Server_DoubleQlearning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statistics as stat \n",
    "\n",
    "B = 512;C = 10000;W = 1.4 * pow(10,6)\n",
    "f_I = pow(10,9);f_M = pow(10,12)\n",
    "N_0 = pow(10,-16);P = 0.5 \n",
    "\n",
    "def arrival_and_departure_times(param1,param2,N):\n",
    "    a = []\n",
    "    d = []\n",
    "    s = 0\n",
    "    for i in range(N):\n",
    "        s = s + np.random.exponential(param1)\n",
    "        a.append(s)\n",
    "    for i in range(N):\n",
    "        d.append(a[i] + np.random.exponential(param2))\n",
    "    return (np.array(a),np.array(d))\n",
    "\n",
    "def local_cost():\n",
    "    return ((B*C)/f_I)\n",
    "\n",
    "def cloud_cost(n):\n",
    "    h_g_2 = np.random.exponential(1)\n",
    "    R = W * np.log2(1 + P*h_g_2/N_0)\n",
    "    #R = np.random.choice([R,R/5],p = [0.5,0.5])\n",
    "    return (B/R + B*C*n/f_M)\n",
    "\n",
    "def next_state(local_pos,cloud_pos,action):\n",
    "    if action == 0:\n",
    "        (new_local_pos,new_cloud_pos,reward) = (local_pos + 1,cloud_pos,-local_cost())\n",
    "    else:\n",
    "        (new_local_pos,new_cloud_pos,reward) = (local_pos,cloud_pos + 1,-cloud_cost(cloud_pos))\n",
    "    return  (new_local_pos,new_cloud_pos,reward)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def Server_DoubleQ_learning(N):\n",
    "    gamma = 0.9\n",
    "    n = 50\n",
    "    reward_list = []\n",
    "    reward_sum_list = []\n",
    "    reward_sum_avg_list = np.array([0]*N)\n",
    "    runs = 1\n",
    "    mu = 1/20;lamda = 1\n",
    "    \n",
    "    for ind in range(runs):\n",
    "        reward_sum = 0\n",
    "        count = 0\n",
    "        mu = 1/20;lamda = 1\n",
    "        Qa = np.zeros((n,n,2))\n",
    "        Qb = np.zeros((n,n,2))\n",
    "        freq = np.ones((n,n))\n",
    "        freq_action = np.ones((n,n,2))\n",
    "        (arrivals,departures) = arrival_and_departure_times(mu,lamda,N)\n",
    "        local_pos = 0;cloud_pos = 0\n",
    "        local_actions = np.array([False]*N)\n",
    "        cloud_actions = np.array([False]*N)\n",
    "        reward_list = []\n",
    "        reward_sum_list = []\n",
    "        for i in range(N):\n",
    "            local_pos = ((departures > arrivals[i]) & (arrivals < arrivals[i]) & (local_actions)).sum()\n",
    "            cloud_pos = ((departures > arrivals[i]) & (arrivals < arrivals[i]) & (cloud_actions)).sum()\n",
    "            eps = 1/np.sqrt(freq[local_pos,cloud_pos])\n",
    "            action = np.random.choice([np.argmax(Qa[local_pos,cloud_pos] + Qb[local_pos,cloud_pos]),np.random.choice([0,1])],p = [1 - eps,eps])\n",
    "            if local_pos == n:\n",
    "                action = 1\n",
    "            if cloud_pos == n:\n",
    "                action = 0        \n",
    "            if action == 0:\n",
    "                local_actions[i] = 1\n",
    "            if action == 1:\n",
    "                cloud_actions[i] = 1\n",
    "            (new_local_pos,new_cloud_pos,reward) = next_state(local_pos,cloud_pos,action)\n",
    "            reward_sum = reward_sum + reward\n",
    "            count = count + 1\n",
    "            alpha = 1/np.power(freq_action[local_pos,cloud_pos, action],0.8)\n",
    "            if np.random.choice([True,False]):\n",
    "                Qa[local_pos,cloud_pos,action] = Qa[local_pos,cloud_pos,action] + alpha*(reward + gamma*Qb[local_pos,cloud_pos,np.argmax(Qa[new_local_pos,new_cloud_pos])] - Qa[local_pos,cloud_pos,action])\n",
    "            else:\n",
    "                Qb[local_pos,cloud_pos,action] = Qb[local_pos,cloud_pos,action] + alpha*(reward + gamma*Qa[local_pos,cloud_pos,np.argmax(Qb[new_local_pos,new_cloud_pos])] - Qb[local_pos,cloud_pos,action])\n",
    "            freq[local_pos,cloud_pos] = freq[local_pos,cloud_pos] + 1\n",
    "            freq_action[local_pos,cloud_pos,action] = freq_action[local_pos,cloud_pos,action] + 1     \n",
    "            reward_list.append(reward)\n",
    "            reward_sum_list.append(reward_sum/count)\n",
    "        reward_sum_avg_list = reward_sum_avg_list + np.array(reward_sum_list)\n",
    "    return reward_list,reward_sum_list,reward_sum_avg_list/runs,local_actions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e0aeaf7bd0>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 5000\n",
    "r_list_dq,r_sum_list_dq,r_sum_avg_list_dq,local_actions_dq = Server_DoubleQ_learning(N)\n",
    "plt.plot(-r_sum_avg_list_dq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}