Example_4.3 Gambler's Problem.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "這邊利用課本中的範例4.3來說明value iteration的過程，程式碼主要參考的是[ShangtongZhang](https://github.com/ShangtongZhang/reinforcement-learning-an-introduction/blob/master/chapter04/gamblers_problem.py)，再加上自己理解的註解。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "這範例說明的是一個賭徒去猜一系列的拋硬幣的結果。如果硬幣朝上，那賭徒就得到跟他這次下注一樣多的錢；如果硬幣朝下，那就輸了這次下注的錢。當賭徒贏100元或是輸到脫褲那就結束。每次拋硬幣，賭徒都需要決定要下注多少，而且是整數。這可以描述成一個undiscounted、episodeic、finite MDP的問題：\n",
    "* state：賭徒的資金，也就是已經贏多少了，$s\\in \\left\\{1,2,3,...,99 \\right\\}$\n",
    "* action：下注的金額，$a\\in\\left\\{0,1,...,\\min(s, 100-s) \\right\\}$\n",
    "* reward：達到目標(贏100元)的時候+1，其它狀態轉移的時候皆為0\n",
    "\n",
    "state-value function會給出每一個state賭徒會贏的機率。這邊的policy是賭徒資金到下注的映射。optimal policy會最大化達到目標的機率。\n",
    "\n",
    "假設，$p_h$表示硬幣會是正面的機率。如果$p_h$已知，那整個問題就可以求解了，像是用value iteration來求解。Figure 4.3說明了value iteration連續掃描過程中的value function的變化，以及找到最終的policy($p_h=0.4$)。policy是最佳的(optimal)，但並非是唯一的。事實上optimal policies有一堆，具體取決於能夠最大化value function的那個action的選擇(argmax action)。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\begin{align}\n",
    "v_{k+1}(s) & \\dot{=} \\max_a \\mathbb{E}[R_{t+1} + \\gamma v_k(S_{t+1})\\vert S_{t}=s, A_{t}=a] \\\\\n",
    "&=\\max_a\\sum_{s',r}p(s', r\\vert s, a)[r + \\gamma v_k(s')]\n",
    "\\end{align} \\tag{4.10}\n",
    "$$ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "載入需求套件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面根據狀況說明設置參數"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "賭徒只要贏到100元那就是贏，reward + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "GOAL = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "設置state spaces，因為只要贏到100那就是贏，所以state會是0~100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "        26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
       "        39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
       "        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
       "        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
       "        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
       "        91,  92,  93,  94,  95,  96,  97,  98,  99, 100])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "STATES = np.arange(GOAL + 1)\n",
    "STATES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "出現正面的機率，根據說明，這個機率會是0.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "HEAD_PROB = 0.4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我們設置函數開始這個value iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def figure_4_3():\n",
    "    # state value\n",
    "    # 只有在到達目標，也就是贏到100元的時候才是1，其餘都會是0\n",
    "    state_value = np.zeros(GOAL + 1)\n",
    "    state_value[GOAL] = 1.0\n",
    "    \n",
    "    sweeps_history = []\n",
    "    # value iteration\n",
    "    while True:        \n",
    "        # 跟之前的範例一樣的作法\n",
    "        old_state_value = state_value.copy()\n",
    "        # 記錄value function的變化\n",
    "        sweeps_history.append(old_state_value)\n",
    "        \n",
    "        # 列出所有可能的state\n",
    "        for state in STATES[1:GOAL]:\n",
    "            # 根據文中說明設置每個state所擁有的actions\n",
    "            # 取小是因為，你如果身上只有30元，那當然就只能下注30，\n",
    "            # 如果你身上有70元，目標100，那你就只需要再下注30就能達到目標\n",
    "            actions = np.arange(min(state, GOAL - state) + 1)\n",
    "            # 記錄每一個action會有的return\n",
    "            action_returns = []\n",
    "            for action in actions:\n",
    "                # 計算每一個action的return\n",
    "                # 正面的機率0.4 * 該state + 下注後的金額的的value(贏) + \n",
    "                # 反面的機率0.6 * 該state - 下注後的金額的value(輸)\n",
    "                action_returns.append(\n",
    "                    HEAD_PROB * state_value[state + action] + (1 - HEAD_PROB) * state_value[state - action]\n",
    "                )\n",
    "            # 根據4.10，我們直接取最大的那一個值做為該state的value\n",
    "            new_value = np.max(action_returns)\n",
    "            state_value[state] = new_value\n",
    "        # 計算新、舊value function之間的差異，判斷是否小於閥值\n",
    "        delta = abs(state_value - old_state_value).max()\n",
    "        if delta < 1e-9:\n",
    "            sweeps_history.append(state_value)\n",
    "            break            \n",
    "    \n",
    "    # value function好了之後就要計算optimal oplicy\n",
    "    policy = np.zeros(GOAL + 1)\n",
    "    for state in STATES[1: GOAL]:\n",
    "        actions = np.arange(min(state, GOAL - state) + 1)        \n",
    "        action_returns = []\n",
    "        for action in actions:\n",
    "            action_returns.append(\n",
    "                HEAD_PROB * state_value[state + action] + (1 - HEAD_PROB) * state_value[state - action]\n",
    "            )          \n",
    "        # round to resemble the figure in the book, see\n",
    "        # https://github.com/ShangtongZhang/reinforcement-learning-an-introduction/issues/83\n",
    "        # 大致就是四捨五入之後取最大的一個來做為遇到這個state預計執行的action\n",
    "#         print(action_returns)\n",
    "        policy[state] = actions[np.argmax(np.round(action_returns[1: ], 5)) + 1]        \n",
    "        \n",
    "    # 下面開始設置圖表\n",
    "    plt.figure(figsize=(10, 20))\n",
    "    # 設置子圖表\n",
    "    plt.subplot(2, 1, 1)\n",
    "    for sweep, state_value in enumerate(sweeps_history):\n",
    "        plt.plot(state_value, label=f'sweep {sweep}')\n",
    "    \n",
    "    plt.xlabel('Capital')\n",
    "    plt.ylabel('Value estimates')\n",
    "    plt.legend(loc='best')\n",
    "    \n",
    "    # 設置另一個子圖\n",
    "    plt.subplot(2, 1, 2)\n",
    "    plt.scatter(STATES, policy)\n",
    "    plt.xlabel('Capital')\n",
    "    plt.ylabel('Final policy (stake)')\n",
    "    \n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure_4_3()"
   ]
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}