main.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import math\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "from copy import deepcopy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xr9_R16ewXc6"
   },
   "source": [
    "# Dataset"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Name**: name of appliance\n",
    "- **Type**: non-shiftable (NS), power-shiftable operable at different power levels (PS), time-shiftable operable at any time slot within normal working slots (TS)\n",
    "- **Diss.Coeff.**: dissatisfaction coefficient / factor determining weightage of deviation from maximum power level for power shiftable appliances and from earliest normal working slot (waiting time) for time shiftable appliances\n",
    "- **Power rating**: appliance's power levels\n",
    "- **Time Slot**: normal working slots for each appliance"
   ]
  },
  {
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   "metadata": {
    "id": "T4Fgp56ckIiq"
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   "outputs": [],
   "source": [
    "num_slots = 8"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Type</th>\n",
       "      <th>Diss. Coeff.</th>\n",
       "      <th>Power Rating (kWh)</th>\n",
       "      <th>Time Slot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ref</td>\n",
       "      <td>NS</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>[1, 8]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AS</td>\n",
       "      <td>NS</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[1, 8]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AC2</td>\n",
       "      <td>PS</td>\n",
       "      <td>50.00</td>\n",
       "      <td>[0.7, 1.4, 0.1]</td>\n",
       "      <td>[1, 8]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>L1</td>\n",
       "      <td>PS</td>\n",
       "      <td>20.00</td>\n",
       "      <td>[0.2, 0.6, 0.1]</td>\n",
       "      <td>[5, 7]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>H</td>\n",
       "      <td>PS</td>\n",
       "      <td>0.12</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>WM</td>\n",
       "      <td>TS</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.7</td>\n",
       "      <td>[6, 8]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DW</td>\n",
       "      <td>TS</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.3</td>\n",
       "      <td>[6, 8]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AC1</td>\n",
       "      <td>PS</td>\n",
       "      <td>0.05</td>\n",
       "      <td>[0.7, 1.4, 0.1]</td>\n",
       "      <td>[1, 8]</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>L2</td>\n",
       "      <td>PS</td>\n",
       "      <td>0.03</td>\n",
       "      <td>[0.2, 0.6, 0.1]</td>\n",
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      "text/plain": [
       "  Name Type  Diss. Coeff. Power Rating (kWh) Time Slot\n",
       "0  Ref   NS        100.00                0.5    [1, 8]\n",
       "1   AS   NS        100.00                0.1    [1, 8]\n",
       "3  AC2   PS         50.00    [0.7, 1.4, 0.1]    [1, 8]\n",
       "5   L1   PS         20.00    [0.2, 0.6, 0.1]    [5, 7]\n",
       "4    H   PS          0.12    [0.5, 1.5, 0.1]    [1, 8]\n",
       "7   WM   TS          0.10                0.7    [6, 8]\n",
       "8   DW   TS          0.06                0.3    [6, 8]\n",
       "2  AC1   PS          0.05    [0.7, 1.4, 0.1]    [1, 8]\n",
       "6   L2   PS          0.03    [0.2, 0.6, 0.1]    [5, 7]"
      ]
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   "source": [
    "appliance_data = [['Ref', 'NS', 100, 0.5, [1, num_slots]], ['AS', 'NS', 100, 0.1, [1, num_slots]], \n",
    "                  ['AC1', 'PS', 0.05, [0.7, 1.4, 0.1], [1, num_slots]], ['AC2', 'PS', 50, [0.7, 1.4, 0.1], [1, num_slots]], \n",
    "                  ['H', 'PS', 0.12, [0.5, 1.5, 0.1], [1, num_slots]], \n",
    "                  ['L1', 'PS', 20, [0.2, 0.6, 0.1], [5, 7]],\n",
    "                  ['L2', 'PS', 0.03, [0.2, 0.6, 0.1], [5, 7]],\n",
    "                  ['WM', 'TS', 0.1, 0.7, [6, 8]],\n",
    "                  ['DW', 'TS', 0.06, 0.3, [6, 8]]]\n",
    "  \n",
    "appliance_df = pd.DataFrame(appliance_data, columns=['Name', 'Type', 'Diss. Coeff.', 'Power Rating (kWh)', 'Time Slot'])\n",
    "appliance_df\n",
    "\n",
    "appliance_df = appliance_df.sort_values('Diss. Coeff.', ascending = False)\n",
    "appliance_df"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "id": "JGaxwy54H0Ck"
   },
   "source": [
    "# Electricity Price Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ytbGS7mC_QBz"
   },
   "outputs": [],
   "source": [
    "ep_mx_q1 = 35\n",
    "ep_mx_q2 = 35\n",
    "ep_mx_q3 = 30\n",
    "ep_mx_q4 = 20\n",
    "ep_k = 5\n",
    "min_price = 7"
   ]
  },
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\n",
      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_points = 1000\n",
    "\n",
    "mu, sigma = 0, 1.0/(np.sqrt(2 * np.pi) * ep_mx_q1)\n",
    "s = np.random.normal(mu, sigma, num_points)\n",
    "count, bins, ignored = plt.hist(s, num_slots, density=True)\n",
    "plt.clf()\n",
    "electricity_prices = [max(0, 1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (i - mu)**2 / (2 * sigma**2)) + random.uniform(-ep_k, ep_k)) for i in bins[:-1]]\n",
    "distribution = [1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (i - mu)**2 / (2 * sigma**2)) for i in bins[:-1]]\n",
    "\n",
    "mu, sigma = 0, 1.0/(np.sqrt(2 * np.pi) * ep_mx_q2)\n",
    "s = np.random.normal(mu, sigma, num_points)\n",
    "count, bins, ignored = plt.hist(s, num_slots, density=True)\n",
    "plt.clf()\n",
    "electricity_prices += [max(0, 1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (i - mu)**2 / (2 * sigma**2)) + random.uniform(-ep_k, ep_k)) for i in bins[:-1]]\n",
    "distribution += [1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (i - mu)**2 / (2 * sigma**2)) for i in bins[:-1]]\n",
    "\n",
    "mu, sigma = 0, 1.0/(np.sqrt(2 * np.pi) * ep_mx_q3)\n",
    "s = np.random.normal(mu, sigma, num_points)\n",
    "count, bins, ignored = plt.hist(s, num_slots, density=True)\n",
    "plt.clf()\n",
    "electricity_prices += [max(0, 1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (i - mu)**2 / (2 * sigma**2)) + random.uniform(-ep_k, ep_k)) for i in bins[:-1]]\n",
    "distribution += [1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (i - mu)**2 / (2 * sigma**2)) for i in bins[:-1]]\n",
    "\n",
    "mu, sigma = 0, 1.0/(np.sqrt(2 * np.pi) * ep_mx_q4)\n",
    "s = np.random.normal(mu, sigma, num_points)\n",
    "count, bins, ignored = plt.hist(s, num_slots, density=True)\n",
    "plt.clf()\n",
    "electricity_prices += [max(0, 1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (i - mu)**2 / (2 * sigma**2)) + random.uniform(-ep_k, ep_k)) for i in bins[:-1]]\n",
    "distribution += [1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (i - mu)**2 / (2 * sigma**2)) for i in bins[:-1]]\n",
    "\n",
    "electricity_prices = [i + min_price for i in electricity_prices]\n",
    "distribution = [i + min_price for i in distribution]\n",
    "\n",
    "slots = range(1, 4*num_slots+1)\n",
    "f = plt.figure()\n",
    "f.set_figwidth(20)\n",
    "f.set_figheight(9)\n",
    "plt.title(\"Electricity Prices\")\n",
    "plt.xlabel(\"Slot\")\n",
    "plt.ylabel(\"Price ($/MWk)\")\n",
    "plt.plot(slots, electricity_prices, \"-o\", label = \"prices\")\n",
    "plt.plot(slots, distribution, label = \"distribution\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7rWS2T8qH4OD"
   },
   "source": [
    "# Solar Power Generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not used in current code as it is fixed allocation in each timeslot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cuPsOSrww35e"
   },
   "outputs": [],
   "source": [
    "sp_mx_q1 = 200\n",
    "sp_mx_q2 = 200\n",
    "sp_mx_q3 = 250\n",
    "sp_mx_q4 = 100\n",
    "sp_k = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 584
    },
    "id": "JCIwsDRJw37q",
    "outputId": "0cf63aa8-75f1-43c1-c6fb-03109f66fbf6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_points = 1000\n",
    "\n",
    "solar_power = [0.0 for i in range(num_slots - num_slots//2)]\n",
    "distribution = [0.0 for i in range(num_slots - num_slots//2)]\n",
    "\n",
    "mu, sigma = 0, 1.0/(np.sqrt(2 * np.pi) * sp_mx_q1)\n",
    "s = np.random.normal(mu, sigma, num_points)\n",
    "count, bins, ignored = plt.hist(s, num_slots//2, density=True)\n",
    "plt.clf()\n",
    "solar_power += [max(0, 1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (i - mu)**2 / (2 * sigma**2)) + random.uniform(-sp_k, sp_k)) for i in bins[:-1]]\n",
    "distribution += [1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (i - mu)**2 / (2 * sigma**2)) for i in bins[:-1]]\n",
    "\n",
    "solar_power += [0.0 for i in range(num_slots - num_slots//2)]\n",
    "distribution += [0.0 for i in range(num_slots - num_slots//2)]\n",
    "\n",
    "mu, sigma = 0, 1.0/(np.sqrt(2 * np.pi) * sp_mx_q2)\n",
    "s = np.random.normal(mu, sigma, num_points)\n",
    "count, bins, ignored = plt.hist(s, num_slots//2, density=True)\n",
    "plt.clf()\n",
    "solar_power += [max(0, 1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (i - mu)**2 / (2 * sigma**2)) + random.uniform(-sp_k, sp_k)) for i in bins[:-1]]\n",
    "distribution += [1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (i - mu)**2 / (2 * sigma**2)) for i in bins[:-1]]\n",
    "\n",
    "solar_power += [0.0 for i in range(num_slots - num_slots//2)]\n",
    "distribution += [0.0 for i in range(num_slots - num_slots//2)]\n",
    "\n",
    "mu, sigma = 0, 1.0/(np.sqrt(2 * np.pi) * sp_mx_q3)\n",
    "s = np.random.normal(mu, sigma, num_points)\n",
    "count, bins, ignored = plt.hist(s, num_slots//2, density=True)\n",
    "plt.clf()\n",
    "solar_power += [max(0, 1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (i - mu)**2 / (2 * sigma**2)) + random.uniform(-sp_k, sp_k)) for i in bins[:-1]]\n",
    "distribution += [1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (i - mu)**2 / (2 * sigma**2)) for i in bins[:-1]]\n",
    "\n",
    "solar_power += [0.0 for i in range(num_slots - num_slots//2)]\n",
    "distribution += [0.0 for i in range(num_slots - num_slots//2)]\n",
    "\n",
    "mu, sigma = 0, 1.0/(np.sqrt(2 * np.pi) * sp_mx_q4)\n",
    "s = np.random.normal(mu, sigma, num_points)\n",
    "count, bins, ignored = plt.hist(s, num_slots//2, density=True)\n",
    "plt.clf()\n",
    "solar_power += [max(0, 1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (i - mu)**2 / (2 * sigma**2)) + random.uniform(-sp_k, sp_k)) for i in bins[:-1]]\n",
    "distribution += [1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (i - mu)**2 / (2 * sigma**2)) for i in bins[:-1]]\n",
    "\n",
    "\n",
    "slots = range(1,  4*num_slots+1)\n",
    "f = plt.figure()\n",
    "f.set_figwidth(20)\n",
    "f.set_figheight(9)\n",
    "plt.title(\"Solar Power\")\n",
    "plt.xlabel(\"Slot\")\n",
    "plt.ylabel(\"Power (Wh)\")\n",
    "plt.plot(slots, solar_power, \"-o\", label = \"power\")\n",
    "plt.plot(slots, distribution, label = \"distribution\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WDjZvAeCw3-f",
    "outputId": "09dac47a-813d-4ca8-89f5-811d77cb05b0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(7, 0.0),\n",
       " (15.602279727337844, 0.0),\n",
       " (19.26662548892838, 0.0),\n",
       " (35.466597339307334, 0.0),\n",
       " (37.25448101066665, 18.309597674418082),\n",
       " (29.293973053510776, 62.89061381977774),\n",
       " (11.33558823393894, 192.86902154043128),\n",
       " (7.822240423853112, 38.23853090991156)]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states = []\n",
    "for i in range(1, num_slots + 1):\n",
    "    states.append((electricity_prices[i-1], solar_power[i-1]))\n",
    "\n",
    "states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aIJnlbP4zLxJ",
    "outputId": "ac4c7397-d30d-47c7-95ff-5d2ad0c8f751"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Ref': ['on'],\n",
       " 'AS': ['on'],\n",
       " 'AC2': [0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4],\n",
       " 'L1': [0.2, 0.3, 0.4, 0.5, 0.6],\n",
       " 'H': [0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5],\n",
       " 'WM': ['on', 'off'],\n",
       " 'DW': ['on', 'off'],\n",
       " 'AC1': [0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4],\n",
       " 'L2': [0.2, 0.3, 0.4, 0.5, 0.6]}"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agents = appliance_df['Name'].tolist()\n",
    "agent_actions = {}\n",
    "for agent in agents:\n",
    "    agent_data = appliance_df.loc[appliance_df['Name'] == agent]\n",
    "    if (agent_data['Type'] == 'NS').bool():\n",
    "        agent_actions[agent] = ['on']\n",
    "    elif (agent_data['Type'] == 'PS').bool():\n",
    "        pr = agent_data['Power Rating (kWh)'].tolist()[0]\n",
    "        agent_actions[agent] = [round(i, 1) for i in np.arange(pr[0], pr[1] + pr[2], pr[2])]\n",
    "    elif (agent_data['Type'] == 'TS').bool():\n",
    "        agent_actions[agent] = ['on', 'off']\n",
    "agent_actions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0RNZInrJBdUO"
   },
   "outputs": [],
   "source": [
    "def in_slot(t, ts):\n",
    "    if t + 1 >= ts[0] and t + 1 <= ts[1]:\n",
    "            return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7LPxHID_obNN"
   },
   "outputs": [],
   "source": [
    "without_DR_actions = {}\n",
    "for t in range(num_slots):\n",
    "    without_DR_actions[t] = {}\n",
    "    for agent in agents:\n",
    "        without_DR_actions[t][agent] = None\n",
    "        act = None\n",
    "        agent_data = appliance_df.loc[appliance_df['Name'] == agent]\n",
    "        if (agent_data['Type'] == 'NS').bool():\n",
    "            ts = agent_data['Time Slot'].tolist()[0]\n",
    "            if in_slot(t, ts):\n",
    "                pr = agent_data['Power Rating (kWh)'].tolist()[0]\n",
    "                act = 'on'\n",
    "        elif (agent_data['Type'] == 'PS').bool():\n",
    "            ts = agent_data['Time Slot'].tolist()[0]\n",
    "            if in_slot(t, ts):\n",
    "                pr = agent_data['Power Rating (kWh)'].tolist()[0]\n",
    "                act = pr[1]\n",
    "        elif (agent_data['Type'] == 'TS').bool():\n",
    "            ts = agent_data['Time Slot'].tolist()[0]\n",
    "            if in_slot(t, ts):\n",
    "                pr = agent_data['Power Rating (kWh)'].tolist()[0]\n",
    "                if t + 1 == ts[0]:\n",
    "                    act = 'on'\n",
    "                else:\n",
    "                    act = 'off'\n",
    "        without_DR_actions[t][agent] = act"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D6XvCMF2wMXN"
   },
   "source": [
    "# Multi-Agent Q Learning\n",
    "\n",
    "Agents take that action which maximizes their own expected cumulative reward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8klVS5r56Y_l",
    "outputId": "82dd90f7-4b4b-466f-a8d1-168ef8791e8e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-7.834912, -7.934911763757258, {'Ref': 0, 'AS': 0, 'AC2': 0, 'L1': 0, 'H': 0, 'WM': 1, 'DW': 0, 'AC1': 0, 'L2': 0})\n",
      "(0.0, 0.0)\n"
     ]
    }
   ],
   "source": [
    "cols = ['Name', 'Type', 'Diss. Coeff.', 'Power Rating (kWh)', 'Time Slot']\n",
    "\n",
    "def get_reward_DR(st, t, agent, act, started):\n",
    "    reward = 0.0\n",
    "    cost = 0.0\n",
    "    agent_data = appliance_df.loc[appliance_df['Name'] == agent]\n",
    "    if (agent_data['Type'] == 'NS').bool():\n",
    "        ts = agent_data['Time Slot'].tolist()[0]\n",
    "        if in_slot(t, ts):\n",
    "            pr = agent_data['Power Rating (kWh)'].tolist()[0]\n",
    "            reward = (-st[0] * max(0, (pr)))\n",
    "            cost = (-st[0] * max(0, (pr)))\n",
    "    elif (agent_data['Type'] == 'PS').bool():\n",
    "        ts = agent_data['Time Slot'].tolist()[0]\n",
    "        if in_slot(t, ts):\n",
    "            pr = agent_data['Power Rating (kWh)'].tolist()[0]\n",
    "            dc = agent_data['Diss. Coeff.'].tolist()[0]\n",
    "            reward = ((-st[0] * max(0, (act))) - (dc * math.pow(pr[1] - act, 2)))\n",
    "            cost = (-st[0] * max(0, (act)))\n",
    "    elif (agent_data['Type'] == 'TS').bool():\n",
    "        ts = agent_data['Time Slot'].tolist()[0]\n",
    "        if in_slot(t, ts):\n",
    "            pr = agent_data['Power Rating (kWh)'].tolist()[0]\n",
    "            dc = agent_data['Diss. Coeff.'].tolist()[0]\n",
    "            if (act == 'on' or (t + 1 == ts[1])) and started[agent] != 1:\n",
    "                reward = - (st[0] * max(0, pr) - (dc * math.pow(ts[0] - t - 1, 2)))\n",
    "                cost = (-st[0] * max(0, (pr)))\n",
    "                started[agent] = 1\n",
    "    return round(reward, 6), cost, started\n",
    "\n",
    "def get_reward_without_DR(st, t, agent):\n",
    "    reward = 0.0\n",
    "    cost = 0.0\n",
    "    agent_data = appliance_df.loc[appliance_df['Name'] == agent]\n",
    "    if (agent_data['Type'] == 'NS').bool():\n",
    "        ts = agent_data['Time Slot'].tolist()[0]\n",
    "        if in_slot(t, ts):\n",
    "            pr = agent_data['Power Rating (kWh)'].tolist()[0]\n",
    "            reward = (-st[0] * max(0, (pr)))\n",
    "            cost = (-st[0] * max(0, (pr)))\n",
    "    elif (agent_data['Type'] == 'PS').bool():\n",
    "        ts = agent_data['Time Slot'].tolist()[0]\n",
    "        if in_slot(t, ts):\n",
    "            pr = agent_data['Power Rating (kWh)'].tolist()[0]\n",
    "            reward = (-st[0] * max(0, (pr[1])))\n",
    "            cost = (-st[0] * max(0, (pr[1])))\n",
    "\n",
    "    elif (agent_data['Type'] == 'TS').bool():\n",
    "        ts = agent_data['Time Slot'].tolist()[0]\n",
    "        if in_slot(t, ts):\n",
    "            pr = agent_data['Power Rating (kWh)'].tolist()[0]\n",
    "            if t + 1 == ts[0]:\n",
    "                reward = - (st[0] * max(0, pr))\n",
    "                cost = - (st[0] * max(0, pr))\n",
    "    return round(reward, 6), cost\n",
    "\n",
    "\n",
    "started = {}\n",
    "for agent in agents:\n",
    "        started[agent] = 0\n",
    "print(get_reward_DR(states[6], 6, 'WM', 'on', started))\n",
    "print(get_reward_without_DR(states[6], 6, 'WM'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vkXq_OAwbstd",
    "outputId": "c75bc151-bf73-4e94-cae9-7158a80bf23d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slot:  1\n",
      "Appliance:  Ref\n",
      "Appliance:  AS\n",
      "Appliance:  AC2\n",
      "Appliance:  L1\n",
      "Appliance:  H\n",
      "Appliance:  WM\n",
      "Appliance:  DW\n",
      "Appliance:  AC1\n",
      "Appliance:  L2\n",
      "1\n",
      "Time taken to recommend (s):  6.486686706542969\n",
      "Slot:  1  | Recommendations:  {'Ref': 'on', 'AS': 'on', 'AC2': 1.3, 'L1': None, 'H': 0.5, 'WM': None, 'DW': None, 'AC1': 0.7, 'L2': None}\n",
      "Slot:  2\n",
      "Appliance:  Ref\n",
      "Appliance:  AS\n",
      "Appliance:  AC2\n",
      "Appliance:  L1\n",
      "Appliance:  H\n",
      "Appliance:  WM\n",
      "Appliance:  DW\n",
      "Appliance:  AC1\n",
      "Appliance:  L2\n",
      "1\n",
      "Time taken to recommend (s):  5.2431886196136475\n",
      "Slot:  2  | Recommendations:  {'Ref': 'on', 'AS': 'on', 'AC2': 1.2, 'L1': None, 'H': 0.5, 'WM': None, 'DW': None, 'AC1': 0.7, 'L2': None}\n",
      "Slot:  3\n",
      "Appliance:  Ref\n",
      "Appliance:  AS\n",
      "Appliance:  AC2\n",
      "Appliance:  L1\n",
      "Appliance:  H\n",
      "Appliance:  WM\n",
      "Appliance:  DW\n",
      "Appliance:  AC1\n",
      "Appliance:  L2\n",
      "1\n",
      "Time taken to recommend (s):  3.6918959617614746\n",
      "Slot:  3  | Recommendations:  {'Ref': 'on', 'AS': 'on', 'AC2': 1.2, 'L1': None, 'H': 0.5, 'WM': None, 'DW': None, 'AC1': 0.7, 'L2': None}\n",
      "Slot:  4\n",
      "Appliance:  Ref\n",
      "Appliance:  AS\n",
      "Appliance:  AC2\n",
      "Appliance:  L1\n",
      "Appliance:  H\n",
      "Appliance:  WM\n",
      "Appliance:  DW\n",
      "Appliance:  AC1\n",
      "Appliance:  L2\n",
      "1\n",
      "Time taken to recommend (s):  2.5420291423797607\n",
      "Slot:  4  | Recommendations:  {'Ref': 'on', 'AS': 'on', 'AC2': 1.0, 'L1': None, 'H': 0.5, 'WM': None, 'DW': None, 'AC1': 0.7, 'L2': None}\n",
      "Slot:  5\n",
      "Appliance:  Ref\n",
      "Appliance:  AS\n",
      "Appliance:  AC2\n",
      "Appliance:  L1\n",
      "Appliance:  H\n",
      "Appliance:  WM\n",
      "Appliance:  DW\n",
      "Appliance:  AC1\n",
      "Appliance:  L2\n",
      "1\n",
      "Time taken to recommend (s):  1.3896217346191406\n",
      "Slot:  5  | Recommendations:  {'Ref': 'on', 'AS': 'on', 'AC2': 1.0, 'L1': 0.2, 'H': 0.5, 'WM': None, 'DW': None, 'AC1': 0.7, 'L2': 0.2}\n",
      "Slot:  6\n",
      "Appliance:  Ref\n",
      "Appliance:  AS\n",
      "Appliance:  AC2\n",
      "Appliance:  L1\n",
      "Appliance:  H\n",
      "Appliance:  WM\n",
      "Appliance:  DW\n",
      "Appliance:  AC1\n",
      "Appliance:  L2\n",
      "1\n",
      "Time taken to recommend (s):  0.8107712268829346\n",
      "Slot:  6  | Recommendations:  {'Ref': 'on', 'AS': 'on', 'AC2': 1.1, 'L1': 0.2, 'H': 0.5, 'WM': 'off', 'DW': 'off', 'AC1': 0.7, 'L2': 0.2}\n",
      "Slot:  7\n",
      "Appliance:  Ref\n",
      "Appliance:  AS\n",
      "Appliance:  AC2\n",
      "Appliance:  L1\n",
      "Appliance:  H\n",
      "Appliance:  WM\n",
      "Appliance:  DW\n",
      "Appliance:  AC1\n",
      "Appliance:  L2\n",
      "1\n",
      "Time taken to recommend (s):  0.3780198097229004\n",
      "Slot:  7  | Recommendations:  {'Ref': 'on', 'AS': 'on', 'AC2': 1.3, 'L1': 0.3, 'H': 0.5, 'WM': 'off', 'DW': 'off', 'AC1': 0.7, 'L2': 0.2}\n",
      "Slot:  8\n",
      "Appliance:  Ref\n",
      "Appliance:  AS\n",
      "Appliance:  AC2\n",
      "Appliance:  L1\n",
      "Appliance:  H\n",
      "Appliance:  WM\n",
      "Appliance:  DW\n",
      "Appliance:  AC1\n",
      "Appliance:  L2\n",
      "1\n",
      "Time taken to recommend (s):  0.024461984634399414\n",
      "Slot:  8  | Recommendations:  {'Ref': 'on', 'AS': 'on', 'AC2': 0.7, 'L1': None, 'H': 0.5, 'WM': 'on', 'DW': 'on', 'AC1': 0.7, 'L2': None}\n"
     ]
    }
   ],
   "source": [
    "num_timeslots = num_slots\n",
    "num_episodes = 1000\n",
    "epsilon = 0.3\n",
    "\n",
    "gamma = 0.9\n",
    "theta = 0.3\n",
    "\n",
    "converged = 1\n",
    "threshold = 0.001\n",
    "\n",
    "recommendations = {}\n",
    "actually_started = {}\n",
    "\n",
    "for t in range(num_timeslots):\n",
    "    actually_started[t] = {}\n",
    "    for agent in agents:\n",
    "        actually_started[t][agent] = 0\n",
    "\n",
    "started = {}\n",
    "for agent in agents:\n",
    "        started[agent] = 0\n",
    "\n",
    "Without_DR_total_reward = 0.0\n",
    "With_DR_total_reward = 0.0\n",
    "\n",
    "Without_DR_total_electricity_cost = 0.0\n",
    "With_DR_total_electricity_cost = 0.0\n",
    "\n",
    "average_time = 0\n",
    "\n",
    "for t in range(num_timeslots):\n",
    "    print('Slot: ', t + 1)\n",
    "    start = time.time()\n",
    "    recommendations[t] = {}\n",
    "    Q = {}\n",
    "    Q_prev = {}\n",
    "    R = {}\n",
    "    C = {}\n",
    "    for st in states:\n",
    "        Q[st] = {}\n",
    "        Q_prev[st] = {}\n",
    "        R[st] = {}\n",
    "        C[st] = {}\n",
    "        for agent in agents:\n",
    "            Q[st][agent] = {}\n",
    "            Q_prev[st][agent] = {}\n",
    "            R[st][agent] = {}\n",
    "            C[st][agent] = {}\n",
    "            recommendations[t][agent] = None\n",
    "    converged = 1\n",
    "    for agent in agents:\n",
    "        print('Appliance: ', agent)\n",
    "        for st in states:\n",
    "            for act in agent_actions[agent]:\n",
    "                Q[st][agent][act] = 0.0\n",
    "                Q_prev[st][agent][act] = 0.0\n",
    "        if t > 0:\n",
    "            started[agent] = actually_started[t - 1][agent]\n",
    "        else:\n",
    "            started[agent] = 0\n",
    "        for eps in range(num_episodes):\n",
    "            if t > 0:\n",
    "                if actually_started[t-1][agent] != 1:\n",
    "                    started[agent] = 0\n",
    "            else:\n",
    "                started[agent] = 0\n",
    "            Q_prev = deepcopy(Q)\n",
    "            st = states[t]\n",
    "            itr = 0\n",
    "            curr_t = t\n",
    "            while curr_t < num_slots - 1:\n",
    "                p = np.random.random()\n",
    "                if p < epsilon:\n",
    "                    j = np.random.choice(len(agent_actions[agent]))\n",
    "                else:\n",
    "                    j = np.argmax([Q[st][agent][a] for a in agent_actions[agent]])\n",
    "                at = agent_actions[agent][j]\n",
    "                sdash = states[curr_t + 1]\n",
    "                R[st][agent][at], C[st][agent][at], started = get_reward_DR(st, curr_t, agent, at, started)\n",
    "                Q[st][agent][at] = Q[st][agent][at] + theta * (R[st][agent][at] + gamma * max([Q[sdash][agent][act] for act in agent_actions[agent]]) - Q[st][agent][at]) \n",
    "                st = sdash\n",
    "                curr_t += 1\n",
    "            converged = 1\n",
    "            for s in states:\n",
    "                for a in agent_actions[agent]:\n",
    "                    if abs(Q[s][agent][a] - Q_prev[s][agent][a]) > threshold:\n",
    "                        converged = 0\n",
    "            if converged == 1:\n",
    "                conv_eps = eps\n",
    "                break\n",
    "        recommendations[t][agent] = agent_actions[agent][np.argmax([Q[states[t]][agent][a] for a in agent_actions[agent]])]\n",
    "        agent_data = appliance_df.loc[appliance_df['Name'] == agent]\n",
    "        slot = agent_data['Time Slot'].tolist()[0]\n",
    "        if not in_slot(t, slot):\n",
    "            recommendations[t][agent] = None\n",
    "        if (appliance_df.loc[appliance_df['Name'] == agent]['Type'] == 'TS').bool() and actually_started[t][agent] == 1:\n",
    "            recommendations[t][agent] = 'off'\n",
    "        if recommendations[t][agent] == 'on' and (appliance_df.loc[appliance_df['Name'] == agent]['Type'] == 'TS').bool():\n",
    "            if t + 1 < num_timeslots:\n",
    "                actually_started[t + 1][agent] = 1\n",
    "        if recommendations[t][agent] == 'off' and (appliance_df.loc[appliance_df['Name'] == agent]['Type'] == 'TS').bool() and actually_started[t][agent] == 0 and t + 1 ==  slot[1]:\n",
    "            if t + 1 < num_timeslots: \n",
    "                actually_started[t + 1][agent] = 1\n",
    "            recommendations[t][agent] = 'on'\n",
    "    print(converged)\n",
    "    end = time.time()\n",
    "    print('Time taken to recommend (s): ', end-start)\n",
    "    average_time += (end-start)\n",
    "    print(\"Slot: \", t + 1, \" | Recommendations: \", recommendations[t])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "U5esXtLv6_FL"
   },
   "outputs": [],
   "source": [
    "Appliance_Without_DR_Cost = {}\n",
    "Appliance_With_DR_Cost = {}\n",
    "\n",
    "Timeslot_Without_DR_Cost = {}\n",
    "Timeslot_With_DR_Cost = {}\n",
    "\n",
    "Appliance_Without_DR_Reward = {}\n",
    "Appliance_With_DR_Reward = {}\n",
    "\n",
    "Without_DR_total_reward = 0\n",
    "With_DR_total_reward = 0\n",
    "Without_DR_total_electricity_cost = 0\n",
    "With_DR_total_electricity_cost = 0\n",
    "\n",
    "for agent in agents:\n",
    "    Appliance_Without_DR_Cost[agent] = 0\n",
    "    Appliance_With_DR_Cost[agent] = 0\n",
    "    Appliance_Without_DR_Reward[agent] = 0\n",
    "    Appliance_With_DR_Reward[agent] = 0\n",
    "\n",
    "for t in range(num_timeslots):\n",
    "    Timeslot_Without_DR_Cost[t] = 0\n",
    "    Timeslot_With_DR_Cost[t] = 0\n",
    "    for agent in agents:\n",
    "        temp = deepcopy(actually_started)\n",
    "\n",
    "        rew, c, actually_started[t] = get_reward_DR(states[t], t, agent, recommendations[t][agent], actually_started[t])\n",
    "        actually_started = temp\n",
    "        With_DR_total_reward += rew\n",
    "        Appliance_With_DR_Reward[agent] -= rew\n",
    "        With_DR_total_electricity_cost += c\n",
    "        Appliance_With_DR_Cost[agent] -= c\n",
    "        Timeslot_With_DR_Cost[t] -= c\n",
    "\n",
    "        rew, c = get_reward_without_DR(states[t], t, agent)\n",
    "        Without_DR_total_reward += rew\n",
    "        Appliance_Without_DR_Reward[agent] -= rew\n",
    "        Without_DR_total_electricity_cost += c\n",
    "        Appliance_Without_DR_Cost[agent] -= c\n",
    "        Timeslot_Without_DR_Cost[t] -= c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "K7ZOAPVKvUrd",
    "outputId": "e9864355-ef73-4903-de27-c5416f402d29"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slot:  1  | Recommendations:  {'Ref': 'on', 'AS': 'on', 'AC2': 1.3, 'L1': None, 'H': 0.5, 'WM': None, 'DW': None, 'AC1': 0.7, 'L2': None}\n",
      "Slot:  1  | Original Actions:  {'Ref': 'on', 'AS': 'on', 'AC2': 1.4, 'L1': None, 'H': 1.5, 'WM': None, 'DW': None, 'AC1': 1.4, 'L2': None}\n",
      "\n",
      "Slot:  2  | Recommendations:  {'Ref': 'on', 'AS': 'on', 'AC2': 1.2, 'L1': None, 'H': 0.5, 'WM': None, 'DW': None, 'AC1': 0.7, 'L2': None}\n",
      "Slot:  2  | Original Actions:  {'Ref': 'on', 'AS': 'on', 'AC2': 1.4, 'L1': None, 'H': 1.5, 'WM': None, 'DW': None, 'AC1': 1.4, 'L2': None}\n",
      "\n",
      "Slot:  3  | Recommendations:  {'Ref': 'on', 'AS': 'on', 'AC2': 1.2, 'L1': None, 'H': 0.5, 'WM': None, 'DW': None, 'AC1': 0.7, 'L2': None}\n",
      "Slot:  3  | Original Actions:  {'Ref': 'on', 'AS': 'on', 'AC2': 1.4, 'L1': None, 'H': 1.5, 'WM': None, 'DW': None, 'AC1': 1.4, 'L2': None}\n",
      "\n",
      "Slot:  4  | Recommendations:  {'Ref': 'on', 'AS': 'on', 'AC2': 1.0, 'L1': None, 'H': 0.5, 'WM': None, 'DW': None, 'AC1': 0.7, 'L2': None}\n",
      "Slot:  4  | Original Actions:  {'Ref': 'on', 'AS': 'on', 'AC2': 1.4, 'L1': None, 'H': 1.5, 'WM': None, 'DW': None, 'AC1': 1.4, 'L2': None}\n",
      "\n",
      "Slot:  5  | Recommendations:  {'Ref': 'on', 'AS': 'on', 'AC2': 1.0, 'L1': 0.2, 'H': 0.5, 'WM': None, 'DW': None, 'AC1': 0.7, 'L2': 0.2}\n",
      "Slot:  5  | Original Actions:  {'Ref': 'on', 'AS': 'on', 'AC2': 1.4, 'L1': 0.6, 'H': 1.5, 'WM': None, 'DW': None, 'AC1': 1.4, 'L2': 0.6}\n",
      "\n",
      "Slot:  6  | Recommendations:  {'Ref': 'on', 'AS': 'on', 'AC2': 1.1, 'L1': 0.2, 'H': 0.5, 'WM': 'off', 'DW': 'off', 'AC1': 0.7, 'L2': 0.2}\n",
      "Slot:  6  | Original Actions:  {'Ref': 'on', 'AS': 'on', 'AC2': 1.4, 'L1': 0.6, 'H': 1.5, 'WM': 'on', 'DW': 'on', 'AC1': 1.4, 'L2': 0.6}\n",
      "\n",
      "Slot:  7  | Recommendations:  {'Ref': 'on', 'AS': 'on', 'AC2': 1.3, 'L1': 0.3, 'H': 0.5, 'WM': 'off', 'DW': 'off', 'AC1': 0.7, 'L2': 0.2}\n",
      "Slot:  7  | Original Actions:  {'Ref': 'on', 'AS': 'on', 'AC2': 1.4, 'L1': 0.6, 'H': 1.5, 'WM': 'off', 'DW': 'off', 'AC1': 1.4, 'L2': 0.6}\n",
      "\n",
      "Slot:  8  | Recommendations:  {'Ref': 'on', 'AS': 'on', 'AC2': 0.7, 'L1': None, 'H': 0.5, 'WM': 'on', 'DW': 'on', 'AC1': 0.7, 'L2': None}\n",
      "Slot:  8  | Original Actions:  {'Ref': 'on', 'AS': 'on', 'AC2': 1.4, 'L1': None, 'H': 1.5, 'WM': 'off', 'DW': 'off', 'AC1': 1.4, 'L2': None}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for t in range(num_timeslots):\n",
    "    print(\"Slot: \", t + 1, \" | Recommendations: \", recommendations[t])\n",
    "    print(\"Slot: \", t + 1, \" | Original Actions: \", without_DR_actions[t])\n",
    "    print()"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {
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     "height": 1000
    },
    "id": "indyO1JZbsm9",
    "outputId": "ca43009b-cc65-45ad-daba-68bf539d1733"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Appliance:  Ref  | Dis. Coef.:  100.0\n"
     ]
    },
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       "   Slot Action without DR Action with DR  Elec. Price ($/MWk)\n",
       "0     1                on             on             7.000000\n",
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       "2     3                on             on            19.266625\n",
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       "   Slot  Action without DR  Action with DR  Elec. Price ($/MWk)\n",
       "0     1                1.5             0.5             7.000000\n",
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       "3     4                1.5             0.5            35.466597\n",
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       "   Slot Action without DR Action with DR  Elec. Price ($/MWk)\n",
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       "   Slot Action without DR Action with DR  Elec. Price ($/MWk)\n",
       "0     1              None           None             7.000000\n",
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       "2     3              None           None            19.266625\n",
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       "   Slot  Action without DR  Action with DR  Elec. Price ($/MWk)\n",
       "0     1                1.4             0.7             7.000000\n",
       "1     2                1.4             0.7            15.602280\n",
       "2     3                1.4             0.7            19.266625\n",
       "3     4                1.4             0.7            35.466597\n",
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    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Appliance:  L2  | Dis. Coef.:  0.03\n"
     ]
    },
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
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       "\n",
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       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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      ],
      "text/plain": [
       "   Slot  Action without DR  Action with DR  Elec. Price ($/MWk)\n",
       "0     1                NaN             NaN             7.000000\n",
       "1     2                NaN             NaN            15.602280\n",
       "2     3                NaN             NaN            19.266625\n",
       "3     4                NaN             NaN            35.466597\n",
       "4     5                0.6             0.2            37.254481\n",
       "5     6                0.6             0.2            29.293973\n",
       "6     7                0.6             0.2            11.335588\n",
       "7     8                NaN             NaN             7.822240"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "recom_data = {}\n",
    "Recom_df = {}\n",
    "for agent in agents:\n",
    "    print('\\n\\nAppliance: ', agent, ' | Dis. Coef.: ', appliance_df.loc[appliance_df['Name'] == agent]['Diss. Coeff.'].tolist()[0])\n",
    "    recom_data[agent] = [[t+1, without_DR_actions[t][agent], recommendations[t][agent], states[t][0]] for t in range(num_timeslots)]\n",
    "    Recom_df[agent] = pd.DataFrame(recom_data[agent], columns=['Slot', 'Action without DR', 'Action with DR', 'Elec. Price ($/MWk)'])\n",
    "    display(Recom_df[agent])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4BFFXUCRnEqC",
    "outputId": "d103ba8a-f357-48c1-a219-f5dccc067ab3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(7, 0.0),\n",
       " (15.602279727337844, 0.0),\n",
       " (19.26662548892838, 0.0),\n",
       " (35.466597339307334, 0.0),\n",
       " (37.25448101066665, 18.309597674418082),\n",
       " (29.293973053510776, 62.89061381977774),\n",
       " (11.33558823393894, 192.86902154043128),\n",
       " (7.822240423853112, 38.23853090991156)]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 332
    },
    "id": "RwLFYz03nFCl",
    "outputId": "90295226-2c71-4405-a1a3-cab9a91526e7"
   },
   "outputs": [
    {
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       "      <td>WM</td>\n",
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       "      <th>8</th>\n",
       "      <td>DW</td>\n",
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       "      <td>0.06</td>\n",
       "      <td>0.3</td>\n",
       "      <td>[6, 8]</td>\n",
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       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>L2</td>\n",
       "      <td>PS</td>\n",
       "      <td>0.03</td>\n",
       "      <td>[0.2, 0.6, 0.1]</td>\n",
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       "                                                     [key], {});\n",
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       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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       "  "
      ],
      "text/plain": [
       "  Name Type  Diss. Coeff. Power Rating (kWh) Time Slot\n",
       "0  Ref   NS        100.00                0.5    [1, 8]\n",
       "1   AS   NS        100.00                0.1    [1, 8]\n",
       "3  AC2   PS         50.00    [0.7, 1.4, 0.1]    [1, 8]\n",
       "5   L1   PS         20.00    [0.2, 0.6, 0.1]    [5, 7]\n",
       "4    H   PS          0.12    [0.5, 1.5, 0.1]    [1, 8]\n",
       "7   WM   TS          0.10                0.7    [6, 8]\n",
       "8   DW   TS          0.06                0.3    [6, 8]\n",
       "2  AC1   PS          0.05    [0.7, 1.4, 0.1]    [1, 8]\n",
       "6   L2   PS          0.03    [0.2, 0.6, 0.1]    [5, 7]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appliance_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hh4hmwXcwk4i"
   },
   "source": [
    "## Assuming Recommendations are followed, cost with and w/o Demand Response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jWLQ3MN_tGiu",
    "outputId": "ed279d20-0ee7-4fa9-f740-ef37954f3d03"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Total reward without DR:  -921.6595769999998\n",
      "Total reward with DR:  -568.414001\n",
      "Total electricity cost without DR:  921.6595716712114\n",
      "Total electricity cost with DR:  509.68359763524415\n",
      "Average Recommendation Time:  2.5708343982696533\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('\\n\\nTotal reward without DR: ', Without_DR_total_reward)\n",
    "print('Total reward with DR: ', With_DR_total_reward)\n",
    "\n",
    "print('Total electricity cost without DR: ', -Without_DR_total_electricity_cost)\n",
    "print('Total electricity cost with DR: ', -With_DR_total_electricity_cost)\n",
    "\n",
    "print('Average Recommendation Time: ', average_time / num_timeslots)\n",
    "\n",
    "print()\n",
    "print()\n",
    "\n",
    "dc = appliance_df['Diss. Coeff.'].tolist()\n",
    "DC = {}\n",
    "i = 0\n",
    "for agent in agents:\n",
    "    DC[agent] = dc[i]\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "4q3Of0ltsZQn",
    "outputId": "c417a3c7-60d5-45da-d45c-fa925925b85d"
   },
   "outputs": [
    {
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       "      <th>Name</th>\n",
       "      <th>Cost without DR (cents)</th>\n",
       "      <th>Cost with DR (cents)</th>\n",
       "      <th>Diss. Coef.</th>\n",
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       "      <th>Reward with DR</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ref</td>\n",
       "      <td>81.520893</td>\n",
       "      <td>81.520893</td>\n",
       "      <td>100.0</td>\n",
       "      <td>81.520894</td>\n",
       "      <td>81.520894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AS</td>\n",
       "      <td>16.304179</td>\n",
       "      <td>16.304179</td>\n",
       "      <td>100.0</td>\n",
       "      <td>16.304179</td>\n",
       "      <td>16.304179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AC2</td>\n",
       "      <td>228.258499</td>\n",
       "      <td>176.098968</td>\n",
       "      <td>50.0</td>\n",
       "      <td>228.258500</td>\n",
       "      <td>226.098968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L1</td>\n",
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       "      <td>20.0</td>\n",
       "      <td>46.730426</td>\n",
       "      <td>24.910367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>H</td>\n",
       "      <td>244.562678</td>\n",
       "      <td>81.520893</td>\n",
       "      <td>0.12</td>\n",
       "      <td>244.562679</td>\n",
       "      <td>82.480894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>WM</td>\n",
       "      <td>20.505781</td>\n",
       "      <td>5.475568</td>\n",
       "      <td>0.1</td>\n",
       "      <td>20.505781</td>\n",
       "      <td>5.075568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DW</td>\n",
       "      <td>8.788192</td>\n",
       "      <td>2.346672</td>\n",
       "      <td>0.06</td>\n",
       "      <td>8.788192</td>\n",
       "      <td>2.106672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AC1</td>\n",
       "      <td>228.258499</td>\n",
       "      <td>114.129250</td>\n",
       "      <td>0.05</td>\n",
       "      <td>228.258500</td>\n",
       "      <td>114.325250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>L2</td>\n",
       "      <td>46.730425</td>\n",
       "      <td>15.576808</td>\n",
       "      <td>0.03</td>\n",
       "      <td>46.730426</td>\n",
       "      <td>15.591209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Total</td>\n",
       "      <td>921.659572</td>\n",
       "      <td>509.683598</td>\n",
       "      <td>-</td>\n",
       "      <td>-921.659577</td>\n",
       "      <td>-568.414001</td>\n",
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       "\n",
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       "            + ' to learn more about interactive tables.';\n",
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      "text/plain": [
       "    Name  Cost without DR (cents)  Cost with DR (cents) Diss. Coef.  \\\n",
       "0    Ref                81.520893             81.520893       100.0   \n",
       "1     AS                16.304179             16.304179       100.0   \n",
       "2    AC2               228.258499            176.098968        50.0   \n",
       "3     L1                46.730425             16.710367        20.0   \n",
       "4      H               244.562678             81.520893        0.12   \n",
       "5     WM                20.505781              5.475568         0.1   \n",
       "6     DW                 8.788192              2.346672        0.06   \n",
       "7    AC1               228.258499            114.129250        0.05   \n",
       "8     L2                46.730425             15.576808        0.03   \n",
       "9  Total               921.659572            509.683598           -   \n",
       "\n",
       "   Reward without DR  Reward with DR  \n",
       "0          81.520894       81.520894  \n",
       "1          16.304179       16.304179  \n",
       "2         228.258500      226.098968  \n",
       "3          46.730426       24.910367  \n",
       "4         244.562679       82.480894  \n",
       "5          20.505781        5.075568  \n",
       "6           8.788192        2.106672  \n",
       "7         228.258500      114.325250  \n",
       "8          46.730426       15.591209  \n",
       "9        -921.659577     -568.414001  "
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [[agent, Appliance_Without_DR_Cost[agent], Appliance_With_DR_Cost[agent], DC[agent], Appliance_Without_DR_Reward[agent], Appliance_With_DR_Reward[agent]] for agent in agents]\n",
    "  \n",
    "data.append(['Total', -Without_DR_total_electricity_cost, -With_DR_total_electricity_cost, '-', Without_DR_total_reward, With_DR_total_reward])\n",
    "DR_df = pd.DataFrame(data, columns=['Name', 'Cost without DR (cents)', 'Cost with DR (cents)', 'Diss. Coef.', 'Reward without DR', 'Reward with DR'])\n",
    "DR_df"
   ]
  },
  {
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       "      <th>Cost without DR (cents)</th>\n",
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       "      <td>119.214339</td>\n",
       "      <td>37.254481</td>\n",
       "      <td>6.1</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>207.987209</td>\n",
       "      <td>96.670111</td>\n",
       "      <td>29.293973</td>\n",
       "      <td>7.1</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>69.147088</td>\n",
       "      <td>40.808118</td>\n",
       "      <td>11.335588</td>\n",
       "      <td>6.1</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>38.328978</td>\n",
       "      <td>27.377841</td>\n",
       "      <td>7.822240</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-01172d50-e8dc-49d9-acff-8bc646419a6c')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-01172d50-e8dc-49d9-acff-8bc646419a6c button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-01172d50-e8dc-49d9-acff-8bc646419a6c');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   Slot  Cost without DR (cents)  Cost with DR (cents)  Elec. Price ($/MWk)  \\\n",
       "0     1                34.300000             21.700000             7.000000   \n",
       "1     2                76.451171             46.806839            15.602280   \n",
       "2     3                94.406465             57.799876            19.266625   \n",
       "3     4               173.786327             99.306473            35.466597   \n",
       "4     5               227.252334            119.214339            37.254481   \n",
       "5     6               207.987209             96.670111            29.293973   \n",
       "6     7                69.147088             40.808118            11.335588   \n",
       "7     8                38.328978             27.377841             7.822240   \n",
       "\n",
       "   Power Con. without DR (kWh)  Power Con. with DR (kWh)  \n",
       "0                          4.9                       3.1  \n",
       "1                          4.9                       3.0  \n",
       "2                          4.9                       3.0  \n",
       "3                          4.9                       2.8  \n",
       "4                          6.1                       3.2  \n",
       "5                          7.1                       3.3  \n",
       "6                          6.1                       3.6  \n",
       "7                          4.9                       3.5  "
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_data = [[t+1, Timeslot_Without_DR_Cost[t], Timeslot_With_DR_Cost[t], states[t][0], Timeslot_Without_DR_Cost[t]/states[t][0], Timeslot_With_DR_Cost[t]/states[t][0]] for t in range(num_timeslots)]\n",
    "TS_df = pd.DataFrame(ts_data, columns=['Slot', 'Cost without DR (cents)', 'Cost with DR (cents)', 'Elec. Price ($/MWk)', 'Power Con. without DR (kWh)', 'Power Con. with DR (kWh)'])\n",
    "\n",
    "TS_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "id": "nj4nERqdyDcr",
    "outputId": "300f7c2f-c132-4ff1-ded8-54bf715eefd9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5d463b8bd0>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prices  = []\n",
    "without_DR = []\n",
    "with_DR = []\n",
    "for t in range(num_timeslots):\n",
    "    prices.append(states[t][0])\n",
    "    with_DR.append(Timeslot_With_DR_Cost[t])\n",
    "    without_DR.append(Timeslot_Without_DR_Cost[t])\n",
    "plt.title('Electricity Cost with and without DR')\n",
    "plt.xlabel('Slot')\n",
    "plt.ylabel('Price')\n",
    "plt.plot(range(num_timeslots), without_DR, label=\"Cost without DR\")\n",
    "plt.plot(range(num_timeslots), with_DR, label=\"Cost with DR\", color='g')\n",
    "plt.plot(range(num_timeslots), prices, label=\"Electricity Prices\", color='r')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IULjkW3lj5AN"
   },
   "source": [
    "### Hence, we see a 40-50% cost saving with DR than without DR."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FaZja7N1wDhY"
   },
   "source": [
    "# EAQR: Parallelization\n",
    "\n",
    "Repeated Game formulation\n",
    "\n",
    "**Version 1**: Fully co-operative scenario where the agents try to maximize the global immediate reward.\n",
    "\n",
    "**Version 2**: Fully co-operative scenario where the agents try to maximize the global expected reward.\n",
    "\n",
    "**Additional Goal (Cooperation)**: To account for safety and ensure that power consumption doesn't exceed a limit at a particular timeslot. Previous solution doesn't account for such case and might give a solution where all agents are 'on' at a timeslot together, which may not be desirable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RA21AIfBCBH9"
   },
   "source": [
    "### Let us first look at small problem and see how EAQR applies to it in this scenario."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T5YHnf1eNa_d"
   },
   "source": [
    "We want to get maimum global immediate reward at timeslot t=6 and appliances are 'AC1' and 'L2'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 112
    },
    "id": "NisUhEsSVai7",
    "outputId": "c57cee76-4d0e-464d-95dc-57e2f96162a9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-ad6338cb-5a4d-46d9-8c23-b3edf1f0124a\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>dc</th>\n",
       "      <th>actions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AC1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[1.0, 1.4]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>[0.6, 0.8]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ad6338cb-5a4d-46d9-8c23-b3edf1f0124a')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
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       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
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       "\n",
       "    .colab-df-convert {\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
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       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
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       "\n",
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       "\n",
       "      <script>\n",
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       "          document.querySelector('#df-ad6338cb-5a4d-46d9-8c23-b3edf1f0124a button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-ad6338cb-5a4d-46d9-8c23-b3edf1f0124a');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "  name   dc     actions\n",
       "0  AC1  1.0  [1.0, 1.4]\n",
       "1   L2  0.4  [0.6, 0.8]"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_data = [['AC1', 1.0, [1.0, 1.4]], ['L2', 0.4, [0.6, 0.8]]]\n",
    "agts = ['AC1', 'L2']\n",
    "app_df = pd.DataFrame(app_data, columns=['name', 'dc', 'actions'])\n",
    "app_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hfT8e5ZwBSIp"
   },
   "outputs": [],
   "source": [
    "def get_small_reward_with_DR(t, agent, act):\n",
    "    reward = 0.0\n",
    "    cost = 0.0\n",
    "    max_pow = app_df.loc[app_df['name'] == agent]['actions'].tolist()[0][-1]\n",
    "    dc = max_pow = app_df.loc[app_df['name'] == agent]['dc'].tolist()[0]\n",
    "    reward = ((-states[t][0] * max(0, (act))) - (dc * math.pow(max_pow - act, 2)))\n",
    "    cost = (-states[t][0] * max(0, (act)))\n",
    "    return round(reward, 6), cost, act\n",
    "\n",
    "def get_small_reward_without_DR(t, agent, act):\n",
    "    reward = 0.0\n",
    "    cost = 0.0\n",
    "    reward = (-states[t][0] * max(0, (act)))\n",
    "    cost = (-states[t][0] * max(0, (act)))\n",
    "    return round(reward, 6), cost, act\n",
    "\n",
    "def get_small_global_reward_with_DR(t, actions, penalty_factor, limit):\n",
    "    global_reward = 0\n",
    "    total_power = 0\n",
    "    for agent in agts:\n",
    "        act = actions[agent]\n",
    "        global_reward += get_small_reward_with_DR(t, agent, act)[0]\n",
    "        total_power += get_small_reward_with_DR(t, agent, act)[2]\n",
    "    if total_power > limit:\n",
    "        global_reward -= penalty_factor * (total_power - limit)\n",
    "    return global_reward\n",
    "\n",
    "def get_small_global_reward_without_DR(t, actions, penalty_factor, limit):\n",
    "    global_reward = 0\n",
    "    total_power = 0\n",
    "    for agent in agts:\n",
    "        act = actions[agent]\n",
    "        global_reward += get_small_reward_without_DR(t, agent, act)[0]\n",
    "        total_power += get_small_reward_without_DR(t, agent, act)[2]\n",
    "    if total_power > limit:\n",
    "        global_reward -= penalty_factor * (total_power - limit)\n",
    "    return global_reward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6jPtnrweBSEP"
   },
   "outputs": [],
   "source": [
    "penalty = 0.9\n",
    "lmt = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_J_2Z41DBSB-",
    "outputId": "1250510e-e46a-4b53-bd07-f52c1cdee9a3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slot:  0\n",
      "+------------------+-------------------+-------------------+\n",
      "| Payoffs          |   L2-action1: 0.6 |   L2-action2: 0.8 |\n",
      "+==================+===================+===================+\n",
      "| AC1-action1: 1.0 |           -11.216 |          -20.4681 |\n",
      "+------------------+-------------------+-------------------+\n",
      "| AC1-action2: 1.4 |           -14.176 |          -25.3423 |\n",
      "+------------------+-------------------+-------------------+\n",
      "Slot:  1\n",
      "+------------------+-------------------+-------------------+\n",
      "| Payoffs          |   L2-action1: 0.6 |   L2-action2: 0.8 |\n",
      "+==================+===================+===================+\n",
      "| AC1-action1: 1.0 |          -24.9796 |          -20.4681 |\n",
      "+------------------+-------------------+-------------------+\n",
      "| AC1-action2: 1.4 |          -31.3806 |          -25.3423 |\n",
      "+------------------+-------------------+-------------------+\n",
      "Slot:  2\n",
      "+------------------+-------------------+-------------------+\n",
      "| Payoffs          |   L2-action1: 0.6 |   L2-action2: 0.8 |\n",
      "+==================+===================+===================+\n",
      "| AC1-action1: 1.0 |          -30.8426 |          -20.4681 |\n",
      "+------------------+-------------------+-------------------+\n",
      "| AC1-action2: 1.4 |          -38.7093 |          -25.3423 |\n",
      "+------------------+-------------------+-------------------+\n",
      "Slot:  3\n",
      "+------------------+-------------------+-------------------+\n",
      "| Payoffs          |   L2-action1: 0.6 |   L2-action2: 0.8 |\n",
      "+==================+===================+===================+\n",
      "| AC1-action1: 1.0 |          -56.7626 |          -20.4681 |\n",
      "+------------------+-------------------+-------------------+\n",
      "| AC1-action2: 1.4 |          -71.1092 |          -25.3423 |\n",
      "+------------------+-------------------+-------------------+\n",
      "Slot:  4\n",
      "+------------------+-------------------+-------------------+\n",
      "| Payoffs          |   L2-action1: 0.6 |   L2-action2: 0.8 |\n",
      "+==================+===================+===================+\n",
      "| AC1-action1: 1.0 |          -59.6232 |          -20.4681 |\n",
      "+------------------+-------------------+-------------------+\n",
      "| AC1-action2: 1.4 |          -74.685  |          -25.3423 |\n",
      "+------------------+-------------------+-------------------+\n",
      "Slot:  5\n",
      "+------------------+-------------------+-------------------+\n",
      "| Payoffs          |   L2-action1: 0.6 |   L2-action2: 0.8 |\n",
      "+==================+===================+===================+\n",
      "| AC1-action1: 1.0 |          -46.8864 |          -20.4681 |\n",
      "+------------------+-------------------+-------------------+\n",
      "| AC1-action2: 1.4 |          -58.7639 |          -25.3423 |\n",
      "+------------------+-------------------+-------------------+\n",
      "Slot:  6\n",
      "+------------------+-------------------+-------------------+\n",
      "| Payoffs          |   L2-action1: 0.6 |   L2-action2: 0.8 |\n",
      "+==================+===================+===================+\n",
      "| AC1-action1: 1.0 |          -18.1529 |          -20.4681 |\n",
      "+------------------+-------------------+-------------------+\n",
      "| AC1-action2: 1.4 |          -22.8472 |          -25.3423 |\n",
      "+------------------+-------------------+-------------------+\n",
      "Slot:  7\n",
      "+------------------+-------------------+-------------------+\n",
      "| Payoffs          |   L2-action1: 0.6 |   L2-action2: 0.8 |\n",
      "+==================+===================+===================+\n",
      "| AC1-action1: 1.0 |          -12.5316 |          -20.4681 |\n",
      "+------------------+-------------------+-------------------+\n",
      "| AC1-action2: 1.4 |          -15.8205 |          -25.3423 |\n",
      "+------------------+-------------------+-------------------+\n"
     ]
    }
   ],
   "source": [
    "from tabulate import tabulate\n",
    "for t in range(num_timeslots):\n",
    "    print('Slot: ', t)\n",
    "    mydata = [\n",
    "        ['AC1-action1: 1.0', get_small_global_reward_with_DR(t, {'AC1': 1.0, 'L2': 0.6}, penalty, lmt), get_small_global_reward_with_DR(t, {'AC1': 1.0, 'L2': 0.8}, penalty, lmt)],\n",
    "        ['AC1-action2: 1.4', get_small_global_reward_with_DR(t, {'AC1': 1.4, 'L2': 0.6}, penalty, lmt), get_small_global_reward_with_DR(t, {'AC1': 1.4, 'L2': 0.8}, penalty, lmt)],\n",
    "    ]\n",
    "    \n",
    "    # create header\n",
    "    head = ['Payoffs', 'L2-action1: 0.6', 'L2-action2: 0.8']\n",
    "    \n",
    "    # display table\n",
    "    print(tabulate(mydata, headers=head, tablefmt=\"grid\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KuYCiCofePO5",
    "outputId": "00e7dd4d-3ee3-4f12-95df-4613a1914497"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "converged 23027\n",
      "converged 45348\n",
      "converged 3029\n",
      "converged 7069\n",
      "converged 10503\n",
      "converged 8685\n",
      "converged 3433\n",
      "converged 54135\n",
      "Average Recommendation Time:  177.56671634316444\n"
     ]
    }
   ],
   "source": [
    "max_games = 100000\n",
    "num_games = 100\n",
    "alpha = 0.5\n",
    "eps = 0.5\n",
    "\n",
    "recomm = {}\n",
    "avg_time = 0\n",
    "\n",
    "for t in range(num_timeslots):\n",
    "    start = time.time()\n",
    "    recomm[t] = {}\n",
    "    freq = {}\n",
    "    Q = {}\n",
    "    p = {}\n",
    "    immediate_local_rewards = {}\n",
    "    my_action_history = {}\n",
    "    for agent in agts:\n",
    "        my_action_history[agent] = {}\n",
    "        freq[agent] = {}\n",
    "        Q[agent] = {}\n",
    "        p[agent] = {}\n",
    "    flag = 0\n",
    "    game_count = -1\n",
    "    max_immediate_local_reward = float('-inf')\n",
    "    converged = 0\n",
    "    for agent in agts:\n",
    "        recomm[t][agent] = None\n",
    "        actions = app_df.loc[app_df['name'] == agent]['actions'].tolist()[0]\n",
    "        for act in actions:\n",
    "            freq[agent][act] = 0\n",
    "            Q[agent][act] = random.random()\n",
    "            p[agent][act] = 0 \n",
    "\n",
    "    for game in range(max_games):  \n",
    "        game_count += 1\n",
    "        immediate_local_rewards[game_count] = {}\n",
    "        for agent in agts:\n",
    "            immediate_local_rewards[game_count][agent] = {}  \n",
    "            actions = app_df.loc[app_df['name'] == agent]['actions'].tolist()[0]  \n",
    "            sum_Q = 0\n",
    "            for a in actions:\n",
    "                sum_Q += Q[agent][a]\n",
    "            probs = []\n",
    "            for act in actions:\n",
    "                prob_list = [1/len(actions), Q[agent][act]/sum_Q]\n",
    "                p[agent][act] = random.choices(prob_list, weights=(eps, 1-eps))[0]\n",
    "                probs.append(p[agent][act])\n",
    "            probs = [int(i*1000) for i in probs]\n",
    "            action = random.choices(actions, weights=tuple(probs))[0]\n",
    "            my_action_history[agent][game_count] = action\n",
    "            immediate_local_rewards[game_count][agent]['reward'] = get_small_reward_with_DR(t, agent, action)[0]\n",
    "            immediate_local_rewards[game_count][agent]['action'] = get_small_reward_with_DR(t, agent, action)[2]\n",
    "            if game_count == num_games:\n",
    "                converged = 1\n",
    "                for a in actions:\n",
    "                    num_act_in_last_ns = 0\n",
    "                    num_max_in_last_ns = 0\n",
    "                    for game_no in range(num_games):\n",
    "                        if my_action_history[agent][game_no] == a:\n",
    "                            num_act_in_last_ns += 1\n",
    "                            player_actions = {}\n",
    "                            for ag in agts:\n",
    "                                player_actions[ag] = immediate_local_rewards[game_no][ag]['action']\n",
    "                            temp = get_small_global_reward_with_DR(t, player_actions, penalty, lmt)\n",
    "                            # print(max_immediate_local_reward, temp, player_actions)\n",
    "                            if abs(temp - max_immediate_local_reward) < 0.0001:\n",
    "                                num_max_in_last_ns += 1\n",
    "                            elif temp > max_immediate_local_reward:\n",
    "                                max_immediate_local_reward = temp\n",
    "                                num_max_in_last_ns = 0\n",
    "                    if num_act_in_last_ns > 0:\n",
    "                        freq[agent][a] = num_max_in_last_ns / num_act_in_last_ns\n",
    "                    if abs(alpha * (freq[agent][a] - Q[agent][a])) > 0.0001:\n",
    "                        converged = 0\n",
    "                    Q[agent][a] = Q[agent][a] + alpha * (freq[agent][a] - Q[agent][a])\n",
    "                    flag = 1\n",
    "        if flag == 1:\n",
    "            if converged == 1:\n",
    "                print('converged', game)\n",
    "                break\n",
    "            game_count = -1\n",
    "            flag = 0\n",
    "            for ag in agents:\n",
    "                my_action_history[agent] = {}\n",
    "\n",
    "    for a in agts:\n",
    "        recomm[t][a] = max(Q[a], key=Q[a].get)\n",
    "    end = time.time()\n",
    "    avg_time += (end-start)\n",
    "print('Average Recommendation Time: ', avg_time/num_timeslots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8UXEaMBH64X9",
    "outputId": "508bf589-fb7b-4aa8-9185-831c77eac9a4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: {'AC1': 1.0, 'L2': 0.6},\n",
       " 1: {'AC1': 1.0, 'L2': 0.8},\n",
       " 2: {'AC1': 1.0, 'L2': 0.8},\n",
       " 3: {'AC1': 1.0, 'L2': 0.8},\n",
       " 4: {'AC1': 1.0, 'L2': 0.8},\n",
       " 5: {'AC1': 1.0, 'L2': 0.8},\n",
       " 6: {'AC1': 1.0, 'L2': 0.6},\n",
       " 7: {'AC1': 1.0, 'L2': 0.6}}"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recomm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fG1HrUtWDGwD"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3LhldG7ZDGtW"
   },
   "outputs": [],
   "source": []
  }
 ],
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