{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ２章のkeras版\n",
    "Deep Reinforcement Learning in Actionの２章にでてくる「多椀バンディット問題(Multi-Arm Bandit)」をTorchではなく、Kerasを使って試してみました。\n",
    "\n",
    "多椀バンディット問題は、強化学習では有名な例題みたいです。\n",
    "- http://ibisml.org/archive/ibis2014/ibis2014_bandit.pdf\n",
    "\n",
    "前提条件\n",
    "- 複数台のスロットマシンをプレイするギャンブラーのモデル \n",
    "- 得られる報酬の確率分布は台によって異なる\n",
    "- なるべく期待値の高い台をプレイしたい\n",
    "\n",
    "問題点\n",
    "- ある程度の回数プレイしないと台の良し悪しが分からない\n",
    "- 報酬の期待値の低い台を何度もプレイすると損\n",
    "\n",
    "ここでは、最高$10まででる１０台のスロットルマシーンが題材となっている。\n",
    "\n",
    "- 報酬: $R_k$（k回目の報酬）\n",
    "- 報酬の期待値: $Q_k(a)$（アクションaに対するk回目までの期待値）\n",
    "\n",
    "$$\n",
    "Q_k(a) = \\frac{R_1 + R_2 + ... + R_k}{k_a}\n",
    "$$\n",
    "\n",
    "アクションaの最尤値は、以下のように定義される。\n",
    "\n",
    "$$\n",
    "a = argmax_a (Q_k(a))\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 前準備\n",
    "最初に必要なライブラリーをインポートしておきます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import random\n",
    "from IPython.display import Image\n",
    "%matplotlib inline\n",
    "\n",
    "# keras用のパッケージをインポート\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras import optimizers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 環境のセット\n",
    "多椀バンディットのクラスとしてContextBanditを使用します（サポートページからの引用）。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class ContextBandit:\n",
    "    def __init__(self, arms=10):\n",
    "        self.arms = arms\n",
    "        self.init_distribution(arms)\n",
    "        self.update_state()\n",
    "        \n",
    "    def init_distribution(self, arms):\n",
    "        # Num states = Num Arms to keep things simple\n",
    "        self.bandit_matrix = np.random.rand(arms,arms)\n",
    "        #each row represents a state, each column an arm\n",
    "        \n",
    "    def reward(self, prob):\n",
    "        reward = 0\n",
    "        for i in range(self.arms):\n",
    "            if random.random() < prob:\n",
    "                reward += 1\n",
    "        return reward\n",
    "        \n",
    "    def get_state(self):\n",
    "        return self.state\n",
    "    \n",
    "    def update_state(self):\n",
    "        self.state = np.random.randint(0,self.arms)\n",
    "        \n",
    "    def get_reward(self,arm):\n",
    "        return self.reward(self.bandit_matrix[self.get_state()][arm])\n",
    "        \n",
    "    def choose_arm(self, arm):\n",
    "        reward = self.get_reward(arm)\n",
    "        self.update_state()\n",
    "        return reward\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## モデル作成\n",
    "ニューラルネットで使用するモデルは、入力１０、隠れ層100、出力10のモデルです。\n",
    "- 入力：アクションを10個の配列で表現\n",
    "- 出力: 報酬の予測値\n",
    "\n",
    "処理の流れを図で表すために、Dot記述でグラフを描いてみました（dot記法の練習）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting models/ex_5a.dot\n"
     ]
    }
   ],
   "source": [
    "%%writefile models/ex_5a.dot\n",
    "digraph ex_5 {\n",
    "    graph [rankdir=LR, charset=\"UTF-8\"];\n",
    "    // グラフ名はcluster_で始める\n",
    "    // node\n",
    "    env_get_state [label=\"env.get_state()\", shape=\"box\", style=\"rounded,filled\"];\n",
    "    one_hot_encode [label=\"One-Hot\\nEncode\", shape=\"box\", style=\"rounded,filled\"];\n",
    "        \n",
    "    subgraph cluster_1 {\n",
    "        label = \"Layer1\";\n",
    "        style= \"rounded\";\n",
    "        \n",
    "        theta1 [label=\"θ_1:10x100\", shape=\"box\", style=\"rounded,filled\"];\n",
    "        ReLU1 [label=\"ReLU\", shape=\"box\", style=\"rounded,filled\"];\n",
    "        \n",
    "        theta1 -> ReLU1 [arrowhead=none];\n",
    "    };\n",
    "\n",
    "    env_get_state -> one_hot_encode [label=\"整数\", fontname=\"ipafont-gothic\", lblstyle=\"auto\", arrowhead=none];\n",
    "    one_hot_encode -> theta1 [label=\"実数[10]\", fontname=\"ipafont-gothic\", lblstyle=\"auto\", arrowhead=none];\n",
    "\n",
    "    sample_action [label=\"Sample Action\", shape=\"box\", style=\"rounded,filled\"];\n",
    "    softmax [label=\"Softmax\", shape=\"box\", style=\"rounded,filled\"];\n",
    "\n",
    "    subgraph cluster_2 {\n",
    "        label = \"Layer2\";\n",
    "        style= \"rounded\";\n",
    "        \n",
    "        ReLU2 [label=\"ReLU\", shape=\"box\", style=\"rounded,filled\"];\n",
    "        theta2 [label=\"θ_2:100x10\", shape=\"box\", style=\"rounded,filled\"];\n",
    "        \n",
    "        ReLU2 -> theta2  [arrowhead=none];        \n",
    "    };\n",
    "    \n",
    "    ReLU1 -> theta2  [arrowhead=none, topath=\"bend left\"]; \n",
    "    \n",
    "    sample_action -> softmax [label=\"確率[10]\", fontname=\"ipafont-gothic\", lblstyle=\"auto\", arrowhead=none];\n",
    "    softmax -> ReLU2  [label=\"実数[10]\", fontname=\"ipafont-gothic\", lblstyle=\"auto\", arrowhead=none];\n",
    "    \n",
    "    // node\n",
    "    env_choose_arm [label=\"env.choose_arm()\", shape=\"box\", style=\"rounded,filled\"];\n",
    "    loss_function [label=\"Loss Function\", shape=\"box\", style=\"rounded,filled\"];\n",
    "    Optimizer [shape=\"box\", style=\"rounded,filled\"];\n",
    "    \n",
    "    env_choose_arm -> loss_function [label=\"報酬:整数\", fontname=\"ipafont-gothic\", lblstyle=\"auto\", arrowhead=none];\n",
    "    loss_function -> Optimizer [arrowhead=none];\n",
    "    \n",
    "    sample_action -> env_choose_arm [label=\"整数\", fontname=\"ipafont-gothic\", lblstyle=\"auto\", arrowhead=none];\n",
    "\n",
    "    newrank=true;\n",
    "    {rank=same; ReLU1, theta2}\n",
    "    {rank=same; env_get_state, sample_action, env_choose_arm}\n",
    "    {rank=same; one_hot_encode, softmax, loss_function}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "dot -Tpng -Gdpi=200 models/ex_5a.dot> images/ex_5a.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
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bgwYNCCHv3r3r0aPHgwcPCCFpaWk2NjY+Pj5WVlb0M4e5uTlz6Xd1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\nCQkRP/mLRKLHjx87OTlZWFikpqY6ODiIj/wIIFM//vjjmDFjhELhnj1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uxyp06duDdSY4EB4PPnzywWixoSpaCg0KpVK+pACpkSXpCEI/+5F1NTU/v3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\neHiYm5tX31+c2k/a1961nT0iIoKqP//666/S0tL3/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vTu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\nEwVXtmzZctiwYX369OnWrZuOjg7ub9ed4uLilJSUV69e0SIb79+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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"images/ex_5a.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "処理の流れは、以下のようになります。\n",
    "- env.get_stateで環境から状態を取得し、One-Hot形式に変換\n",
    "- ニューラルネットワークの１層の入力とし、隠れ層100で活性関数としてReLU\n",
    "- ニューラルネットワークの２層目は出力で、10のスロットルの予測値を得る\n",
    "- Softmaxで確率分布に変換\n",
    "- 確率からアクションをサンプリングする\n",
    "- 環境からスロットルを実行し、報酬を得る\n",
    "- ニューラルネットワークの更新"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def softmax(av, tau=1.12):\n",
    "    n = len(av)\n",
    "    probs = np.zeros(n)\n",
    "    for i in range(n):\n",
    "        softm = ( np.exp(av[i] / tau) / np.sum( np.exp(av[:] / tau) ) )\n",
    "        probs[i] = softm\n",
    "    return probs\n",
    "\n",
    "def one_hot(N, pos, val=1):\n",
    "    one_hot_vec = np.zeros(N)\n",
    "    one_hot_vec[pos] = val\n",
    "    return one_hot_vec\n",
    "\n",
    "arms = 10\n",
    "# N is batch size; D_in is input dimension;\n",
    "# H is hidden dimension; D_out is output dimension.\n",
    "N, D_in, H, D_out = 1, arms, 100, arms\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(H, activation='relu', input_dim=D_in))\n",
    "model.add(Dense(D_out, activation='relu'))\n",
    "\n",
    "learning_rate = 1e-2\n",
    "optimizer = optimizers.Adam(lr=learning_rate)\n",
    "model.compile(optimizer=optimizer, loss='mean_squared_error', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "env = ContextBandit(arms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train(env):\n",
    "    epochs = 5000\n",
    "    #one-hot encode current state\n",
    "    cur_state = one_hot(arms,env.get_state()).reshape(1, D_in)\n",
    "    reward_hist = np.zeros(50)\n",
    "    reward_hist[:] = 5\n",
    "    runningMean = np.average(reward_hist)\n",
    "    plt.xlabel(\"Plays\")\n",
    "    plt.ylabel(\"Mean Reward\")\n",
    "    for i in range(epochs):\n",
    "        cur_state = one_hot(arms,env.get_state())[np.newaxis,:]\n",
    "        y_pred = model.predict(cur_state) #produce reward predictions\n",
    "        av_softmax = softmax(y_pred.flatten(), tau=2.0) #turn reward distribution into probability distribution\n",
    "        av_softmax /= av_softmax.sum() #make sure total prob adds to 1\n",
    "        choice = np.random.choice(arms, p=av_softmax) #sample an action\n",
    "        cur_reward = env.choose_arm(choice)\n",
    "        one_hot_reward = y_pred.copy()[0]\n",
    "        one_hot_reward[choice] = cur_reward\n",
    "        one_hot_reward = one_hot_reward[np.newaxis,:]\n",
    "        \n",
    "        if i % 50 == 0:\n",
    "            runningMean = np.average(reward_hist)\n",
    "            reward_hist[:] = 0\n",
    "            plt.scatter(i, runningMean)\n",
    "        reward_hist[i % 50] = cur_reward\n",
    "        \n",
    "        model.fit(cur_state, one_hot_reward, epochs=1, batch_size=1, verbose=0)\n",
    "\n",
    "        cur_state = one_hot(arms,env.get_state())[np.newaxis,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ln+Hh9QwOLmTZsncxOLiQ4eH1eYckItIUlTAyoK60IpIXlTAKRl1pRaQbKGFk\nYHJXWlBXWhEpIiWMDAwMDDA0tIb+/qXMnbuI/v6lDA2tUXWUiBSK2jAypF5SIpI1dasVEZFY1Ogt\nIiKZU8IQEZFYlDBERCQWJQwREYlFCUNERGJRwhARkViUMEREJJbUE4aZjZvZHWa21cw2NZjnEjO7\nx8y2mdnJacckIiLNS/sWrQBPAWV3/13Um2a2HDjW3Z9jZqcDlwGLM4grMxrhLSLdIIsqKZtmPa8H\nvgzg7rcC88xsQQZxZUL3wRCRbpFFwnBgo5ltNrN3RLx/JHB/zesHw2mFV6lUWLFiJRMTI+zefTsT\nEyOsWLGSSqWSd2giIk3Lokrqxe7+azMbAG40s7vd/Yc170dd4yTyolGrV6/e+7xcLlMul5OMM3HV\n+2BMTOx/HwxVTYlIGkZHRxkdHU1l2ZlefNDMVgGPuvu/1Ey7DBhx9/Xh6x3AEnd/uO5/C3fxQd1p\nT0TyVpiLD5rZbDM7OHw+BzgDuKtutg3AueE8i4FH6pNFUek+GCLSTVItYZjZs4BvEFQxHQR8zd0v\nMrPzAXf3K8L5LgVeBTwOnOfuWyKWVbgSRpV6SYlIXnQ/DBERiaUwVVIiItI9lDBERCQWJQwREYlF\nCUNERGJRwhARkViUMEREJBYlDBERiUUJQ0REYlHCEBGRWJQwREQkFiUMERGJRQlDRERiUcIQEZFY\nlDBERCQWJQwREYklk4RhZgeY2RYz2xDx3lvNbFf4/hYze3sWMYmISHOyKmG8Dxib4v117r4ofHwx\no5gyldZN2bOi+PNV5PiLHDsUP/4kpZ4wzOwo4NXAF6aaLe048lb0L53iz1eR4y9y7FD8+JOURQnj\nYuADBPf1buQvzGybmV0dJhgREekwqSYMM3sN8LC7byMoRUSVJDYAJXc/GfhP4EtpxiQiIq0x96lO\n/NtcuNkngb8C/gT0A4cA17r7uQ3mPwD4rbvPj3gvvUBFRLqYuydS7Z9qwpi0IrMlwP929zPrph/u\n7r8On78B+IC7vziToEREJLaD8lipmX0M2Ozu/wFcaGZnAnuA3wJvyyMmERGZWmYlDBERKbaOGelt\nZm80s7vM7EkzW1T33t+Z2T1mdreZnVEz/VVmtsPMfm5mH6qZXjKzW8zsZ2Y2bGa5lKSmizNPZjZk\nZg+b2faaaYea2XfD/bbRzObVvHdJ+BlsM7OTa6a/Ndyun5lZZNtUSvEfZWbfM7MxM7vTzC4s0jaY\n2Uwzu9VVIyKbAAAGcElEQVTMtobxrwqnR353zazPzNaF8f/EzI6uWVbk7yODbZg0ILdgsY+b2R3h\n/t8UTivEdydc7zwz+3q4335qZqdnEr+7d8QD+DPgOcD3gEU1058LbCWoPisB9xL0tjogfD4IzAC2\nAQvD/1kPvCl8/jng/By3q2GcOe/vlwInA9trpn0K+GD4/EPAReHz5cC3w+enA7eEzw8FfgHMA+ZX\nn2cU/+HAyeHzg4GfAQsLtg2zw78HAreEcUV+d4F3A2vC52cRDHYFeF7U7yOj+N8PfBXYEL4uUuz3\nAYfWTSvSd2ctcF74/KAwhtTjT33DWtgRI0xOGB8GPlTz+vpwoxcD10fNB1SAA8Lni4EbctyehnHm\n/SBIYrUJYwewIHx+OHB3+Pwy4Kya+e4GFgBnA5+rmf652vky3pbrgFcWcRuA2cBtwGnArrrv7vXh\n8xuA08PnBwK7or5P1d9HBjEfBdwIlNmXMOp/dx0Ze7iuXwJPq5tWiO8OQW/TX0RMTz3+jqmSmsKR\nwP01rx8Mp9VPfwA40syeBvzO3Z+qmX5EFoE2EBlnTrFM5xnu/jCABz3XnhFOb7QNjT6bTJlZiaC0\ndAvBD6YQ2xBW6WwFfk1w8P0F8Ejdd7cay9443f1JYLeZHUZ+8U8akNvgd9epsUMQ90Yz22xmfx1O\nK8p35xjgN2Z2ZVgleIWZzSaD+DOt2zezGwky295JBB/cR9z9W43+LWKaE93+4kQPEMyzZb9R/EVS\nvw3Vzy33bTOzg4FrgPe5+2PWeLxOx21DeHA9xczmAt8gqH5tFEujODOP32oG5JpZuTo5IpaOi73G\ni93912Y2AHzXzH42xbo77btzELAIeI+732ZmFxOU1lKPP9MShrsvc/cTax4nhH8bJQsIsuEza14f\nBTwUTj+6frq7/waYb8EgwNr58xIZZ06xTOdhM1sAwfgYguoRaPIzyCBOAMJG1WuAr7j7N8PJhdoG\nAHf/PXAzQTVOo+/u3vjN7ECCuubf0Xi70vQS4Ewzuw8YBl4OfAaYV4DYgb1n4Lh7haA68zSK8915\nALjf3W8LX/87QQJJPf5OrZKqzXwbgLPDnhbPAp4NbAI2A882s0Ez6yOoj6seNL4HvCl8/taa6XmI\ninO/y7znpP6scAP7xsG8jX37bQNwLoCZLSaoNnkY2AgsC3tsHAosC6dl5YvAmLt/tmZaIbbBzJ5e\n7cViZv0E7S9jBG14Ud/dDeFrwve/VzM96veRGnf/P+5+tLsfQ/B9/p67/1URYgcws9lhyRQzmwOc\nAdxJQb474brvN7PjwkmvAH6aSfxZNDDFbMj5HwT1aRPAfzG5ofjvCHpQ3A2cUTP9VQS9Y+4BPlwz\n/VnArcDPCXpuzMh52yLjzDmmqwjOJv4I/Ao4j6DXxE1hrDcC82vmvzT8DO5gcqeEt4Xb9XPg3Azj\nfwnwJEGvs63AlnA/H1aEbQBOCGPeBmwnqJZt+N0FZgJXh3HeQnD9tSl/HxltxxL2NXoXIvYwzur3\n5s7qb7Io351wvScRnIxuA64l6OmUevwauCciIrF0apWUiIh0GCUMERGJRQlDRERiUcIQEZFYlDBE\nRCQWJQwREYlFCUMkggWX2d9iwaXH15vZrHD6o3nHJpIXJQyRaI+7+yJ3P4HgbpDvCqdr4JL0LCUM\nken9gOCyFRBeSsXM5pjZTWZ2W3gjnteF0//Bwps5ha//0cwuMLPDzezmsNSy3cxekv1miLRHI71F\nIpjZo+5+SM0FDr/j7lfUTD8Q6PfgCrlPI7gpzXPMbBC41t1fYGZGcNmFUwkuvTLT3f8pnD7b3R/P\na/tEWpHrrUtFOli/mW0Jn/+A4EKHMPmS3f9kZi8DngKOMLNnuPtOM/uNmZ1EcBObLe7+OzPbDAyZ\n2Qzgm+5+R4bbIpIIJQyRaH9w90VTvP8/gacDp7j7U2b2S2BW+N4XCEoUhxMmGnf/QZhcXgOsNbNP\nu/tX0wtfJHlqwxCJFnVzmdrp8whuNfqUmS0luN1t1XUEV859IeHlos3saKDi7kMECWWqZCTSkVTC\nEInWqHGvOv1rwLfM7A6C+3HfvXcG9z1mNkJwy9Lq/GXgA2a2B3iU8P4EIkWiRm+RhIV3nbsdeKO7\n/yLveESSoiopkQSZ2XMJekbdqGQh3UYlDBERiUUlDBERiUUJQ0REYlHCEBGRWJQwREQkFiUMERGJ\nRQlDRERi+f9ZgkeCcSoXtAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f208a5e3890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train(env)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}