{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Uczenie maszynowe UMZ 2019/2020\n",
    "### 16 czerwca 2020\n",
    "# 15. Uczenie przez wzmacnianie i systemy dialogowe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 15.1. Uczenie przez wzmacnianie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Paradygmat uczenia przez wzmacnianie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"https://cdn-images-1.medium.com/max/1560/1*Yf8rcXiwvqEAinDTWTnCPA.jpeg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"https://bigdata-madesimple.com/wp-content/uploads/2018/02/Machine-Learning-Explained1.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"https://bigdata-madesimple.com/wp-content/uploads/2018/02/Machine-Learning-Explained2.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"https://bigdata-madesimple.com/wp-content/uploads/2018/02/Machine-Learning-Explained3.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* Paradygmat uczenia przez wzmacnianie naśladuje sposób, w jaki uczą się dzieci.\n",
    "* Interakcja ze środowiskiem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* W chwili $t$ agent w stanie $S_t$ podejmuje akcję $A_t$, następnie obserwuje zmianę w środowisku w stanie $S_{t+1}$ i otrzymuje nagrodę $R_{t+1}$: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "<img src=\"https://cdn-images-1.medium.com/max/1600/1*WOYVzYnF-rbdcgZU2Wt9Yw.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* Celem jest znalezienie takiej taktyki wyboru kolejnej akcji, aby zmaksymalizować wartość końcowej nagrody. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Zastosowanie uczenia przez wzmacnianie:\n",
    "* strategie gier\n",
    "* systemy dialogowe\n",
    "* sterowanie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Uczenie przez wzmacnianie jako proces decyzyjny Markowa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Paradygmat uczenia przez wzmacnianie można formalnie opisać jako proces decyzyjny Markowa:\n",
    "$$ (S, A, T, R) $$\n",
    "gdzie:\n",
    "* $S$ – skończony zbiór stanów\n",
    "* $A$ – skończony zbiór akcji\n",
    "* $T \\colon A \\times S \\to S$ – funkcja przejścia która opisuje, jak zmienia się środowisko pod wpływem wybranych akcji\n",
    "* $R \\colon A \\times S \\to \\mathbb{R}$ – funkcja nagrody"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Albo, jeśli przyjmiemy, że środowisko zmienia się w sposób niedeterministyczny:\n",
    "$$ (S, A, P, R) $$\n",
    "gdzie:\n",
    "* $S$ – skończony zbiór stanów\n",
    "* $A$ – skończony zbiór akcji\n",
    "* $P \\colon A \\times S \\times S \\to [0, 1]$ – prawdopodobieństwo przejścia\n",
    "* $R \\colon A \\times S \\times S \\to \\mathbb{R}$ – funkcja nagrody"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Na przykład, prawdopodobieństwo, że akcja $a$ spowoduje przejście ze stanu $s$ do $s'$:\n",
    "$$ P_a(s, s') \\; = \\; \\mathbf{P}( \\, s_{t+1} = s' \\, | \\, s_t = s, a_t = a \\,) $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Strategia\n",
    "\n",
    "* Strategią (_policy_) nazywamy odwzorowanie $\\pi \\colon S \\to A$, które bieżącemu stanowi przyporządkuje kolejną akcję do wykonania.\n",
    "* Algorytm uczenia przez wzmacnianie będzie starał się zoptymalizować strategię tak, żeby na koniec otrzymać jak najwyższą nagrodę.\n",
    "* W chwili $t$, ostateczna końcowa nagroda jest zdefiniowana jako:\n",
    "$$ R_t := r_{t+1} + \\gamma \\, r_{t+2} + \\gamma^2 \\, r_{t+3} + \\ldots = \\sum_{k=0}^T \\gamma^k \\, r_{t+k+1} \\; , $$\n",
    "gdzie $0 < \\gamma < 1$ jest czynnikiem, który określa, jak bardzo bieżemy pod uwagę nagrody, które otrzymamy w odległej przyszłości."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Algorytm szuka optymalnej strategii metodą prób i błędów – podejmując akcje i obserwując ich wpływ na środowisko. W podejmowaniu decyzji pomoże mu oszacowanie wartości następujących funkcji:\n",
    "* Funkcja wartości ($V$) odzwierciedla, jak atrakcyjne w dalekiej perspektywie jest przejście do danego stanu:\n",
    "$$ V_{\\pi}(s) = \\mathbf{E}_{\\pi}(R \\, | \\, s_t = s) $$\n",
    "* Funkcja $Q$ odzwierciedla, jak atrakcyjne w dalekiej perspektywie jest przejście do danego stanu przez podjęcie danej akcji:\n",
    "$$ Q_{\\pi}(s, a) = \\mathbf{E}_{\\pi}(R \\, | \\, s_t = s, a_t = a) $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Algorytmy uczenia przez wzmacnianie\n",
    "* Programowanie dynamiczne (DP):\n",
    "  * _bootstrapping_ – aktualizacja oczacowań dla danego stanu na podstawie oszacowań dla możliwych stanów następnych\n",
    "* Metody Monte Carlo (MC)\n",
    "* Uczenie oparte na różnicach czasowych (_temporal difference learning_, TD):\n",
    "  * _on-policy_ – aktualizacja bieżącej strategii:\n",
    "    * SARSA (_state–action–reward–state–action_)\n",
    "  * _off-policy_ – eksploracja strategii innych niż bieżąca:\n",
    "    * _Q-Learning_\n",
    "    * _Actor–Critic_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Przykład: odwrócone wahadło (_cart and pole_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"https://www.youtube.com/embed/46wjA6dqxOM\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7fa9a4350050>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import IPython\n",
    "IPython.display.YouTubeVideo('46wjA6dqxOM', width=800, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Przykład: symulacja autonomicznego samochodu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
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SgxsztpB9BNcRaE23rdR08kxjHFGcskr4Yo4ReWUy8EAHST/UyAect8rKu2Z2qqwwOUYw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\njoMTwtG+hvEyXetQbTeJZvUkmy9Wt8lioGO2WIiEG1pXNeZ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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"https://www.youtube.com/embed/G-GpY7bevuw\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7fa9a4332750>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import IPython\n",
    "IPython.display.YouTubeVideo('G-GpY7bevuw', width=800, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 15.2. Systemy dialogowe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Rodzaje systemów dialogowych\n",
    "* Chatboty\n",
    "* Systemy zorientowane na zadania (_task-oriented systems_, _goal-oriented systems_):\n",
    "  * szukanie informacji\n",
    "  * wypełnianie formularzy\n",
    "  * rozwiązywanie problemów\n",
    "  * systemy edukacyjne i tutorialowe\n",
    "  * inteligentni asystenci"
   ]
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    "### Architektura systemu dialogowego\n",
    "\n",
    "<img src=\"sds.png\">"
   ]
  }
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