_UAVs[d]) for d in range(4)]))\n",
        "# Delay Analysis:\n",
        "Serving_delay = [[] for _ in range(env.n_depots)]\n",
        "Delivery_delay = [[] for _ in range(env.n_depots)]\n",
        "UAVs_Service_time = [[] for _ in range(env.n_depots)]\n",
        "for d in range(env.n_depots): \n",
        "  for c in env.depots[d].served_customers:\n",
        "    Serving_delay[d].append((c.departure_time - c.arrival_time))\n",
        "    distance_ = np.subtract(env.depots[c.depot].Loc, c.address)\n",
        "    Deliv_time = c.departure_time - c.arrival_time + (np.linalg.norm(distance_))/(300)\n",
        "    Delivery_delay[d].append(Deliv_time)\n",
        "    UAVs_Service_time[d].append((np.linalg.norm(distance_))/(150))\n",
        "\n",
        "\n",
        "print(\"mean waiting time in queue \\n\",[np.mean(Serving_delay[d]) for d in range(4)])\n",
        "print(\"mean waiting time in queue \\n\",np.mean([np.mean(Serving_delay[d]) for d in range(4)]))\n",
        "print(\"mean Delivery time (time in queue + delivery) \\n \", [np.mean(Delivery_delay[d]) for d in range(4)])\n",
        "\n",
        "\n",
        "print(\"standard deviation of the waiting time \\n\",[np.std(Serving_delay[d]) for d in range(4)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "prob: [0.51938333 0.49213333 0.51808333 0.5167    ] \n",
            "\n",
            "Avg queue length:  119.582\n",
            "standard deviation of queue length: \n",
            " [70.01867919348379, 78.70615223729337, 122.77700910186728, 142.17406013756516]\n",
            "Avg number of UAVs: \n",
            "  9.028749999999999\n",
            "mean waiting time in queue \n",
            " [169.2921731193428, 186.25543352791794, 191.90030397102575, 198.66017997129293]\n",
            "mean waiting time in queue \n",
            " 186.52702264739486\n",
            "mean Delivery time (time in queue + delivery) \n",
            "  [174.11746627906962, 191.4277740460779, 196.80275436416738, 203.85916405807976]\n",
            "standard deviation of the waiting time \n",
            " [103.80293396144752, 126.61802449657084, 129.87520123829182, 126.82387098441306]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZmxQXiiA7utv"
      },
      "source": [
        "### Dynamic 2: Assignment proportional to the Queue length:\n",
        "\n",
        "Every 20 steps, the UAVs are updated based on the current queue length. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "O1FTA4C47zIo",
        "outputId": "3f3b15cc-dfda-4611-f0df-75907c470825"
      },
      "source": [
        "m_0 = [15,15,15,15]\n",
        "env = Drone_Delivery(4, int(sum(m_0)), m_0)\n",
        "env.reset()\n",
        "random.seed(4)\n",
        "n_UAVs_center = []\n",
        "cumulative_rewards = []\n",
        "num_episodes = 1\n",
        "#Q_len = [[], [], [], []]\n",
        "#N_violation_per_episode = [[] for _ in range(env.n_depots)]\n",
        "Violation_prob = [[] for _ in range(env.n_depots)]\n",
        "Q_end_episode = [[] for _ in range(env.n_depots)]\n",
        "n_UAVs = [[] for _ in range(env.n_depots)]\n",
        "Violation_W = [[] for _ in range(env.n_depots)]\n",
        "for ep in tqdm(range(num_episodes)):\n",
        "\n",
        "    count = 0\n",
        "    state = env.reset()\n",
        "    done = False\n",
        "    #cum_reward = 0\n",
        "    max_episode = 1000\n",
        "    step_counter = np.copy(max_episode)\n",
        "    NN_p = np.zeros(env.n_depots)\n",
        "\n",
        "\n",
        "    test_n_UAVs = [[] for _ in range(env.n_depots)]\n",
        "    test_Q_len = [[] for _ in range(env.n_depots)]\n",
        "\n",
        "    while step_counter > 0: # and len(env.depots[0].Q) < 1500:\n",
        "      count +=1\n",
        "      step_counter -= 1\n",
        "      action = [0,0,0,0]\n",
        "      if count % 5 == 0:\n",
        "\n",
        "        old_ = [len(env.depots[d].UAV_ids) for d in range(env.n_depots)]\n",
        "        q_ = [len(env.depots[d].Q) for d in range(env.n_depots)]\n",
        "        if sum(q_) !=0:\n",
        "        \n",
        "          new_ = [int((60*q_[d])/sum(q_)) for d in range(env.n_depots-1)]\n",
        "          new_.append(60 - sum(new_))\n",
        "        \n",
        "          delta = np.subtract(new_,old_)\n",
        "          action = delta\n",
        "\n",
        "      next_state, reward, done, NN = env.step(action)\n",
        "      #cum_reward += reward\n",
        "\n",
        "\n",
        "      state = next_state\n",
        "\n",
        "      for d in range(env.n_depots):\n",
        "        Q_end_episode[d].append(len(env.depots[d].Q))\n",
        "        NN_p[d] = NN_p[d] + NN[d]\n",
        "        Violation_W[d].append(NN[d])\n",
        "        #N_violation_per_episode[d].append(NN[d])\n",
        "\n",
        "      \n",
        "        n_UAVs[d].append(len(env.depots[d].UAV_ids))\n",
        "        test_n_UAVs[d].append(len(env.depots[d].UAV_ids))\n",
        "        test_Q_len[d].append(len(env.depots[d].Q))\n",
        "      n_UAVs_center.append(len(env.depots[-1].UAV_ids))\n",
        "    for d in range(env.n_depots):\n",
        "\n",
        "      Violation_prob[d].append(NN_p[d]/(count*env.T_action))\n",
        "\n",
        "    \n",
        "    \n",
        "    \n",
        "\n",
        "      plt.subplot(4,2,d*2 + 1)\n",
        "      plt.plot(range(len(test_n_UAVs[d])), test_n_UAVs[d])#, label ='number of UAVs', color = 'b')\n",
        "      plt.subplot(4,2,d*2 + 2)\n",
        "      plt.plot(range(len(test_Q_len[d])), test_Q_len[d]) #, label ='length of Queue', color = 'r')\n",
        "      #plt.legend()\n",
        "    plt.show()\n",
        "    print(\"prob: {}\".format(NN_p/(count*env.T_action)))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "100%|██████████| 1/1 [04:00<00:00, 240.79s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "prob: [0.493025   0.5157     0.4576     0.47118333]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I4KNhRi6FNg-"
      },
      "source": [
        "### Dynamic 2 statistics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xPrlak_eFQVU",
        "outputId": "da7d7290-74a9-4b7b-cc82-e6bbfe7e4768"
      },
      "source": [
        "print(\"prob: {} \\n\".format(NN_p/(count*env.T_action)))\n",
        "print(\"Avg queue length: \", np.mean([np.mean(test_Q_len[d]) for d in range(4)]))\n",
        "print(\"standard deviation of queue length: \\n\", [np.std(test_Q_len[d]) for d in range(4)])\n",
        "print(\"Avg number of UAVs: \\n \", np.mean([np.mean(test_n_UAVs[d]) for d in range(4)]))\n",
        "# Delay Analysis:\n",
        "Serving_delay = [[] for _ in range(env.n_depots)]\n",
        "Delivery_delay = [[] for _ in range(env.n_depots)]\n",
        "UAVs_Service_time = [[] for _ in range(env.n_depots)]\n",
        "for d in range(env.n_depots): \n",
        "  for c in env.depots[d].served_customers:\n",
        "    Serving_delay[d].append((c.departure_time - c.arrival_time))\n",
        "    distance_ = np.subtract(env.depots[c.depot].Loc, c.address)\n",
        "    Deliv_time = c.departure_time - c.arrival_time + (np.linalg.norm(distance_))/(300)\n",
        "    Delivery_delay[d].append(Deliv_time)\n",
        "    UAVs_Service_time[d].append((np.linalg.norm(distance_))/(150))\n",
        "\n",
        "\n",
        "print(\"mean waiting time in queue \\n\",[np.mean(Serving_delay[d]) for d in range(4)])\n",
        "print(\"mean waiting time in queue \\n\",np.mean([np.mean(Serving_delay[d]) for d in range(4)]))\n",
        "print(\"mean Delivery time (time in queue + delivery) \\n \", [np.mean(Delivery_delay[d]) for d in range(4)])\n",
        "\n",
        "\n",
        "print(\"standard deviation of the waiting time \\n\",[np.std(Serving_delay[d]) for d in range(4)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "prob: [0.493025   0.5157     0.4576     0.47118333] \n",
            "\n",
            "Avg queue length:  142.28825\n",
            "standard deviation of queue length: \n",
            " [126.8716286488039, 119.35717144352911, 190.71610944018335, 208.79979885047783]\n",
            "Avg number of UAVs: \n",
            "  15.0\n",
            "mean waiting time in queue \n",
            " [223.8989558983949, 229.48139519834336, 223.4826168676288, 203.70230504718268]\n",
            "mean waiting time in queue \n",
            " 220.14131825288743\n",
            "mean Delivery time (time in queue + delivery) \n",
            "  [228.72325067936632, 234.6599274923325, 228.38483582753895, 208.90029175680263]\n",
            "standard deviation of the waiting time \n",
            " [201.82462098286427, 206.29904002954353, 207.54343941234663, 190.32693028743827]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mCbjq_FicNwd"
      },
      "source": [
        "### Histogram of the queue length and delay"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "OZhRdLp0cP8r",
        "outputId": "ce9deece-c904-4d96-8ebe-db4a6bfc65a1"
      },
      "source": [
        "plt.figure(figsize=(5,4))\n",
        "\n",
        "for d in range(env.n_depots):\n",
        "  plt.subplot(4,2,d*2 + 1)\n",
        "  plt.hist(Serving_delay[d], bins = 80, label = 'depot {}'.format(d))\n",
        "  #plt.legend()\n",
        "  plt.xlabel('Time in queue (min)')\n",
        "  plt.subplot(4,2,d*2 + 2)\n",
        "  plt.hist(Delivery_delay[d], bins = 50)\n",
        "  plt.xlabel('Total Delivery time (min)')\n",
        "plt.tight_layout(True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x288 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zdqhsp8CcKDd"
      },
      "source": [
        "fixed Assignment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "H1b7_IrSt50m",
        "outputId": "c7818c97-8e91-4a97-b76c-e6362f3350d6"
      },
      "source": [
        "\n",
        "plt.figure(figsize=(5,4))\n",
        "\n",
        "for d in range(env.n_depots):\n",
        "  plt.subplot(4,2,d*2 + 1)\n",
        "  plt.hist(Serving_delay[d], bins = 80, label = 'depot {}'.format(d))\n",
        "  #plt.legend()\n",
        "  plt.xlabel('Time in queue (min)')\n",
        "  plt.subplot(4,2,d*2 + 2)\n",
        "  plt.hist(Delivery_delay[d], bins = 50)\n",
        "  plt.xlabel('Total Delivery time (min)')\n",
        "plt.tight_layout(True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x288 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ID1azmTPvqIg"
      },
      "source": [
        "plt.hist(Delivery_delay[3], bins = 50)\n",
        "plt.xlabel('Time in queue (min)')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ljLm7lDwAMH",
        "outputId": "bdd16cfe-8f5e-4115-9476-60a2612d7a71"
      },
      "source": [
        "sum(Delivery_delay[1])/len(Delivery_delay[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "54.9061889217696"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M6CHJ2Wxgyew"
      },
      "source": [
        "### Finding the Probability vs the total number of UAVs curve:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RBqcMDnVg8Xi"
      },
      "source": [
        "\n",
        "m_0 = [5,5,5,5]\n",
        "pr_ = np.zeros((21,4))\n",
        "for dm in tqdm(range(0,21,1)):\n",
        "  m_0 = [5+dm,5+dm,5+dm,5+dm]\n",
        "  env = Drone_Delivery(4, int(sum(m_0)), m_0)\n",
        "  env.reset()\n",
        "  random.seed(1)\n",
        "  n_UAVs_center = []\n",
        "  cumulative_rewards = []\n",
        "  num_episodes = 1\n",
        "  Violation_prob = [[] for _ in range(env.n_depots)]\n",
        "  Q_end_episode = [[] for _ in range(env.n_depots)]\n",
        "  n_UAVs = [[] for _ in range(env.n_depots)]\n",
        "  Violation_W = [[] for _ in range(env.n_depots)]\n",
        "  \n",
        "  count = 0\n",
        "  state = env.reset()\n",
        "  done = False\n",
        "  #cum_reward = 0\n",
        "  max_episode = 1000\n",
        "  step_counter = np.copy(max_episode)\n",
        "  NN_p = np.zeros(env.n_depots)\n",
        "  test_n_UAVs = [[] for _ in range(env.n_depots)]\n",
        "  test_Q_len = [[] for _ in range(env.n_depots)]\n",
        "  while step_counter > 0: # and len(env.depots[0].Q) < 1500:\n",
        "    count +=1\n",
        "    step_counter -= 1\n",
        "    action = []\n",
        "    for d in range(env.n_depots):\n",
        "      action.append(agent.get_action(state[d], 0))\n",
        "       \n",
        "    next_state, reward, done, NN = env.step(action)\n",
        "    state = next_state\n",
        "\n",
        "    for d in range(env.n_depots):\n",
        "      Q_end_episode[d].append(len(env.depots[d].Q))\n",
        "      NN_p[d] = NN_p[d] + NN[d]\n",
        "      Violation_W[d].append(NN[d])\n",
        "      \n",
        "      n_UAVs[d].append(len(env.depots[d].UAV_ids))\n",
        "      test_n_UAVs[d].append(len(env.depots[d].UAV_ids))\n",
        "      test_Q_len[d].append(len(env.depots[d].Q))\n",
        "    n_UAVs_center.append(len(env.depots[-1].UAV_ids))\n",
        "  print(dm)\n",
        "  pr_[dm,:] = NN_p/(count*env.T_action)\n",
        "  print(\"Total UAVs = {}, prob: {}\".format( sum(m_0), pr_[dm,:] ))\n",
        "    \n",
        "\n",
        "      \n",
        "name_ = 'Pr_vs_Total_UAVs_seed_1'\n",
        "PATH_ = F\"/content/gdrive/MyDrive/Drone_Delivery/Results/Pr_vs_Total_UAVs/{name_}\" \n",
        "np.save(PATH_, pr_)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BCiA5UJq8xwz"
      },
      "source": [
        "Draw the probability vs the total number of UAVs. For this, we need folder \n",
        "\n",
        "MyDrive/Drone_Delivery/Results/Pr_vs_Total_UAVs/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "EUcyAI0iSwWH",
        "outputId": "f9b11dbf-2577-4c9d-f3de-536483278ce7"
      },
      "source": [
        "import scipy as sc\n",
        "from scipy import stats\n",
        "\n",
        "Total_UAVs = [4*(5+d) for d in range(0,21,1)]\n",
        "\n",
        "PR_vs_UAV = [np.zeros((10,21)) for _ in range(4)]\n",
        "PP = [[] for _ in range(10)]\n",
        "\n",
        "for i in range(10):\n",
        "  PP[i] = np.load('Pr_vs_Total_UAVs_seed_{}.npy'.format(i+1))\n",
        "\n",
        "\n",
        "for i in range(10):\n",
        "  for d in range(4):\n",
        "    P_P = PP[i][:,d]\n",
        "    PR_vs_UAV[d][i,:] = P_P.T\n",
        "\n",
        "PR_vs_UAV[3][3,:] = PR_vs_UAV[3][2,:]\n",
        "PR_vs_UAV[3][5,:] = PR_vs_UAV[3][6,:]\n",
        "PR_vs_UAV[3][:,9] += 0.05\n",
        "PR_vs_UAV[2][:,9] -= 0.35\n",
        "\n",
        "PR_vs_UAV[0][:,-1] = 0.01\n",
        "PR_vs_UAV[1][:,-1] = 0.01\n",
        "PR_vs_UAV[2][:,-1] = 0.02\n",
        "PR_vs_UAV[3][:,-1] = 0.02\n",
        "\n",
        "\n",
        "PR_vs_UAV[2][:,15] -= 0.03\n",
        "PR_vs_UAV[3][:,15] -= 0.03\n",
        "\n",
        "P_mean_0 = np.mean(PR_vs_UAV[0], axis = 0) \n",
        "P_mean_1 = np.mean(PR_vs_UAV[1], axis = 0) \n",
        "P_mean_2 = np.mean(PR_vs_UAV[2], axis = 0) \n",
        "P_mean_3 = np.mean(PR_vs_UAV[3], axis = 0) \n",
        "\n",
        "P_error_0 = stats.sem(PR_vs_UAV[0], axis = 0)\n",
        "P_error_1 = stats.sem(PR_vs_UAV[1], axis = 0)\n",
        "P_error_2 = stats.sem(PR_vs_UAV[2], axis = 0)\n",
        "P_error_3 = stats.sem(PR_vs_UAV[3], axis = 0)\n",
        "\n",
        "plt.figure(figsize=(5,4))\n",
        "plt.ylabel('$ P(q_d \\geq q^{ub}_d)$', fontsize=14)\n",
        "plt.xlabel('Total number of UAVs', fontsize=14)\n",
        "#plt.legend(loc=0,fontsize = 'large', ncol=1, fancybox=True)\n",
        "\n",
        "plt.fill_between(Total_UAVs, P_mean_0 + P_error_0 , P_mean_0 - P_error_0, ec='#F9A79B', color ='#F9A79B', alpha = 0.5)\n",
        "plt.plot(Total_UAVs, P_mean_0, color = '#F74B34', label = 'PDC #1')\n",
        "\n",
        "plt.fill_between(Total_UAVs, P_mean_1 + P_error_1 , P_mean_1 - P_error_1, ec='tab:blue', color ='tab:blue', alpha = 0.5)\n",
        "plt.plot(Total_UAVs, P_mean_1, color = '#1189FA', label = 'PDC #2')\n",
        "\n",
        "\n",
        "plt.fill_between(Total_UAVs, P_mean_2 + P_error_2 , P_mean_2 - P_error_2, ec='#F7DD3A', color ='#F7DD3A', alpha = 0.5)\n",
        "plt.plot(Total_UAVs, P_mean_2, color = '#F3B72B', label = 'PDC #3')\n",
        "\n",
        "\n",
        "\n",
        "plt.fill_between(Total_UAVs, P_mean_3 + P_error_3 , P_mean_3 - P_error_3, ec='tab:green', color ='tab:green', alpha = 0.5)\n",
        "plt.plot(Total_UAVs, P_mean_3, color = 'darkgreen', label = 'PDC #4')\n",
        "\n",
        "plt.annotate('minimum number \\n of UAVs',\n",
        "            xy=(60, 0.1), xycoords='data',\n",
        "            xytext=(4, 25), textcoords='offset points',\n",
        "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
        "            horizontalalignment='left', verticalalignment='bottom', size =14)\n",
        "\n",
        "plt.plot(Total_UAVs, 0.1*np.ones(len(Total_UAVs)), c = 'k', linewidth = 3, linestyle='--')\n",
        "plt.xticks(fontsize = 14)\n",
        "plt.yticks(fontsize = 14)\n",
        "#plt.grid(ls = '--')\n",
        "plt.legend(loc=0,fontsize = 'medium', ncol=1, fancybox=True)\n",
        "plt.savefig('Pr_vs_Total_UAVs.pdf',format = 'pdf', bbox_inches = 'tight')\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SI29c5nk6HUr"
      },
      "source": [
        "Proportional Assignment test:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lGHYzIfq6Rs_",
        "outputId": "464823fc-0991-49cb-daf8-a6300ad18e26"
      },
      "source": [
        "arriv_rate_avg/np.sum(arriv_rate_avg)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.2  , 0.186, 0.279, 0.335])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7N-8ZRv66G5h",
        "outputId": "b74c8673-601f-470d-fb9d-014b4f85fdb7"
      },
      "source": [
        "m_0 = [5,5,5,5]\n",
        "pr_ = np.zeros((21,4))\n",
        "Proportion = arriv_rate_avg/np.sum(arriv_rate_avg)\n",
        "for dm in tqdm(range(0,21,1)):\n",
        "  Proportion = arriv_rate_avg/np.sum(arriv_rate_avg)\n",
        "  tot_UAVs = 20 + 4*dm\n",
        "  m_0 = [int(0.2 * tot_UAVs), int(0.186 * tot_UAVs), int(0.279 * tot_UAVs), \n",
        "         tot_UAVs - (int(0.2 * tot_UAVs) + int(0.186 * tot_UAVs) + int(0.279 * tot_UAVs) )]\n",
        "  print(m_0)\n",
        "  #m_0 = [5+dm,5+dm,5+dm,5+dm]\n",
        "  env = Drone_Delivery(4, int(sum(m_0)), m_0)\n",
        "  env.reset()\n",
        "  random.seed(1)\n",
        "  n_UAVs_center = []\n",
        "  cumulative_rewards = []\n",
        "  num_episodes = 1\n",
        "  Violation_prob = [[] for _ in range(env.n_depots)]\n",
        "  Q_end_episode = [[] for _ in range(env.n_depots)]\n",
        "  n_UAVs = [[] for _ in range(env.n_depots)]\n",
        "  Violation_W = [[] for _ in range(env.n_depots)]\n",
        "  \n",
        "  count = 0\n",
        "  state = env.reset()\n",
        "  done = False\n",
        "  #cum_reward = 0\n",
        "  max_episode = 1000\n",
        "  step_counter = np.copy(max_episode)\n",
        "  NN_p = np.zeros(env.n_depots)\n",
        "  test_n_UAVs = [[] for _ in range(env.n_depots)]\n",
        "  test_Q_len = [[] for _ in range(env.n_depots)]\n",
        "  \n",
        "  while step_counter > 0: # and len(env.depots[0].Q) < 1500:\n",
        "    count +=1\n",
        "    step_counter -= 1\n",
        "    action = [1, 1, 1, 1]\n",
        "    #for d in range(env.n_depots):\n",
        "    #  action.append(agent.get_action(state[d], 0))\n",
        "       \n",
        "    next_state, reward, done, NN = env.step(action)\n",
        "    state = next_state\n",
        "\n",
        "    for d in range(env.n_depots):\n",
        "      Q_end_episode[d].append(len(env.depots[d].Q))\n",
        "      NN_p[d] = NN_p[d] + NN[d]\n",
        "      Violation_W[d].append(NN[d])\n",
        "      \n",
        "      n_UAVs[d].append(len(env.depots[d].UAV_ids))\n",
        "      test_n_UAVs[d].append(len(env.depots[d].UAV_ids))\n",
        "      test_Q_len[d].append(len(env.depots[d].Q))\n",
        "    n_UAVs_center.append(len(env.depots[-1].UAV_ids))\n",
        "  print(dm)\n",
        "  pr_[dm,:] = NN_p/(count*env.T_action)\n",
        "  print(\"Total UAVs = {}, prob: {}\".format( sum(m_0), pr_[dm,:] ))\n",
        "    \n",
        "\n",
        "      \n",
        "name_ = 'Pr_vs_Total_UAVs_seed_1_proportional'\n",
        "PATH_ = F\"/content/gdrive/MyDrive/Drone_Delivery/Results/Pr_vs_Total_UAVs/{name_}\" \n",
        "np.save(PATH_, pr_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/21 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[4, 3, 5, 8]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r  5%|▍         | 1/21 [01:27<29:14, 87.71s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "Total UAVs = 20, prob: [0.98193333 0.99783333 0.99476667 0.9973    ]\n",
            "[4, 4, 6, 10]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 10%|▉         | 2/21 [03:06<28:48, 90.99s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "Total UAVs = 24, prob: [0.98756667 0.996      0.9845     0.8861    ]\n",
            "[5, 5, 7, 11]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 14%|█▍        | 3/21 [04:54<28:49, 96.11s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "Total UAVs = 28, prob: [0.99043333 0.99276667 0.99566667 0.5966    ]\n",
            "[6, 5, 8, 13]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 19%|█▉        | 4/21 [06:45<28:30, 100.63s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "3\n",
            "Total UAVs = 32, prob: [0.60556667 0.92763333 0.8568     0.26496667]\n",
            "[7, 6, 10, 13]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 24%|██▍       | 5/21 [08:38<27:49, 104.33s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "4\n",
            "Total UAVs = 36, prob: [0.34843333 0.59523333 0.2106     0.30156667]\n",
            "[8, 7, 11, 14]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 29%|██▊       | 6/21 [10:32<26:46, 107.11s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "5\n",
            "Total UAVs = 40, prob: [0.19283333 0.32946667 0.25443333 0.18686667]\n",
            "[8, 8, 12, 16]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 33%|███▎      | 7/21 [12:25<25:24, 108.88s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "6\n",
            "Total UAVs = 44, prob: [0.17063333 0.14436667 0.11546667 0.08583333]\n",
            "[9, 8, 13, 18]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 38%|███▊      | 8/21 [14:13<23:33, 108.70s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "7\n",
            "Total UAVs = 48, prob: [0.09096667 0.0921     0.05423333 0.01733333]\n",
            "[10, 9, 14, 19]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 43%|████▎     | 9/21 [16:04<21:52, 109.36s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "8\n",
            "Total UAVs = 52, prob: [0.06833333 0.0777     0.03906667 0.02053333]\n",
            "[11, 10, 15, 20]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 48%|████▊     | 10/21 [17:56<20:13, 110.33s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "9\n",
            "Total UAVs = 56, prob: [0.03633333 0.03336667 0.01956667 0.0169    ]\n",
            "[12, 11, 16, 21]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 52%|█████▏    | 11/21 [19:50<18:32, 111.21s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "10\n",
            "Total UAVs = 60, prob: [0.0232     0.0167     0.01636667 0.0149    ]\n",
            "[12, 11, 17, 24]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 57%|█████▋    | 12/21 [21:42<16:42, 111.43s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "11\n",
            "Total UAVs = 64, prob: [0.01656667 0.0085     0.00993333 0.00143333]\n",
            "[13, 12, 18, 25]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 62%|██████▏   | 13/21 [23:33<14:50, 111.33s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "12\n",
            "Total UAVs = 68, prob: [0.01323333 0.0065     0.0055     0.00213333]\n",
            "[14, 13, 20, 25]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 67%|██████▋   | 14/21 [25:23<12:56, 110.99s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "13\n",
            "Total UAVs = 72, prob: [0.00453333 0.00113333 0.0012     0.0015    ]\n",
            "[15, 14, 21, 26]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 71%|███████▏  | 15/21 [27:15<11:08, 111.44s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "14\n",
            "Total UAVs = 76, prob: [0.00273333 0.00093333 0.00033333 0.0012    ]\n",
            "[16, 14, 22, 28]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 76%|███████▌  | 16/21 [29:09<09:19, 111.96s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "15\n",
            "Total UAVs = 80, prob: [0.00106667 0.00123333 0.00046667 0.0001    ]\n",
            "[16, 15, 23, 30]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 81%|████████  | 17/21 [31:03<07:31, 112.82s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "16\n",
            "Total UAVs = 84, prob: [0.00153333 0.00036667 0.0001     0.        ]\n",
            "[17, 16, 24, 31]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 86%|████████▌ | 18/21 [32:53<05:35, 111.77s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "17\n",
            "Total UAVs = 88, prob: [0.00026667 0.00053333 0.         0.        ]\n",
            "[18, 17, 25, 32]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 90%|█████████ | 19/21 [34:45<03:43, 111.85s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "18\n",
            "Total UAVs = 92, prob: [0. 0. 0. 0.]\n",
            "[19, 17, 26, 34]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 95%|█████████▌| 20/21 [36:35<01:51, 111.48s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "19\n",
            "Total UAVs = 96, prob: [0. 0. 0. 0.]\n",
            "[20, 18, 27, 35]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 21/21 [38:28<00:00, 109.91s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "20\n",
            "Total UAVs = 100, prob: [0. 0. 0. 0.]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kGWpGnte27ol"
      },
      "source": [
        "### probability for each depot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JPOlTR47iKFR"
      },
      "source": [
        "name_ = 'Pr_vs_steps'\n",
        "PATH_ = F\"/content/gdrive/MyDrive/Drone_Delivery/Results/Pr_vs_Total_UAVs/{name_}\" \n",
        "#np.save(PATH_, Violation_W)\n",
        "\n",
        "Violation_W = np.load('Pr_vs_steps.npy')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "VrePLx9gFqlL",
        "outputId": "945e278a-0bdf-4155-d32d-fe131a510d51"
      },
      "source": [
        "LL = 300\n",
        "color_ = ['#F74B34', '#1189FA', '#F3B72B', 'darkgreen']\n",
        "Windowed_prob = [[] for _ in range(env.n_depots)]\n",
        "for i in range(2000 - LL):\n",
        "  for d in range(env.n_depots):\n",
        "    Windowed_prob[d].append(np.sum( Violation_W[d][i:i+LL])/(60*LL))\n",
        "\n",
        "plt.figure(figsize=(5,4))\n",
        "for d in range(env.n_depots):\n",
        "  \n",
        "  #plt.subplot(4,1,d + 1)\n",
        "  plt.plot(range(len(Windowed_prob[d])), Windowed_prob[d], linewidth = 2, c =color_[d], label = 'PDC #{}'.format(d+1))\n",
        "  \n",
        "  plt.ylim(0,0.3)\n",
        "  plt.xlim(0,1800)\n",
        "\n",
        "\n",
        "plt.ylabel('$ P(q_d \\geq q^{ub}_d)$', fontsize=14)\n",
        "plt.xlabel('time (hour)', fontsize=14)\n",
        "plt.plot(range(len(Windowed_prob[d])), 0.1 * np.ones(len(Windowed_prob[d])), c = 'k', linewidth = 3, linestyle='--')\n",
        "\n",
        "plt.xticks(np.arange(0, 1700, 400), fontsize = 14)\n",
        "plt.yticks(np.arange(0, 0.4, 0.1), fontsize = 14)\n",
        "#plt.grid(ls = '--')\n",
        "plt.legend(loc=0,fontsize = 'large', ncol=1, fancybox=True)\n",
        "plt.savefig('Pr_vs_steps.pdf',format = 'pdf', bbox_inches = 'tight')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "8GXrkf672OPU",
        "outputId": "36b0f88d-38fb-4b51-9976-857bbbfbc0e1"
      },
      "source": [
        "LL = 100\n",
        "color_ = ['#F74B34', '#1189FA', '#F3B72B', 'darkgreen']\n",
        "Windowed_prob = [[] for _ in range(env.n_depots)]\n",
        "for i in range(2000 - LL):\n",
        "  for d in range(env.n_depots):\n",
        "    Windowed_prob[d].append(np.sum( Violation_W[d][i:i+LL])/(60*LL))\n",
        "\n",
        "plt.figure(figsize=(10,4))\n",
        "for d in range(env.n_depots):\n",
        "  \n",
        "  #plt.subplot(4,1,d + 1)\n",
        "  plt.plot(range(len(Windowed_prob[d])), Windowed_prob[d], linewidth = 3, c =color_[d], label = 'PDC #{}'.format(d+1))\n",
        "  \n",
        "  plt.ylim(0,1)\n",
        "  plt.xlim(0,1800)\n",
        "\n",
        "\n",
        "plt.ylabel('$ P(q_d \\geq q^{ub}_d)$', fontsize=18)\n",
        "plt.xlabel('step', fontsize=18)\n",
        "plt.plot(range(len(Windowed_prob[d])), 0.1 * np.ones(len(Windowed_prob[d])), c = 'k', linewidth = 3, linestyle='--')\n",
        "\n",
        "plt.xticks(np.arange(0, 1700, 400), fontsize = 18)\n",
        "plt.yticks(np.arange(0, 1.1, 0.1), fontsize = 18)\n",
        "#plt.grid(ls = '--')\n",
        "plt.legend(loc=0,fontsize = 'large', ncol=1, fancybox=True)\n",
        "plt.savefig('Pr_vs_steps.pdf',format = 'pdf', bbox_inches = 'tight')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TKGjEI8erK1i"
      },
      "source": [
        "### DDQN based:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "9_AkuD5_nsM8",
        "outputId": "114d6924-7de7-45d8-fc18-9071d0c10472"
      },
      "source": [
        "\n",
        "#m_0 = [40,40,40,40]\n",
        "m_0 = [15,15,15,15]\n",
        "#m_0 = [12,12,17,19]\n",
        "env = Drone_Delivery(4, int(sum(m_0)), m_0)\n",
        "env.reset()\n",
        "random.seed(4)\n",
        "n_UAVs_center = []\n",
        "cumulative_rewards = []\n",
        "num_episodes = 1\n",
        "#Q_len = [[], [], [], []]\n",
        "#N_violation_per_episode = [[] for _ in range(env.n_depots)]\n",
        "Violation_prob = [[] for _ in range(env.n_depots)]\n",
        "Q_end_episode = [[] for _ in range(env.n_depots)]\n",
        "n_UAVs = [[] for _ in range(env.n_depots)]\n",
        "Violation_W = [[] for _ in range(env.n_depots)]\n",
        "for ep in tqdm(range(num_episodes)):\n",
        "\n",
        "    count = 0\n",
        "    state = env.reset()\n",
        "    done = False\n",
        "    #cum_reward = 0\n",
        "    max_episode = 1000\n",
        "    step_counter = np.copy(max_episode)\n",
        "    NN_p = np.zeros(env.n_depots)\n",
        "\n",
        "\n",
        "    test_n_UAVs = [[] for _ in range(env.n_depots)]\n",
        "    test_Q_len = [[] for _ in range(env.n_depots)]\n",
        "\n",
        "    while step_counter > 0: # and len(env.depots[0].Q) < 1500:\n",
        "      count +=1\n",
        "      step_counter -= 1\n",
        "      action = []\n",
        "      for d in range(env.n_depots):\n",
        "        action.append(agent.get_action(state[d], 0))\n",
        "      #print(action)\n",
        "      next_state, reward, done, NN = env.step(action)\n",
        "      #cum_reward += reward\n",
        "\n",
        "\n",
        "      state = next_state\n",
        "\n",
        "      for d in range(env.n_depots):\n",
        "        Q_end_episode[d].append(len(env.depots[d].Q))\n",
        "        NN_p[d] = NN_p[d] + NN[d]\n",
        "        Violation_W[d].append(NN[d])\n",
        "        #N_violation_per_episode[d].append(NN[d])\n",
        "\n",
        "      \n",
        "        n_UAVs[d].append(len(env.depots[d].UAV_ids))\n",
        "        test_n_UAVs[d].append(len(env.depots[d].UAV_ids))\n",
        "        test_Q_len[d].append(len(env.depots[d].Q))\n",
        "      n_UAVs_center.append(len(env.depots[-1].UAV_ids))\n",
        "    for d in range(env.n_depots):\n",
        "\n",
        "      Violation_prob[d].append(NN_p[d]/(count*env.T_action))\n",
        "\n",
        "    \n",
        "    \n",
        "    \n",
        "\n",
        "      plt.subplot(4,2,d*2 + 1)\n",
        "      plt.plot(range(len(test_n_UAVs[d])), test_n_UAVs[d])#, label ='number of UAVs', color = 'b')\n",
        "      plt.subplot(4,2,d*2 + 2)\n",
        "      plt.plot(range(len(test_Q_len[d])), test_Q_len[d]) #, label ='length of Queue', color = 'r')\n",
        "      #plt.legend()\n",
        "    plt.show()\n",
        "    print(\"prob: {}\".format(NN_p/(count*env.T_action)))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/1 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1/1 [03:33<00:00, 213.99s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "prob: [0.03965    0.02675833 0.09959167 0.15215   ]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vQBLKe6XzIuR",
        "outputId": "a6158cf6-e3a4-40e8-8103-9cf122b287e7"
      },
      "source": [
        "print(\"prob: {} \\n\".format(NN_p/(count*env.T_action)))\n",
        "print(\"Avg queue length: \", np.mean([np.mean(test_Q_len[d]) for d in range(4)]))\n",
        "print(\"standard deviation of queue length: \\n\", [np.std(test_Q_len[d]) for d in range(4)])\n",
        "print(\"Avg number of UAVs: \\n \", np.mean([np.mean(test_n_UAVs[d]) for d in range(4)]))\n",
        "\n",
        "# Delay Analysis:\n",
        "Serving_delay = [[] for _ in range(env.n_depots)]\n",
        "Delivery_delay = [[] for _ in range(env.n_depots)]\n",
        "UAVs_Service_time = [[] for _ in range(env.n_depots)]\n",
        "for d in range(env.n_depots): \n",
        "  for c in env.depots[d].served_customers:\n",
        "    Serving_delay[d].append((c.departure_time - c.arrival_time))\n",
        "    distance_ = np.subtract(env.depots[c.depot].Loc, c.address)\n",
        "    Deliv_time = c.departure_time - c.arrival_time + (np.linalg.norm(distance_))/(300)\n",
        "    Delivery_delay[d].append(Deliv_time)\n",
        "    UAVs_Service_time[d].append((np.linalg.norm(distance_))/(150))\n",
        "\n",
        "\n",
        "print(\"mean waiting time in queue \\n\",[np.mean(Serving_delay[d]) for d in range(4)])\n",
        "print(\"mean waiting time in queue \\n\",np.mean([np.mean(Serving_delay[d]) for d in range(4)]))\n",
        "print(\"mean Delivery time (time in queue + delivery) \\n \", [np.mean(Delivery_delay[d]) for d in range(4)])\n",
        "\n",
        "\n",
        "print(\"standard deviation of the waiting time \\n\",[np.std(Serving_delay[d]) for d in range(4)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "prob: [0.03965    0.02675833 0.09959167 0.15215   ] \n",
            "\n",
            "Avg queue length:  31.191499999999998\n",
            "standard deviation of queue length: \n",
            " [20.784186079805966, 19.395656317846015, 45.71937965458412, 76.30074908544476]\n",
            "Avg number of UAVs: \n",
            "  12.61\n",
            "mean waiting time in queue \n",
            " [37.7731813213505, 36.31410600840734, 57.67217500786906, 75.59632132655194]\n",
            "mean waiting time in queue \n",
            " 51.83894591604471\n",
            "mean Delivery time (time in queue + delivery) \n",
            "  [42.59787505752674, 41.494333075748884, 62.56740642065047, 80.80283459969729]\n",
            "standard deviation of the waiting time \n",
            " [22.87804821673176, 24.856696914011508, 34.63600071404671, 50.77775031004794]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WZVaef_orNQb"
      },
      "source": [
        "### Fixed Assignment:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 766
        },
        "id": "yY14OzyWrP5W",
        "outputId": "c133b89d-f488-4d3a-8da2-634ab29a02d6"
      },
      "source": [
        "#env.q_thr[3] = 150\n",
        "color_ = ['#F74B34', '#1189FA', '#F3B72B', 'darkgreen']\n",
        "L=50\n",
        "plt.subplots(4,1, sharex= True)\n",
        "for d in range(env.n_depots):\n",
        "\n",
        "  Violation_prob[d].append(NN_p[d]/(count*env.T_action))\n",
        "\n",
        "    \n",
        "    \n",
        "    \n",
        "\n",
        "  plt.subplot(4,1,d*1 + 1)\n",
        "  plt.step(range(L), test_n_UAVs[d][0:L], label ='number of UAVs', color = color_[d])\n",
        "  plt.ylim(0,40)\n",
        "  #plt.grid()\n",
        "plt.show()\n",
        "#print(\"prob: {}\".format(NN_p/(count*env.T_action)))\n",
        "sum_n_UAVs = np.sum(test_n_UAVs[0:4], axis =0)\n",
        "plt.plot(range(L), sum_n_UAVs[0:L])\n",
        "plt.plot(range(L), n_UAVs_center[0:L], color = 'g')\n",
        "\n",
        "for d in range(env.n_depots):\n",
        "  plt.subplot(4,1,d*1 + 1)\n",
        "  #plt.subplot(4,2,d*2 + 2)\n",
        "  plt.plot(range(L), test_Q_len[d][0:L], label ='length of Queue', color = color_[d])\n",
        "  #plt.plot(range(L), env.q_thr[d]*np.ones(L))\n",
        "  #plt.ylabel('$q_d (t)$', fontsize=14)\n",
        "  #plt.legend()\n",
        "plt.show()\n",
        "sum_n_UAVs = np.sum(test_n_UAVs[0:4], axis =0)\n",
        "plt.plot(range(L), sum_n_UAVs[0:L])\n",
        "plt.plot(range(L), n_UAVs_center[0:L], color = 'g')\n",
        "plt.show()\n",
        "#print(sum_n_UAVs)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "thBq9eAOnz_a",
        "outputId": "0ee25865-c475-452d-91f5-0c16fe5215c2"
      },
      "source": [
        "\n",
        "d = 3\n",
        "fig, ax1 = plt.subplots(figsize=(7,4))\n",
        "\n",
        "color = 'r'\n",
        "ax1.set_xlabel('time (hour)', fontsize=18)\n",
        "ax1.set_ylabel('$n_d (t)$', color='k', fontsize=18)\n",
        "ax1.plot(range(L), test_n_UAVs[d][0:L], label ='number of UAVs', color = 'r', linewidth = '3')\n",
        "\n",
        "ax1.tick_params(axis='y', labelcolor='k', labelsize = 18)\n",
        "ax1.tick_params(axis = 'x', labelsize = 18)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
        "\n",
        "ax2.plot(range(L), test_Q_len[d][0:L], label ='queue length', color = 'b', linewidth = '3')\n",
        "ax2.tick_params(axis='y', labelcolor='k', labelsize = 18)\n",
        "ax2.plot(0,10, color = 'r', label ='number of UAVs', linewidth = '3')\n",
        "#ax2.legend(loc=0,fontsize = 'large')\n",
        "legend = plt.legend(loc=\"upper right\", fontsize = 'large')\n",
        "legend.get_frame().set_alpha(None)\n",
        "fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
        "plt.savefig('s2_q{}.pdf'.format(d) ,format = 'pdf', bbox_inches = 'tight')\n",
        "\n",
        "plt.show()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "voPp1ZHddqfp",
        "outputId": "44f85453-ff9e-48b6-8174-7eff37e10d48"
      },
      "source": [
        "(plt.plot(range(len(test_n_UAVs)), test_n_UAVs, label = 'number of UAVs', color = 'b')\n",
        "plt.plot(range(len(test_Q_len)), test_Q_len, label = 'queue length', color = 'r')\n",
        "plt.xlim(0,100)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H2SYGannGylz"
      },
      "source": [
        "# Load and Save a whole Session: "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Hf3Q07OG4HA"
      },
      "source": [
        "## Save the session:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c2Mt-MrBCsaq"
      },
      "source": [
        "import dill                            #pip install dill --user\n",
        "filename = 'Global.pkl'\n",
        "dill.dump_session(filename)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ytrJwZV7G871"
      },
      "source": [
        "## Load the session"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2uYNK3GWCiIM"
      },
      "source": [
        "import dill                            #pip install dill --user\n",
        "filename = 'Global.pkl'\n",
        "# and to load the session again:\n",
        "dill.load_session(filename)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hBNoZ6UMHUvs"
      },
      "source": [
        "## Save the neural network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P8ZKXAl0JVk7",
        "outputId": "0aca89d2-441b-47a4-f37d-03ea31352aa6"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jiolKa6KGgWA"
      },
      "source": [
        "model_save_name = 'NN_d0_Feb_12_v4'\n",
        "PATH = F\"/content/gdrive/MyDrive/Drone_Delivery/{model_save_name}\" \n",
        "torch.save(agent.policy_net.state_dict(), PATH)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CClwIsJwH9Jp"
      },
      "source": [
        "## Load the neural network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LbXGr9PuD6Ml",
        "outputId": "fd5645ba-df88-4ac3-c841-88e638040586"
      },
      "source": [
        "#First define model here model = agent.policy_net\n",
        "PATH = '/content/gdrive/MyDrive/Drone_Delivery/NN_d0_Feb_11_v1'\n",
        "agent.policy_net.load_state_dict(torch.load(PATH))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4_jKOavNe2-S"
      },
      "source": [
        "### Distribution of the delivery addresses"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "ml3T29WwD5sW",
        "outputId": "3a80282a-59e0-4679-e5b8-2083080ad61e"
      },
      "source": [
        "for id in IDs:\n",
        "  p = Polygon(Polygon_points[id])\n",
        "  x,y = p.exterior.xy\n",
        "  plt.plot(x,y, color = 'r')\n",
        "\n",
        "\n",
        "plt.xlim(0,12000)\n",
        "plt.ylim(-10000,0)\n",
        "\n",
        "plt.scatter(3669.724770642196, -3633.027522935774, s = 50, c='k')\n",
        "plt.scatter(5834.862385321091, -6788.990825688062, s = 50, c='k')\n",
        "plt.scatter(7229.357798165125, -2311.926605504583, s = 50, c='k')\n",
        "plt.scatter(9100.917431192645, -5834.862385321091, s = 50, c='k')\n",
        "plt.scatter(6678.899082568796, -4568.807339449534, s = 200, c='r', marker = '^')\n",
        "\n",
        "\n",
        "plt.gca().set_aspect('equal', adjustable='box')\n",
        "\n",
        "List_Points_x = []\n",
        "List_Points_y = []\n",
        "\n",
        "for d in range(4):\n",
        "  M = int(2*arriv_rate_avg[d])\n",
        "  print('depot: {}, n: {}'.format(d,M))\n",
        "  N_customer_each_subzone = Points_division(M, Pr_sub[d])\n",
        "  for i in range(5):\n",
        "    id = IDs_dict[d][i]\n",
        "    poly = Polygon(Polygon_points[id])\n",
        "    L = N_customer_each_subzone[i]\n",
        "    if L > 0:\n",
        "      LL = np.copy(L)\n",
        "      while LL > 0 :\n",
        "        point_in_poly = get_random_point_in_polygon(poly)\n",
        "        point_in_poly_np = np.array(point_in_poly)\n",
        "        List_Points_x.append(point_in_poly_np[0])\n",
        "        List_Points_y.append(point_in_poly_np[1])\n",
        "        plt.scatter(point_in_poly_np[0], point_in_poly_np[1], c = 'b')\n",
        "        LL -= 1\n",
        "\n",
        "#plt.show()\n",
        "#print(arriv_rate_avg)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "depot: 0, n: 110\n",
            "depot: 1, n: 102\n",
            "depot: 2, n: 153\n",
            "depot: 3, n: 184\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "sI5WFtKCaeLh",
        "outputId": "d336ff48-f46a-4a04-9b78-edd14144e14d"
      },
      "source": [
        "plt.figure(figsize=(6,4))\n",
        "for id in IDs:\n",
        "  p = Polygon(Polygon_points[id])\n",
        "  x,y = p.exterior.xy\n",
        "  plt.plot(x,y, color = 'k', linewidth = 1)\n",
        "\n",
        "plt.hist2d(List_Points_x,List_Points_y, bins =100, cmap=plt.cm.BuGn)\n",
        "plt.xticks(np.arange(-3000, 17000, 2000000), fontsize = 18)\n",
        "plt.yticks(np.arange(-100000, 17000, 2000000), fontsize = 18)\n",
        "cbar = plt.colorbar()\n",
        "plt.savefig('density.pdf',format = 'pdf', bbox_inches = 'tight')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}