Experiment4A.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from engine import RMG, AdvRw\n",
    "from agent import RandomAgent, IndQLearningAgent, FPLearningAgent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "N_EXP = 10\n",
    "\n",
    "r0ss = []\n",
    "r1ss = []\n",
    "\n",
    "smooth = 100\n",
    "\n",
    "for n in range(N_EXP):\n",
    "    batch_size = 1\n",
    "    max_steps = 1\n",
    "    gamma = 0.  # only one steps!\n",
    "\n",
    "    mode = 'adversary'\n",
    "    env = AdvRw(mode, p=0.6)  # p is only used in neutral mode\n",
    "    env.reset()\n",
    "\n",
    "    possible_actions = [0, 1]  # Target A or B\n",
    "\n",
    "    p = IndQLearningAgent(possible_actions, n_states=1, learning_rate=0.1, epsilon=0.1, gamma=gamma)\n",
    "    #p = FPLearningAgent(possible_actions, possible_actions, n_states=1, learning_rate=0.1, epsilon=0.1, gamma=gamma)\n",
    "\n",
    "    # Stateless interactions (agents do not have memory)\n",
    "    s = 0\n",
    "\n",
    "    n_iter = 1000 + smooth # because of the smoothening in the graphics\n",
    "\n",
    "    r0s = []\n",
    "    r1s = []\n",
    "\n",
    "    for i in range(n_iter):\n",
    "\n",
    "        # A full episode:\n",
    "        done = False\n",
    "\n",
    "        while not done:\n",
    "\n",
    "            # Agents decide\n",
    "            a0 = p.act()\n",
    "\n",
    "            # World changes\n",
    "            _, (r0, _), done, _ = env.step(a0)\n",
    "\n",
    "            # Agents learn\n",
    "\n",
    "            p.update(s, (a0, None), (r0, None), s )\n",
    "\n",
    "            #s = new_s  #stateless!\n",
    "\n",
    "            r0s.append(r0)\n",
    "\n",
    "        env.reset()\n",
    "    \n",
    "    print(n)\n",
    "    r0ss.append(r0s)\n",
    "    r1ss.append(r1s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def moving_average(a, n=3) :\n",
    "    ret = np.cumsum(a, dtype=float)\n",
    "    ret[n:] = ret[n:] - ret[:-n]\n",
    "    return ret[n - 1:] / n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We report moving avearage of rewards, since it's common in RL taks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0zONRo0axfPlyAJqamhg3bhxut5vq6moGDBjABx98wKg8vDfgOOuPssjh4Ltl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\nDrlUQHCaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffb59ddf7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# We set a fancy theme\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "\n",
    "plt.axis([0, max_steps*n_iter - smooth, -60.5, 60.5])\n",
    "\n",
    "\n",
    "plt.plot(moving_average(r0s, smooth), 'c')\n",
    "plt.plot(moving_average(r1s, smooth))\n",
    "plt.xlabel('t');\n",
    "plt.ylabel('R');\n",
    "\n",
    "plt.savefig('img/4A_' + mode + '.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1JqamgDQg0vjyedK4k0mFDRuieOONIAYGLNx1Vz/+8IcwbrmlGYsWabz22sRV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffb90b9a6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# We set a fancy theme\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "plt.axis([0, max_steps*n_iter - smooth, -60.5, 60.5])\n",
    "for i in range(N_EXP):\n",
    "    plt.plot(moving_average(r0ss[i], 100), 'b', alpha=0.05)\n",
    "    \n",
    "plt.plot(moving_average(np.asarray(r0ss).mean(axis=0), 100), 'b', alpha=1)\n",
    "\n",
    "plt.xlabel('t');\n",
    "plt.ylabel('R');\n",
    "\n",
    "\n",
    "plt.savefig('img/4A_' + mode + '.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the neutral case (N=100):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.673836163836164"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moving_average(np.asarray(r0ss).mean(axis=0), 100).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the friend case (N=100):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44.89634365634366"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moving_average(np.asarray(r0ss).mean(axis=0), 100).mean()"
   ]
  },
  {
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}