2021-03-02 08:32:40 +01:00
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2021-04-14 08:03:54 +02:00
"## Uczenie maszynowe –
2021-03-02 08:32:40 +01:00
"# 15. Uczenie przez wzmacnianie i systemy dialogowe"
]
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"source": [
"## 15.1. Uczenie przez wzmacnianie"
]
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{
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"source": [
"### Paradygmat uczenia przez wzmacnianie"
]
},
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"slide_type": "subslide"
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"source": [
"<img src=\"https://cdn-images-1.medium.com/max/1560/1*Yf8rcXiwvqEAinDTWTnCPA.jpeg\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
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},
"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": {
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"source": [
"<img src=\"https://bigdata-madesimple.com/wp-content/uploads/2018/02/Machine-Learning-Explained3.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
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"source": [
"* Paradygmat uczenia przez wzmacnianie naśladuje sposób, w jaki uczą się dzieci.\n",
"* Interakcja ze środowiskiem."
]
},
{
"cell_type": "markdown",
"metadata": {
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"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": {
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"slide_type": "fragment"
}
},
"source": [
"<img src=\"https://cdn-images-1.medium.com/max/1600/1*WOYVzYnF-rbdcgZU2Wt9Yw.png\">"
]
},
{
"cell_type": "markdown",
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"source": [
"* Celem jest znalezienie takiej taktyki wyboru kolejnej akcji, aby zmaksymalizować wartość końcowej nagrody. "
]
},
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"source": [
"Zastosowanie uczenia przez wzmacnianie:\n",
"* strategie gier\n",
"* systemy dialogowe\n",
"* sterowanie"
]
},
{
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"source": [
"### Uczenie przez wzmacnianie jako proces decyzyjny Markowa"
]
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"Paradygmat uczenia przez wzmacnianie można formalnie opisać jako proces decyzyjny Markowa:\n",
"$$ (S, A, T, R) $$\n",
"gdzie:\n",
"* $S$ –
"* $A$ –
"* $T \\colon A \\times S \\to S$ –
"* $R \\colon A \\times S \\to \\mathbb{R}$ –
]
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"source": [
"Albo, jeśli przyjmiemy, że środowisko zmienia się w sposób niedeterministyczny:\n",
"$$ (S, A, P, R) $$\n",
"gdzie:\n",
"* $S$ –
"* $A$ –
"* $P \\colon A \\times S \\times S \\to [0, 1]$ –
"* $R \\colon A \\times S \\times S \\to \\mathbb{R}$ –
]
},
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"slide_type": "subslide"
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"source": [
"Na przykład, prawdopodobieństwo, że akcja $a$ spowoduje przejście ze stanu $s$ do $s'$:\n",
"$$ P_a(s, s') \\; = \\; \\mathbf{P}( \\, s_{t+1} = s' \\, | \\, s_t = s, a_t = a \\,) $$"
]
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"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."
]
},
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"metadata": {
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"slide_type": "subslide"
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"source": [
"Algorytm szuka optymalnej strategii metodą prób i błędów –
"* 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",
"$$ Q_{\\pi}(s, a) = \\mathbf{E}_{\\pi}(R \\, | \\, s_t = s, a_t = a) $$"
]
},
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"cell_type": "markdown",
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"source": [
"### Algorytmy uczenia przez wzmacnianie\n",
"* Programowanie dynamiczne (DP):\n",
" * _bootstrapping_ –
"* Metody Monte Carlo (MC)\n",
"* Uczenie oparte na różnicach czasowych (_temporal difference learning_, TD):\n",
" * _on-policy_ –
" * SARSA (_state– – – –
" * _off-policy_ –
" * _Q-Learning_\n",
" * _Actor–
]
},
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"cell_type": "markdown",
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"source": [
"### Przykład: odwrócone wahadło (_cart and pole_)"
]
},
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"\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>"
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},
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"metadata": {},
"output_type": "execute_result"
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],
"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"
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"outputs": [
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"\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"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Architektura systemu dialogowego\n",
"\n",
"<img src=\"sds.png\">"
]
}
],
"metadata": {
"celltoolbar": "Slideshow",
"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": "3.8.3"
},
"livereveal": {
"start_slideshow_at": "selected",
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"theme": "white"
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}
},
"nbformat": 4,
"nbformat_minor": 4
}
