16_reinforcement_learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPython 3.6.6\n",
      "IPython 6.5.0\n",
      "\n",
      "numpy 1.15.2\n",
      "sklearn 0.20.0\n",
      "scipy 1.1.0\n",
      "matplotlib 2.2.3\n",
      "tensorflow 1.12.0\n",
      "gym 0.10.5\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -v -p numpy,sklearn,scipy,matplotlib,tensorflow,gym"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**16장 – 강화 학습**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_이 노트북은 15장에 있는 모든 샘플 코드와 연습문제 해답을 가지고 있습니다._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 설정"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "파이썬 2와 3을 모두 지원합니다. 공통 모듈을 임포트하고 맷플롯립 그림이 노트북 안에 포함되도록 설정하고 생성한 그림을 저장하기 위한 함수를 준비합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 파이썬 2와 파이썬 3 지원\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# 공통\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "\n",
    "# 일관된 출력을 위해 유사난수 초기화\n",
    "def reset_graph(seed=42):\n",
    "    tf.reset_default_graph()\n",
    "    tf.set_random_seed(seed)\n",
    "    np.random.seed(seed)\n",
    "\n",
    "# 맷플롯립 설정\n",
    "from IPython.display import HTML\n",
    "import matplotlib\n",
    "import matplotlib.animation as animation\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# 한글출력\n",
    "plt.rcParams['font.family'] = 'NanumBarunGothic'\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 그림을 저장할 폴더\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"rl\"\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OpenAI 짐(gym)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 노트북에서는 강화 학습 알고리즘을 개발하고 비교할 수 있는 훌륭한 도구인 [OpenAI 짐(gym)](https://gym.openai.com/)을 사용합니다. 짐은 *에이전트*가 학습할 수 있는 많은 환경을 제공합니다. `gym`을 임포트해 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그다음 MsPacman 환경 버전 0을 로드합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = gym.make('MsPacman-v0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`reset()` 메서드를 호출하여 환경을 초기화합니다. 이 메서드는 하나의 관측을 반환합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs = env.reset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "관측은 환경마다 다릅니다. 여기에서는 [width, height, channels] 크기의 3D 넘파이 배열로 저장되어 있는 RGB 이미지입니다(채널은 3개로 빨강, 초록, 파랑입니다). 잠시 후에 보겠지만 다른 환경에서는 다른 오브젝트가 반환될 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(210, 160, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "환경은 `render()` 메서드를 사용하여 화면에 나타낼 수 있고 렌더링 모드를 고를 수 있습니다(렌더링 옵션은 환경마다 다릅니다). 이 경우에는 `mode=\"rgb_array\"`로 지정해서 넘파이 배열로 환경에 대한 이미지를 받겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = env.render(mode=\"rgb_array\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이미지를 그려보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,6))\n",
    "plt.imshow(img)\n",
    "plt.axis(\"off\")\n",
    "save_fig(\"MsPacman\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1980년대로 돌아오신 걸 환영합니다! :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 환경에서는 렌더링된 이미지가 관측과 동일합니다(하지만 많은 경우에 그렇지 않습니다):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(img == obs).all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "환경을 그리기 위한 유틸리티 함수를 만들겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_environment(env, figsize=(5,6)):\n",
    "    plt.figure(figsize=figsize)\n",
    "    img = env.render(mode=\"rgb_array\")\n",
    "    plt.imshow(img)\n",
    "    plt.axis(\"off\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "환경을 어떻게 다루는지 보겠습니다. 에이전트는 \"행동 공간\"(가능한 행동의 모음)에서 하나의 행동을 선택합니다. 이 환경의 액션 공간을 다음과 같습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Discrete(9)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.action_space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Discrete(9)`는 가능한 행동이 정수 0에서부터 8까지있다는 의미입니다. 이는 조이스틱의 9개의 위치(0=중앙, 1=위, 2=오른쪽, 3=왼쪽, 4=아래, 5=오른쪽위, 6=왼쪽위, 7=오른쪽아래, 8=왼쪽아래)에 해당합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그다음 환경에게 플레이할 행동을 알려주고 게임의 다음 단계를 진행시킵니다. 왼쪽으로 110번을 진행하고 왼쪽아래로 40번을 진행해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "env.reset()\n",
    "for step in range(110):\n",
    "    env.step(3) #왼쪽\n",
    "for step in range(40):\n",
    "    env.step(8) #왼쪽아래"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "어디에 있을까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 360x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_environment(env)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "사실 `step()` 함수는 여러 개의 중요한 객체를 반환해 줍니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs, reward, done, info = env.step(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "앞서 본 것처럼 관측은 보이는 환경을 설명합니다. 여기서는 210x160 RGB 이미지입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(210, 160, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "환경은 마지막 스텝에서 받을 수 있는 보상을 알려 줍니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reward"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "게임이 종료되면 환경은 `done=True`를 반환합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "done"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "마지막으로 `info`는 환경의 내부 상태에 관한 추가 정보를 제공하는 딕셔너리입니다. 디버깅에는 유용하지만 에이전트는 학습을 위해서 이 정보를 사용하면 안됩니다(학습이 아니고 속이는 셈이므로)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ale.lives': 3}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10번의 스텝마다 랜덤한 방향을 선택하는 식으로 전체 게임(3개의 팩맨)을 플레이하고 각 프레임을 저장해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "frames = []\n",
    "\n",
    "n_max_steps = 1000\n",
    "n_change_steps = 10\n",
    "\n",
    "obs = env.reset()\n",
    "for step in range(n_max_steps):\n",
    "    img = env.render(mode=\"rgb_array\")\n",
    "    frames.append(img)\n",
    "    if step % n_change_steps == 0:\n",
    "        action = env.action_space.sample() # play randomly\n",
    "    obs, reward, done, info = env.step(action)\n",
    "    if done:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 애니메이션으로 한번 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_scene(num, frames, patch):\n",
    "    plt.close()  # 이전 그래프를 닫지 않으면 두 개의 그래프가 출력되는 matplotlib의 버그로 보입니다.\n",
    "    patch.set_data(frames[num])\n",
    "    return patch,\n",
    "\n",
    "def plot_animation(frames, figsize=(5,6), repeat=False, interval=40):\n",
    "    fig = plt.figure(figsize=figsize)\n",
    "    patch = plt.imshow(frames[0])\n",
    "    plt.axis('off')\n",
    "    return animation.FuncAnimation(fig, update_scene, fargs=(frames, patch), \n",
    "                                   frames=len(frames), repeat=repeat, interval=interval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "AAAAAQAAAABMYXZmNTcuODMuMTAw\n",
       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video = plot_animation(frames)\n",
    "HTML(video.to_html5_video())  # HTML5 동영상으로 만들어 줍니다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "환경을 더 이상 사용하지 않으면 환경을 종료하여 자원을 반납합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "첫 번째 에이전트를 학습시키기 위해 간단한 Cart-Pole 환경을 사용하겠습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 간단한 Cart-Pole 환경"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cart-Pole은 아주 간단한 환경으로 왼쪽이나 오른쪽으로 움직일 수 있는 카트와 카트 위에 수직으로 서 있는 막대로 구성되어 있습니다. 에이전트는 카트를 왼쪽이나 오른쪽으로 움직여서 막대가 넘어지지 않도록 유지시켜야 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "env = gym.make(\"CartPole-v0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs = env.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.02189362,  0.03504881, -0.04745863,  0.00361576])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "관측은 4개의 부동소수로 구성된 1D 넘파이 배열입니다. 각각 카트의 수평 위치, 속도, 막대의 각도(0=수직), 각속도를 나타냅니다. 이 환경을 렌더링하려면 먼저 몇 가지 이슈를 해결해야 합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 렌더링 이슈 해결하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "일부 환경(Cart-Pole을 포함하여)은 `rgb_array` 모드를 설정하더라도 별도의 창을 띄우기 위해 디스플레이 접근이 필수적입니다. 일반적으로 이 창을 무시하면 됩니다. 주피터가 헤드리스(headless) 서버로 (즉 스크린이 없이) 실행중이면 예외가 발생합니다. 이를 피하는 한가지 방법은 Xvfb 같은 가짜 X 서버를 설치하는 것입니다. `xvfb-run` 명령을 사용해 주피터를 실행합니다:\n",
    "\n",
    "    $ xvfb-run -s \"-screen 0 1400x900x24\" jupyter notebook\n",
    "    \n",
    "주피터가 헤드리스 서버로 실행 중이지만 Xvfb를 설치하기 번거롭다면 Cart-Pole에 대해서는 다음 렌더링 함수를 사용할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image, ImageDraw\n",
    "\n",
    "try:\n",
    "    from pyglet.gl import gl_info\n",
    "    openai_cart_pole_rendering = True   # 문제없음, OpenAI 짐의 렌더링 함수를 사용합니다\n",
    "except Exception:\n",
    "    openai_cart_pole_rendering = False  # 가능한 X 서버가 없다면, 자체 렌더링 함수를 사용합니다\n",
    "\n",
    "def render_cart_pole(env, obs):\n",
    "    if openai_cart_pole_rendering:\n",
    "        # OpenAI 짐의 렌더링 함수를 사용합니다\n",
    "        return env.render(mode=\"rgb_array\")\n",
    "    else:\n",
    "        # Cart-Pole 환경을 위한 렌더링 (OpenAI 짐이 처리할 수 없는 경우)\n",
    "        img_w = 600\n",
    "        img_h = 400\n",
    "        cart_w = img_w // 12\n",
    "        cart_h = img_h // 15\n",
    "        pole_len = img_h // 3.5\n",
    "        pole_w = img_w // 80 + 1\n",
    "        x_width = 2\n",
    "        max_ang = 0.2\n",
    "        bg_col = (255, 255, 255)\n",
    "        cart_col = 0x000000 # 파랑 초록 빨강\n",
    "        pole_col = 0x669acc # 파랑 초록 빨강\n",
    "\n",
    "        pos, vel, ang, ang_vel = obs\n",
    "        img = Image.new('RGB', (img_w, img_h), bg_col)\n",
    "        draw = ImageDraw.Draw(img)\n",
    "        cart_x = pos * img_w // x_width + img_w // x_width\n",
    "        cart_y = img_h * 95 // 100\n",
    "        top_pole_x = cart_x + pole_len * np.sin(ang)\n",
    "        top_pole_y = cart_y - cart_h // 2 - pole_len * np.cos(ang)\n",
    "        draw.line((0, cart_y, img_w, cart_y), fill=0)\n",
    "        draw.rectangle((cart_x - cart_w // 2, cart_y - cart_h // 2, cart_x + cart_w // 2, cart_y + cart_h // 2), fill=cart_col) # draw cart\n",
    "        draw.line((cart_x, cart_y - cart_h // 2, top_pole_x, top_pole_y), fill=pole_col, width=pole_w) # draw pole\n",
    "        return np.array(img)\n",
    "\n",
    "def plot_cart_pole(env, obs):\n",
    "    img = render_cart_pole(env, obs)\n",
    "    plt.imshow(img)\n",
    "    plt.axis(\"off\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cart_pole(env, obs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "행동 공간을 확인해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Discrete(2)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.action_space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "네 딱 두 개의 행동이 있네요. 왼쪽이나 오른쪽 방향으로 가속합니다. 막대가 넘어지기 전까지 카트를 왼쪽으로 밀어보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs = env.reset()\n",
    "while True:\n",
    "    obs, reward, done, info = env.step(0)\n",
    "    if done:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = render_cart_pole(env, obs)\n",
    "plt.imshow(img)\n",
    "plt.axis(\"off\")\n",
    "save_fig(\"cart_pole_plot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 600, 3)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "막대가 실제로 넘어지지 않더라도 너무 기울어지면 게임이 끝납니다. 환경을 다시 초기화하고 이번에는 오른쪽으로 밀어보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs = env.reset()\n",
    "while True:\n",
    "    obs, reward, done, info = env.step(1)\n",
    "    if done:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cart_pole(env, obs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "아까 말했던 것과 같은 상황인 것 같습니다. 어떻게 막대가 똑 바로 서있게 만들 수 있을까요? 이를 위한 *정책*을 만들어야 합니다. 이 정책은 에이전트가 각 스텝에서 행동을 선택하기 위해 사용할 전략입니다. 어떤 행동을 할지 결정하기 위해 지난 행동이나 관측을 사용할 수 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 하드 코딩 정책"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "간단한 정책을 하드 코딩해 보겠습니다. 막대가 왼쪽으로 기울어지면 카트를 왼쪽으로 밀고 반대의 경우는 오른쪽으로 밉니다. 작동이 되는지 확인해 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "frames = []\n",
    "\n",
    "n_max_steps = 1000\n",
    "n_change_steps = 10\n",
    "\n",
    "obs = env.reset()\n",
    "for step in range(n_max_steps):\n",
    "    img = render_cart_pole(env, obs)\n",
    "    frames.append(img)\n",
    "\n",
    "    # hard-coded policy\n",
    "    position, velocity, angle, angular_velocity = obs\n",
    "    if angle < 0:\n",
    "        action = 0\n",
    "    else:\n",
    "        action = 1\n",
    "\n",
    "    obs, reward, done, info = env.step(action)\n",
    "    if done:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video = plot_animation(frames, figsize=(6,4))\n",
    "HTML(video.to_html5_video())  # HTML5 동영상으로 만들어 줍니다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "아니네요, 불안정해서 몇 번 움직이고 막대가 너무 기울어져 게임이 끝났습니다. 더 똑똑한 정책이 필요합니다!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 신경망 정책"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "관측을 입력으로 받고 각 관측에 대해 선택할 행동을 출력하는 신경망을 만들어 보겠습니다. 행동을 선택하기 위해 네트워크는 먼저 각 행동에 대한 확률을 추정하고 그다음 추정된 확률을 기반으로 랜덤하게 행동을 선택합니다. Cart-Pole 환경의 경우에는 두 개의 행동(왼쪽과 오른쪽)이 있으므로 하나의 출력 뉴런만 있으면 됩니다. 행동 0(왼쪽)에 대한 확률 `p`를 출력할 것입니다. 행동 1(오른쪽)에 대한 확률은 `1 - p`가 됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "# 1. 네트워크 구조를 설정합니다\n",
    "n_inputs = 4  # == env.observation_space.shape[0]\n",
    "n_hidden = 4  # 간단한 작업이므로 너무 많은 뉴런이 필요하지 않습니다\n",
    "n_outputs = 1 # 왼쪽으로 가속할 확률을 출력합니다\n",
    "initializer = tf.variance_scaling_initializer()\n",
    "\n",
    "# 2. 네트워크를 만듭니다\n",
    "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
    "hidden = tf.layers.dense(X, n_hidden, activation=tf.nn.elu,\n",
    "                         kernel_initializer=initializer)\n",
    "outputs = tf.layers.dense(hidden, n_outputs, activation=tf.nn.sigmoid,\n",
    "                          kernel_initializer=initializer)\n",
    "\n",
    "# 3. 추정된 확률을 기반으로 랜덤하게 행동을 선택합니다\n",
    "p_left_and_right = tf.concat(axis=1, values=[outputs, 1 - outputs])\n",
    "action = tf.multinomial(tf.log(p_left_and_right), num_samples=1)\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 환경은 각 관측이 환경의 모든 상태를 포함하고 있기 때문에 지난 행동과 관측은 무시해도 괜찮습니다. 숨겨진 상태가 있다면 이 정보를 추측하기 위해 이전 행동과 상태를 고려해야 합니다. 예를 들어, 속도가 없고 카트의 위치만 있다면 현재 속도를 예측하기 위해 현재의 관측뿐만 아니라 이전 관측도 고려해야 합니다. 관측에 잡음이 있을 때도 같은 경우입니다. 현재 상태를 근사하게 추정하기 위해 과거 몇 개의 관측을 사용하는 것이 좋을 것입니다. 이 문제는 아주 간단해서 현재 관측에 잡음이 없고 환경의 모든 상태가 담겨 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "정책 네트워크에서 만든 확률을 기반으로 가장 높은 확률을 가진 행동을 고르지 않고 왜 랜덤하게 행동을 선택하는지 궁금할 수 있습니다. 이런 방식이 에이전트가 새 행동을 *탐험*하는 것과 잘 동작하는 행동을 *이용*하는 것 사이에 균형을 맞추게 합니다. 만약 어떤 레스토랑에 처음 방문했다고 가정합시다. 모든 메뉴에 대한 선호도가 동일하므로 랜덤하게 하나를 고릅니다. 이 메뉴가 맛이 좋았다면 다음에 이를 주문할 가능성을 높일 것입니다. 하지만 100% 확률이 되어서는 안됩니다. 그렇지 않으면 다른 메뉴를 전혀 선택하지 않게 되고 더 좋을 수 있는 메뉴를 시도해 보지 못하게 됩니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "정책 신경망을 랜덤하게 초기화하고 게임 하나를 플레이해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_max_steps = 1000\n",
    "frames = []\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    obs = env.reset()\n",
    "    for step in range(n_max_steps):\n",
    "        img = render_cart_pole(env, obs)\n",
    "        frames.append(img)\n",
    "        action_val = action.eval(feed_dict={X: obs.reshape(1, n_inputs)})\n",
    "        obs, reward, done, info = env.step(action_val[0][0])\n",
    "        if done:\n",
    "            break\n",
    "\n",
    "env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "랜덤하게 초기화한 정책 네트워크가 얼마나 잘 동작하는지 확인해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video = plot_animation(frames, figsize=(6,4))\n",
    "HTML(video.to_html5_video())  # HTML5 동영상으로 만들어 줍니다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "음.. 별로 좋지 않네요. 신경망이 더 잘 학습되어야 합니다. 먼저 앞서 사용한 기본 정책을 학습할 수 있는지 확인해 보겠습니다. 막대가 왼쪽으로 기울어지면 왼쪽으로 움직이고 오른쪽으로 기울어지면 오른쪽으로 이동해야 합니다. 다음 코드는 같은 신경망이지만 타깃 확률 `y`와 훈련 연산(`cross_entropy`, `optimizer`, `training_op`)을 추가했습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "reset_graph()\n",
    "\n",
    "n_inputs = 4\n",
    "n_hidden = 4\n",
    "n_outputs = 1\n",
    "\n",
    "learning_rate = 0.01\n",
    "\n",
    "initializer = tf.variance_scaling_initializer()\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
    "y = tf.placeholder(tf.float32, shape=[None, n_outputs])\n",
    "\n",
    "hidden = tf.layers.dense(X, n_hidden, activation=tf.nn.elu, kernel_initializer=initializer)\n",
    "logits = tf.layers.dense(hidden, n_outputs)\n",
    "outputs = tf.nn.sigmoid(logits) # 행동 0(왼쪽)에 대한 확률\n",
    "p_left_and_right = tf.concat(axis=1, values=[outputs, 1 - outputs])\n",
    "action = tf.multinomial(tf.log(p_left_and_right), num_samples=1)\n",
    "\n",
    "cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(labels=y, logits=logits)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
    "training_op = optimizer.minimize(cross_entropy)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "동일한 네트워크를 동시에 10개의 다른 환경에서 플레이하고 1,000번 반복동안 훈련시키겠습니다. 완료되면 환경을 리셋합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "n_environments = 10\n",
    "n_iterations = 1000\n",
    "\n",
    "envs = [gym.make(\"CartPole-v0\") for _ in range(n_environments)]\n",
    "observations = [env.reset() for env in envs]\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for iteration in range(n_iterations):\n",
    "        target_probas = np.array([([1.] if obs[2] < 0 else [0.]) for obs in observations]) # angle<0 이면 proba(left)=1. 이 되어야 하고 그렇지 않으면 proba(left)=0. 이 되어야 합니다\n",
    "        action_val, _ = sess.run([action, training_op], feed_dict={X: np.array(observations), y: target_probas})\n",
    "        for env_index, env in enumerate(envs):\n",
    "            obs, reward, done, info = env.step(action_val[env_index][0])\n",
    "            observations[env_index] = obs if not done else env.reset()\n",
    "    saver.save(sess, \"./my_policy_net_basic.ckpt\")\n",
    "\n",
    "for env in envs:\n",
    "    env.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def render_policy_net(model_path, action, X, n_max_steps = 1000):\n",
    "    frames = []\n",
    "    env = gym.make(\"CartPole-v0\")\n",
    "    obs = env.reset()\n",
    "    with tf.Session() as sess:\n",
    "        saver.restore(sess, model_path)\n",
    "        for step in range(n_max_steps):\n",
    "            img = render_cart_pole(env, obs)\n",
    "            frames.append(img)\n",
    "            action_val = action.eval(feed_dict={X: obs.reshape(1, n_inputs)})\n",
    "            obs, reward, done, info = env.step(action_val[0][0])\n",
    "            if done:\n",
    "                break\n",
    "    env.close()\n",
    "    return frames        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
      "INFO:tensorflow:Restoring parameters from ./my_policy_net_basic.ckpt\n"
     ]
    },
    {
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       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = render_policy_net(\"./my_policy_net_basic.ckpt\", action, X)\n",
    "video = plot_animation(frames, figsize=(6,4))\n",
    "HTML(video.to_html5_video())  # HTML5 동영상으로 만들어 줍니다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "정책을 잘 학습한 것 같네요. 이제 스스로 더 나은 정책을 학습할 수 있는지 알아 보겠습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 정책 그래디언트"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "신경망을 훈련하기 위해 타깃 확률 `y`를 정의할 필요가 있습니다. 행동이 좋다면 이 확률을 증가시켜야 하고 반대로 나쁘면 이를 감소시켜야 합니다. 하지만 행동이 좋은지 나쁜지 어떻게 알 수 있을까요? 대부분의 행동으로 인한 영향은 뒤늦게 나타나는 것이 문제입니다. 게임에서 이기거나 질 때 어떤 행동이 이런 결과에 영향을 미쳤는지 명확하지 않습니다. 마지막 행동일까요? 아니면 마지막 10개의 행동일까요? 아니면 50번 스텝 앞의 행동일까요? 이를 *신용 할당 문제*라고 합니다.\n",
    "\n",
    "*정책 그래디언트* 알고리즘은 먼저 여러번 게임을 플레이하고 성공한 게임에서의 행동을 조금 더 높게 실패한 게임에서는 조금 더 낮게 되도록 하여 이 문제를 해결합니다. 먼저 게임을 진행해 보고 다시 어떻게 한 것인지 살펴 보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "reset_graph()\n",
    "\n",
    "n_inputs = 4\n",
    "n_hidden = 4\n",
    "n_outputs = 1\n",
    "\n",
    "learning_rate = 0.01\n",
    "\n",
    "initializer = tf.variance_scaling_initializer()\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
    "\n",
    "hidden = tf.layers.dense(X, n_hidden, activation=tf.nn.elu, kernel_initializer=initializer)\n",
    "logits = tf.layers.dense(hidden, n_outputs)\n",
    "outputs = tf.nn.sigmoid(logits)  # 행동 0(왼쪽)에 대한 확률\n",
    "p_left_and_right = tf.concat(axis=1, values=[outputs, 1 - outputs])\n",
    "action = tf.multinomial(tf.log(p_left_and_right), num_samples=1)\n",
    "\n",
    "y = 1. - tf.to_float(action)\n",
    "cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(labels=y, logits=logits)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
    "grads_and_vars = optimizer.compute_gradients(cross_entropy)\n",
    "gradients = [grad for grad, variable in grads_and_vars]\n",
    "gradient_placeholders = []\n",
    "grads_and_vars_feed = []\n",
    "for grad, variable in grads_and_vars:\n",
    "    gradient_placeholder = tf.placeholder(tf.float32, shape=grad.get_shape())\n",
    "    gradient_placeholders.append(gradient_placeholder)\n",
    "    grads_and_vars_feed.append((gradient_placeholder, variable))\n",
    "training_op = optimizer.apply_gradients(grads_and_vars_feed)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discount_rewards(rewards, discount_rate):\n",
    "    discounted_rewards = np.zeros(len(rewards))\n",
    "    cumulative_rewards = 0\n",
    "    for step in reversed(range(len(rewards))):\n",
    "        cumulative_rewards = rewards[step] + cumulative_rewards * discount_rate\n",
    "        discounted_rewards[step] = cumulative_rewards\n",
    "    return discounted_rewards\n",
    "\n",
    "def discount_and_normalize_rewards(all_rewards, discount_rate):\n",
    "    all_discounted_rewards = [discount_rewards(rewards, discount_rate) for rewards in all_rewards]\n",
    "    flat_rewards = np.concatenate(all_discounted_rewards)\n",
    "    reward_mean = flat_rewards.mean()\n",
    "    reward_std = flat_rewards.std()\n",
    "    return [(discounted_rewards - reward_mean)/reward_std for discounted_rewards in all_discounted_rewards]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-22., -40., -50.])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discount_rewards([10, 0, -50], discount_rate=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([-0.28435071, -0.86597718, -1.18910299]),\n",
       " array([1.26665318, 1.0727777 ])]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discount_and_normalize_rewards([[10, 0, -50], [10, 20]], discount_rate=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
      "반복: 249"
     ]
    }
   ],
   "source": [
    "env = gym.make(\"CartPole-v0\")\n",
    "\n",
    "n_games_per_update = 10\n",
    "n_max_steps = 1000\n",
    "n_iterations = 250\n",
    "save_iterations = 10\n",
    "discount_rate = 0.95\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for iteration in range(n_iterations):\n",
    "        print(\"\\r반복: {}\".format(iteration), end=\"\")\n",
    "        all_rewards = []\n",
    "        all_gradients = []\n",
    "        for game in range(n_games_per_update):\n",
    "            current_rewards = []\n",
    "            current_gradients = []\n",
    "            obs = env.reset()\n",
    "            for step in range(n_max_steps):\n",
    "                action_val, gradients_val = sess.run([action, gradients], feed_dict={X: obs.reshape(1, n_inputs)})\n",
    "                obs, reward, done, info = env.step(action_val[0][0])\n",
    "                current_rewards.append(reward)\n",
    "                current_gradients.append(gradients_val)\n",
    "                if done:\n",
    "                    break\n",
    "            all_rewards.append(current_rewards)\n",
    "            all_gradients.append(current_gradients)\n",
    "\n",
    "        all_rewards = discount_and_normalize_rewards(all_rewards, discount_rate=discount_rate)\n",
    "        feed_dict = {}\n",
    "        for var_index, gradient_placeholder in enumerate(gradient_placeholders):\n",
    "            mean_gradients = np.mean([reward * all_gradients[game_index][step][var_index]\n",
    "                                      for game_index, rewards in enumerate(all_rewards)\n",
    "                                          for step, reward in enumerate(rewards)], axis=0)\n",
    "            feed_dict[gradient_placeholder] = mean_gradients\n",
    "        sess.run(training_op, feed_dict=feed_dict)\n",
    "        if iteration % save_iterations == 0:\n",
    "            saver.save(sess, \"./my_policy_net_pg.ckpt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "env.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
      "INFO:tensorflow:Restoring parameters from ./my_policy_net_pg.ckpt\n"
     ]
    },
    {
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       "My4xMDA=\n",
       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = render_policy_net(\"./my_policy_net_pg.ckpt\", action, X, n_max_steps=1000)\n",
    "video = plot_animation(frames, figsize=(6,4))\n",
    "HTML(video.to_html5_video())  # HTML5 동영상으로 만들어 줍니다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 마르코프 연쇄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "상태: 0 0 3 \n",
      "상태: 0 1 2 1 2 1 2 1 2 1 3 \n",
      "상태: 0 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 3 \n",
      "상태: 0 3 \n",
      "상태: 0 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 3 \n",
      "상태: 0 1 3 \n",
      "상태: 0 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 ...\n",
      "상태: 0 0 3 \n",
      "상태: 0 0 0 1 2 1 2 1 3 \n",
      "상태: 0 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 3 \n"
     ]
    }
   ],
   "source": [
    "transition_probabilities = [\n",
    "        [0.7, 0.2, 0.0, 0.1],  # s0에서 s0, s1, s2, s3으로\n",
    "        [0.0, 0.0, 0.9, 0.1],  # s1에서 ...\n",
    "        [0.0, 1.0, 0.0, 0.0],  # s2에서 ...\n",
    "        [0.0, 0.0, 0.0, 1.0],  # s3에서 ...\n",
    "    ]\n",
    "\n",
    "n_max_steps = 50\n",
    "\n",
    "def print_sequence(start_state=0):\n",
    "    current_state = start_state\n",
    "    print(\"상태:\", end=\" \")\n",
    "    for step in range(n_max_steps):\n",
    "        print(current_state, end=\" \")\n",
    "        if current_state == 3:\n",
    "            break\n",
    "        current_state = np.random.choice(range(4), p=transition_probabilities[current_state])\n",
    "    else:\n",
    "        print(\"...\", end=\"\")\n",
    "    print()\n",
    "\n",
    "for _ in range(10):\n",
    "    print_sequence()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 마르코프 결정 과정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "policy_fire\n",
      "상태 (+보상): 0 (10) 0 (10) 0 1 (-50) 2 2 2 (40) 0 (10) 0 (10) 0 (10) ... 전체 보상 = 210\n",
      "상태 (+보상): 0 1 (-50) 2 (40) 0 (10) 0 (10) 0 1 (-50) 2 2 (40) 0 (10) ... 전체 보상 = 70\n",
      "상태 (+보상): 0 (10) 0 1 (-50) 2 (40) 0 (10) 0 (10) 0 (10) 0 (10) 0 (10) 0 (10) ... 전체 보상 = 70\n",
      "상태 (+보상): 0 1 (-50) 2 1 (-50) 2 (40) 0 (10) 0 1 (-50) 2 (40) 0 ... 전체 보상 = -10\n",
      "상태 (+보상): 0 1 (-50) 2 (40) 0 (10) 0 (10) 0 1 (-50) 2 (40) 0 (10) 0 (10) ... 전체 보상 = 290\n",
      "요약: 평균=121.1, 표준 편차=129.333766, 최소=-330, 최대=470\n",
      "\n",
      "policy_random\n",
      "상태 (+보상): 0 1 (-50) 2 1 (-50) 2 (40) 0 1 (-50) 2 2 (40) 0 ... 전체 보상 = -60\n",
      "상태 (+보상): 0 (10) 0 0 0 0 0 (10) 0 0 0 (10) 0 ... 전체 보상 = -30\n",
      "상태 (+보상): 0 1 1 (-50) 2 (40) 0 0 1 1 1 1 ... 전체 보상 = 10\n",
      "상태 (+보상): 0 (10) 0 (10) 0 0 0 0 1 (-50) 2 (40) 0 0 ... 전체 보상 = 0\n",
      "상태 (+보상): 0 0 (10) 0 1 (-50) 2 (40) 0 0 0 0 (10) 0 (10) ... 전체 보상 = 40\n",
      "요약: 평균=-22.1, 표준 편차=88.152740, 최소=-380, 최대=200\n",
      "\n",
      "policy_safe\n",
      "상태 (+보상): 0 1 1 1 1 1 1 1 1 1 ... 전체 보상 = 0\n",
      "상태 (+보상): 0 1 1 1 1 1 1 1 1 1 ... 전체 보상 = 0\n",
      "상태 (+보상): 0 (10) 0 (10) 0 (10) 0 1 1 1 1 1 1 ... 전체 보상 = 30\n",
      "상태 (+보상): 0 (10) 0 1 1 1 1 1 1 1 1 ... 전체 보상 = 10\n",
      "상태 (+보상): 0 1 1 1 1 1 1 1 1 1 ... 전체 보상 = 0\n",
      "요약: 평균=22.3, 표준 편차=26.244312, 최소=0, 최대=170\n",
      "\n"
     ]
    }
   ],
   "source": [
    "transition_probabilities = [\n",
    "        [[0.7, 0.3, 0.0], [1.0, 0.0, 0.0], [0.8, 0.2, 0.0]], # s0에서, 행동 a0이 선택되면 0.7의 확률로 상태 s0로 가고 0.3의 확률로 상태 s1로 가는 식입니다.\n",
    "        [[0.0, 1.0, 0.0], None, [0.0, 0.0, 1.0]],\n",
    "        [None, [0.8, 0.1, 0.1], None],\n",
    "    ]\n",
    "\n",
    "rewards = [\n",
    "        [[+10, 0, 0], [0, 0, 0], [0, 0, 0]],\n",
    "        [[0, 0, 0], [0, 0, 0], [0, 0, -50]],\n",
    "        [[0, 0, 0], [+40, 0, 0], [0, 0, 0]],\n",
    "    ]\n",
    "\n",
    "possible_actions = [[0, 1, 2], [0, 2], [1]]\n",
    "\n",
    "def policy_fire(state):\n",
    "    return [0, 2, 1][state]\n",
    "\n",
    "def policy_random(state):\n",
    "    return np.random.choice(possible_actions[state])\n",
    "\n",
    "def policy_safe(state):\n",
    "    return [0, 0, 1][state]\n",
    "\n",
    "class MDPEnvironment(object):\n",
    "    def __init__(self, start_state=0):\n",
    "        self.start_state=start_state\n",
    "        self.reset()\n",
    "    def reset(self):\n",
    "        self.total_rewards = 0\n",
    "        self.state = self.start_state\n",
    "    def step(self, action):\n",
    "        next_state = np.random.choice(range(3), p=transition_probabilities[self.state][action])\n",
    "        reward = rewards[self.state][action][next_state]\n",
    "        self.state = next_state\n",
    "        self.total_rewards += reward\n",
    "        return self.state, reward\n",
    "\n",
    "def run_episode(policy, n_steps, start_state=0, display=True):\n",
    "    env = MDPEnvironment()\n",
    "    if display:\n",
    "        print(\"상태 (+보상):\", end=\" \")\n",
    "    for step in range(n_steps):\n",
    "        if display:\n",
    "            if step == 10:\n",
    "                print(\"...\", end=\" \")\n",
    "            elif step < 10:\n",
    "                print(env.state, end=\" \")\n",
    "        action = policy(env.state)\n",
    "        state, reward = env.step(action)\n",
    "        if display and step < 10:\n",
    "            if reward:\n",
    "                print(\"({})\".format(reward), end=\" \")\n",
    "    if display:\n",
    "        print(\"전체 보상 =\", env.total_rewards)\n",
    "    return env.total_rewards\n",
    "\n",
    "for policy in (policy_fire, policy_random, policy_safe):\n",
    "    all_totals = []\n",
    "    print(policy.__name__)\n",
    "    for episode in range(1000):\n",
    "        all_totals.append(run_episode(policy, n_steps=100, display=(episode<5)))\n",
    "    print(\"요약: 평균={:.1f}, 표준 편차={:1f}, 최소={}, 최대={}\".format(np.mean(all_totals), np.std(all_totals), np.min(all_totals), np.max(all_totals)))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q-러닝"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q-러닝은 에이전트가 플레이하는 것(가령, 랜덤하게)을 보고 점진적으로 Q-가치 추정을 향상시킵니다. 정확한 (또는 충분히 이에 가까운) Q-가치가 추정되면 최적의 정책은 가장 높은 Q-가치(즉, 그리디 정책)를 가진 행동을 선택하는 것이 됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_states = 3\n",
    "n_actions = 3\n",
    "n_steps = 20000\n",
    "alpha = 0.01\n",
    "gamma = 0.99\n",
    "exploration_policy = policy_random\n",
    "q_values = np.full((n_states, n_actions), -np.inf)\n",
    "for state, actions in enumerate(possible_actions):\n",
    "    q_values[state][actions]=0\n",
    "\n",
    "env = MDPEnvironment()\n",
    "for step in range(n_steps):\n",
    "    action = exploration_policy(env.state)\n",
    "    state = env.state\n",
    "    next_state, reward = env.step(action)\n",
    "    next_value = np.max(q_values[next_state]) # 그리디한 정책\n",
    "    q_values[state, action] = (1-alpha)*q_values[state, action] + alpha*(reward + gamma * next_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimal_policy(state):\n",
    "    return np.argmax(q_values[state])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[39.13508139, 38.88079412, 35.23025716],\n",
       "       [18.9117071 ,        -inf, 20.54567816],\n",
       "       [       -inf, 72.53192111,        -inf]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "상태 (+보상): 0 (10) 0 (10) 0 1 (-50) 2 (40) 0 (10) 0 1 (-50) 2 (40) 0 (10) ... 전체 보상 = 230\n",
      "상태 (+보상): 0 (10) 0 (10) 0 (10) 0 1 (-50) 2 2 1 (-50) 2 (40) 0 (10) ... 전체 보상 = 90\n",
      "상태 (+보상): 0 1 (-50) 2 (40) 0 (10) 0 (10) 0 (10) 0 (10) 0 (10) 0 (10) 0 (10) ... 전체 보상 = 170\n",
      "상태 (+보상): 0 1 (-50) 2 (40) 0 (10) 0 (10) 0 (10) 0 (10) 0 (10) 0 (10) 0 (10) ... 전체 보상 = 220\n",
      "상태 (+보상): 0 1 (-50) 2 (40) 0 (10) 0 1 (-50) 2 (40) 0 (10) 0 (10) 0 (10) ... 전체 보상 = -50\n",
      "요약: 평균=125.6, 표준 편차=127.363464, 최소=-290, 최대=500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "all_totals = []\n",
    "for episode in range(1000):\n",
    "    all_totals.append(run_episode(optimal_policy, n_steps=100, display=(episode<5)))\n",
    "print(\"요약: 평균={:.1f}, 표준 편차={:1f}, 최소={}, 최대={}\".format(np.mean(all_totals), np.std(all_totals), np.min(all_totals), np.max(all_totals)))\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DQN 알고리즘으로 미스팩맨 게임 학습하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 미스팩맨 환경 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(210, 160, 3)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env = gym.make(\"MsPacman-v0\")\n",
    "obs = env.reset()\n",
    "obs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Discrete(9)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.action_space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 전처리"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이미지 전처리는 선택 사항이지만 훈련 속도를 크게 높여 줍니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "mspacman_color = 210 + 164 + 74\n",
    "\n",
    "def preprocess_observation(obs):\n",
    "    img = obs[1:176:2, ::2] # 자르고 크기를 줄입니다.\n",
    "    img = img.sum(axis=2) # 흑백 스케일로 변환합니다.\n",
    "    img[img==mspacman_color] = 0 # 대비를 높입니다.\n",
    "    img = (img // 3 - 128).astype(np.int8) # -128~127 사이로 정규화합니다.\n",
    "    return img.reshape(88, 80, 1)\n",
    "\n",
    "img = preprocess_observation(obs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "노트 `preprocess_observation()` 함수가 책에 있는 것과 조금 다릅니다. 64비트 부동소수를 -1.0~1.0 사이로 나타내지 않고 부호있는 바이트(-128~127 사이)로 표현합니다. 이렇게 하는 이유는 재생 메모리가 약 8배나 적게 소모되기 때문입니다(52GB에서 6.5GB로). 정밀도를 감소시켜도 눈에 띄이게 훈련에 미치는 영향은 없습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.subplot(121)\n",
    "plt.title(\"원본 관측 (160×210 RGB)\")\n",
    "plt.imshow(obs)\n",
    "plt.axis(\"off\")\n",
    "plt.subplot(122)\n",
    "plt.title(\"전처리된 관측 (88×80 그레이스케일)\")\n",
    "plt.imshow(img.reshape(88, 80), interpolation=\"nearest\", cmap=\"gray\")\n",
    "plt.axis(\"off\")\n",
    "save_fig(\"preprocessing_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DQN 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "input_height = 88\n",
    "input_width = 80\n",
    "input_channels = 1\n",
    "conv_n_maps = [32, 64, 64]\n",
    "conv_kernel_sizes = [(8,8), (4,4), (3,3)]\n",
    "conv_strides = [4, 2, 1]\n",
    "conv_paddings = [\"SAME\"] * 3 \n",
    "conv_activation = [tf.nn.relu] * 3\n",
    "n_hidden_in = 64 * 11 * 10  # conv3은 11x10 크기의 64개의 맵을 가집니다\n",
    "n_hidden = 512\n",
    "hidden_activation = tf.nn.relu\n",
    "n_outputs = env.action_space.n  # 9개의 행동이 가능합니다\n",
    "initializer = tf.variance_scaling_initializer()\n",
    "\n",
    "def q_network(X_state, name):\n",
    "    prev_layer = X_state / 128.0 # 픽셀 강도를 [-1.0, 1.0] 범위로 스케일 변경합니다.\n",
    "    with tf.variable_scope(name) as scope:\n",
    "        for n_maps, kernel_size, strides, padding, activation in zip(\n",
    "                conv_n_maps, conv_kernel_sizes, conv_strides,\n",
    "                conv_paddings, conv_activation):\n",
    "            prev_layer = tf.layers.conv2d(\n",
    "                prev_layer, filters=n_maps, kernel_size=kernel_size,\n",
    "                strides=strides, padding=padding, activation=activation,\n",
    "                kernel_initializer=initializer)\n",
    "        last_conv_layer_flat = tf.reshape(prev_layer, shape=[-1, n_hidden_in])\n",
    "        hidden = tf.layers.dense(last_conv_layer_flat, n_hidden,\n",
    "                                 activation=hidden_activation,\n",
    "                                 kernel_initializer=initializer)\n",
    "        outputs = tf.layers.dense(hidden, n_outputs,\n",
    "                                  kernel_initializer=initializer)\n",
    "    trainable_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,\n",
    "                                       scope=scope.name)\n",
    "    trainable_vars_by_name = {var.name[len(scope.name):]: var\n",
    "                              for var in trainable_vars}\n",
    "    return outputs, trainable_vars_by_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_state = tf.placeholder(tf.float32, shape=[None, input_height, input_width,\n",
    "                                            input_channels])\n",
    "online_q_values, online_vars = q_network(X_state, name=\"q_networks/online\")\n",
    "target_q_values, target_vars = q_network(X_state, name=\"q_networks/target\")\n",
    "\n",
    "copy_ops = [target_var.assign(online_vars[var_name])\n",
    "            for var_name, target_var in target_vars.items()]\n",
    "copy_online_to_target = tf.group(*copy_ops)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'/conv2d/kernel:0': <tf.Variable 'q_networks/online/conv2d/kernel:0' shape=(8, 8, 1, 32) dtype=float32_ref>,\n",
       " '/conv2d/bias:0': <tf.Variable 'q_networks/online/conv2d/bias:0' shape=(32,) dtype=float32_ref>,\n",
       " '/conv2d_1/kernel:0': <tf.Variable 'q_networks/online/conv2d_1/kernel:0' shape=(4, 4, 32, 64) dtype=float32_ref>,\n",
       " '/conv2d_1/bias:0': <tf.Variable 'q_networks/online/conv2d_1/bias:0' shape=(64,) dtype=float32_ref>,\n",
       " '/conv2d_2/kernel:0': <tf.Variable 'q_networks/online/conv2d_2/kernel:0' shape=(3, 3, 64, 64) dtype=float32_ref>,\n",
       " '/conv2d_2/bias:0': <tf.Variable 'q_networks/online/conv2d_2/bias:0' shape=(64,) dtype=float32_ref>,\n",
       " '/dense/kernel:0': <tf.Variable 'q_networks/online/dense/kernel:0' shape=(7040, 512) dtype=float32_ref>,\n",
       " '/dense/bias:0': <tf.Variable 'q_networks/online/dense/bias:0' shape=(512,) dtype=float32_ref>,\n",
       " '/dense_1/kernel:0': <tf.Variable 'q_networks/online/dense_1/kernel:0' shape=(512, 9) dtype=float32_ref>,\n",
       " '/dense_1/bias:0': <tf.Variable 'q_networks/online/dense_1/bias:0' shape=(9,) dtype=float32_ref>}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "online_vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 0.001\n",
    "momentum = 0.95\n",
    "\n",
    "with tf.variable_scope(\"train\"):\n",
    "    X_action = tf.placeholder(tf.int32, shape=[None])\n",
    "    y = tf.placeholder(tf.float32, shape=[None, 1])\n",
    "    q_value = tf.reduce_sum(online_q_values * tf.one_hot(X_action, n_outputs),\n",
    "                            axis=1, keepdims=True)\n",
    "    error = tf.abs(y - q_value)\n",
    "    clipped_error = tf.clip_by_value(error, 0.0, 1.0)\n",
    "    linear_error = 2 * (error - clipped_error)\n",
    "    loss = tf.reduce_mean(tf.square(clipped_error) + linear_error)\n",
    "\n",
    "    global_step = tf.Variable(0, trainable=False, name='global_step')\n",
    "    optimizer = tf.train.MomentumOptimizer(learning_rate, momentum, use_nesterov=True)\n",
    "    training_op = optimizer.minimize(loss, global_step=global_step)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "노트: 처음 책을 쓸 때는 타깃 Q-가치(y)와 예측 Q-가치(q_value) 사이의 제곱 오차를 사용했습니다. 하지만 매우 잡음이 많은 경험 때문에 작은 오차(1.0 이하)에 대해서만 손실에 이차식을 사용하고, 큰 오차에 대해서는 위의 계산식처럼 선형적인 손실(절대 오차의 두 배)을 사용하는 것이 더 낫습니다. 이렇게 하면 큰 오차가 모델 파라미터를 너무 많이 변경하지 못합니다. 또 몇 가지 하이퍼파라미터를 조정했습니다(작은 학습률을 사용하고 논문에 따르면 적응적 경사 하강법 알고리즘이 이따금 나쁜 성능을 낼 수 있으므로 Adam 최적화대신 네스테로프 가속 경사를 사용합니다). 아래에서 몇 가지 다른 하이퍼파라미터도 수정했습니다(재생 메모리 크기 확대, e-그리디 정책을 위한 감쇠 단계 증가, 할인 계수 증가, 온라인 DQN에서 타깃 DQN으로 복사 빈도 축소 등입니다)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import deque\n",
    "\n",
    "replay_memory_size = 500000\n",
    "replay_memory = deque([], maxlen=replay_memory_size)\n",
    "\n",
    "def sample_memories(batch_size):\n",
    "    indices = np.random.permutation(len(replay_memory))[:batch_size]\n",
    "    cols = [[], [], [], [], []] # 상태, 행동, 보상, 다음 상태, 계속\n",
    "    for idx in indices:\n",
    "        memory = replay_memory[idx]\n",
    "        for col, value in zip(cols, memory):\n",
    "            col.append(value)\n",
    "    cols = [np.array(col) for col in cols]\n",
    "    return cols[0], cols[1], cols[2].reshape(-1, 1), cols[3], cols[4].reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ReplayMemory 클래스를 사용한 방법 =================="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "랜덤 억세스(random access)가 훨씬 빠르기 때문에 deque 대신에 ReplayMemory 클래스를 사용합니다(기여해 준 @NileshPS 님 감사합니다). 또 기본적으로 중복을 허용하여 샘플하면 큰 재생 메모리에서 중복을 허용하지 않고 샘플링하는 것보다 훨씬 빠릅니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ReplayMemory:\n",
    "    def __init__(self, maxlen):\n",
    "        self.maxlen = maxlen\n",
    "        self.buf = np.empty(shape=maxlen, dtype=np.object)\n",
    "        self.index = 0\n",
    "        self.length = 0\n",
    "        \n",
    "    def append(self, data):\n",
    "        self.buf[self.index] = data\n",
    "        self.length = min(self.length + 1, self.maxlen)\n",
    "        self.index = (self.index + 1) % self.maxlen\n",
    "    \n",
    "    def sample(self, batch_size, with_replacement=True):\n",
    "        if with_replacement:\n",
    "            indices = np.random.randint(self.length, size=batch_size) # 더 빠름\n",
    "        else:\n",
    "            indices = np.random.permutation(self.length)[:batch_size]\n",
    "        return self.buf[indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "replay_memory_size = 500000\n",
    "replay_memory = ReplayMemory(replay_memory_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_memories(batch_size):\n",
    "    cols = [[], [], [], [], []] # 상태, 행동, 보상, 다음 상태, 계속\n",
    "    for memory in replay_memory.sample(batch_size):\n",
    "        for col, value in zip(cols, memory):\n",
    "            col.append(value)\n",
    "    cols = [np.array(col) for col in cols]\n",
    "    return cols[0], cols[1], cols[2].reshape(-1, 1), cols[3], cols[4].reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ============================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "eps_min = 0.1\n",
    "eps_max = 1.0\n",
    "eps_decay_steps = 2000000\n",
    "\n",
    "def epsilon_greedy(q_values, step):\n",
    "    epsilon = max(eps_min, eps_max - (eps_max-eps_min) * step/eps_decay_steps)\n",
    "    if np.random.rand() < epsilon:\n",
    "        return np.random.randint(n_outputs) # 랜덤 행동\n",
    "    else:\n",
    "        return np.argmax(q_values) # 최적 행동"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_steps = 4000000  # 전체 훈련 스텝 횟수\n",
    "training_start = 10000  # 10,000번 게임을 반복한 후에 훈련을 시작합니다\n",
    "training_interval = 4  # 4번 게임을 반복하고 훈련 스텝을 실행합니다\n",
    "save_steps = 1000  # 1,000번 훈련 스텝마다 모델을 저장합니다\n",
    "copy_steps = 10000  # 10,000번 훈련 스텝마다 온라인 DQN을 타깃 DQN으로 복사합니다\n",
    "discount_rate = 0.99\n",
    "skip_start = 90  # 게임의 시작 부분은 스킵합니다 (시간 낭비이므로).\n",
    "batch_size = 50\n",
    "iteration = 0  # 게임 반복횟수\n",
    "checkpoint_path = \"./my_dqn.ckpt\"\n",
    "done = True # 환경을 리셋해야 합니다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "학습 과정을 트래킹하기 위해 몇 개의 변수가 필요합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_val = np.infty\n",
    "game_length = 0\n",
    "total_max_q = 0\n",
    "mean_max_q = 0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 훈련 반복 루프입니다!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./my_dqn.ckpt\n",
      "반복 13992\t훈련 스텝 3999999/4000000 (100.0)%\t손실 1.250408\t평균 최대-Q 221.025110    "
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    if os.path.isfile(checkpoint_path + \".index\"):\n",
    "        saver.restore(sess, checkpoint_path)\n",
    "    else:\n",
    "        init.run()\n",
    "        copy_online_to_target.run()\n",
    "    while True:\n",
    "        step = global_step.eval()\n",
    "        if step >= n_steps:\n",
    "            break\n",
    "        iteration += 1\n",
    "        print(\"\\r반복 {}\\t훈련 스텝 {}/{} ({:.1f})%\\t손실 {:5f}\\t평균 최대-Q {:5f}   \".format(\n",
    "            iteration, step, n_steps, step * 100 / n_steps, loss_val, mean_max_q), end=\"\")\n",
    "        if done: # 게임이 종료되면 다시 시작합니다\n",
    "            obs = env.reset()\n",
    "            for skip in range(skip_start): # 게임 시작 부분은 스킵합니다\n",
    "                obs, reward, done, info = env.step(0)\n",
    "            state = preprocess_observation(obs)\n",
    "\n",
    "        # 온라인 DQN이 해야할 행동을 평가합니다\n",
    "        q_values = online_q_values.eval(feed_dict={X_state: [state]})\n",
    "        action = epsilon_greedy(q_values, step)\n",
    "\n",
    "        # 온라인 DQN으로 게임을 플레이합니다.\n",
    "        obs, reward, done, info = env.step(action)\n",
    "        next_state = preprocess_observation(obs)\n",
    "\n",
    "        # 재생 메모리에 기록합니다\n",
    "        replay_memory.append((state, action, reward, next_state, 1.0 - done))\n",
    "        state = next_state\n",
    "\n",
    "        # 트래킹을 위해 통계값을 계산합니다 (책에는 없습니다)\n",
    "        total_max_q += q_values.max()\n",
    "        game_length += 1\n",
    "        if done:\n",
    "            mean_max_q = total_max_q / game_length\n",
    "            total_max_q = 0.0\n",
    "            game_length = 0\n",
    "\n",
    "        if iteration < training_start or iteration % training_interval != 0:\n",
    "            continue # 워밍엄 시간이 지난 후에 일정 간격으로 훈련합니다\n",
    "        \n",
    "        # 메모리에서 샘플링하여 타깃 Q-가치를 얻기 위해 타깃 DQN을 사용합니다\n",
    "        X_state_val, X_action_val, rewards, X_next_state_val, continues = (\n",
    "            sample_memories(batch_size))\n",
    "        next_q_values = target_q_values.eval(\n",
    "            feed_dict={X_state: X_next_state_val})\n",
    "        max_next_q_values = np.max(next_q_values, axis=1, keepdims=True)\n",
    "        y_val = rewards + continues * discount_rate * max_next_q_values\n",
    "\n",
    "        # 온라인 DQN을 훈련시킵니다\n",
    "        _, loss_val = sess.run([training_op, loss], feed_dict={\n",
    "            X_state: X_state_val, X_action: X_action_val, y: y_val})\n",
    "\n",
    "        # 온라인 DQN을 타깃 DQN으로 일정 간격마다 복사합니다\n",
    "        if step % copy_steps == 0:\n",
    "            copy_online_to_target.run()\n",
    "\n",
    "        # 일정 간격으로 저장합니다\n",
    "        if step % save_steps == 0:\n",
    "            saver.save(sess, checkpoint_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "아래 셀에서 에이전트를 테스트하기 위해 언제든지 위의 셀을 중지할 수 있습니다. 그런다음 다시 위의 셀을 실행하면 마지막으로 저장된 파라미터를 로드하여 훈련을 다시 시작할 것입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./my_dqn.ckpt\n"
     ]
    }
   ],
   "source": [
    "frames = []\n",
    "n_max_steps = 10000\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, checkpoint_path)\n",
    "\n",
    "    obs = env.reset()\n",
    "    for step in range(n_max_steps):\n",
    "        state = preprocess_observation(obs)\n",
    "\n",
    "        # 온라인 DQN이 해야할 행동을 평가합니다\n",
    "        q_values = online_q_values.eval(feed_dict={X_state: [state]})\n",
    "        action = np.argmax(q_values)\n",
    "\n",
    "        # 온라인 DQN이 게임을 플레이합니다\n",
    "        obs, reward, done, info = env.step(action)\n",
    "\n",
    "        img = env.render(mode=\"rgb_array\")\n",
    "        frames.append(img)\n",
    "\n",
    "        if done:\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
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       "AAGpAAAATAAAACgAAAAiAAAAIAAAAD0AAAFNAAAAOAAAAEAAAAAiAAABhQAAACwAAAA9AAAAEQAA\n",
       "ALMAAACPAAAAIgAAACMAAAAjAAAAhgAAACEAAAAhAAAAJQAAACEAAAAQAAAAGwAAABRzdGNvAAAA\n",
       "AAAAAAEAAAAsAAAAYnVkdGEAAABabWV0YQAAAAAAAAAhaGRscgAAAAAAAAAAbWRpcmFwcGwAAAAA\n",
       "AAAAAAAAAAAtaWxzdAAAACWpdG9vAAAAHWRhdGEAAAABAAAAAExhdmY1Ny44My4xMDA=\n",
       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video = plot_animation(frames, figsize=(5,6))\n",
    "HTML(video.to_html5_video())  # HTML5 동영상으로 만들어 줍니다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 추가 자료"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 브레이크아웃(Breakout)을 위한 전처리"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다음은 Breakout-v0 아타리 게임을 위한 DQN을 훈련시키기 위해 사용할 수 있는 전처리 함수입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_observation(obs):\n",
    "    img = obs[34:194:2, ::2] # 자르고 크기를 줄입니다.\n",
    "    return np.mean(img, axis=2).reshape(80, 80) / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = gym.make(\"Breakout-v0\")\n",
    "obs = env.reset()\n",
    "for step in range(10):\n",
    "    obs, _, _, _ = env.step(1)\n",
    "\n",
    "img = preprocess_observation(obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.subplot(121)\n",
    "plt.title(\"원본 관측 (160×210 RGB)\")\n",
    "plt.imshow(obs)\n",
    "plt.axis(\"off\")\n",
    "plt.subplot(122)\n",
    "plt.title(\"전처리된 관측 (80×80 그레이스케일)\")\n",
    "plt.imshow(img, interpolation=\"nearest\", cmap=\"gray\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "여기서 볼 수 있듯이 하나의 이미지는 볼의 방향과 속도에 대한 정보가 없습니다. 이 정보들은 이 게임에 아주 중요합니다. 이런 이유로 실제로 몇 개의 연속된 관측을 연결하여 환경의 상태를 표현하는 것이 좋습니다. 한 가지 방법은 관측당 하나의 채널을 할당하여 멀티 채널 이미지를 만드는 것입니다. 다른 방법은 `np.max()` 함수를 사용해 최근의 관측을 모두 싱글 채널 이미지로 합치는 것입니다. 여기에서는 이전 이미지를 흐리게하여 DQN이 현재와 이전을 구분할 수 있도록 했습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import deque\n",
    "\n",
    "def combine_observations_multichannel(preprocessed_observations):\n",
    "    return np.array(preprocessed_observations).transpose([1, 2, 0])\n",
    "\n",
    "def combine_observations_singlechannel(preprocessed_observations, dim_factor=0.5):\n",
    "    dimmed_observations = [obs * dim_factor**index\n",
    "                           for index, obs in enumerate(reversed(preprocessed_observations))]\n",
    "    return np.max(np.array(dimmed_observations), axis=0)\n",
    "\n",
    "n_observations_per_state = 3\n",
    "preprocessed_observations = deque([], maxlen=n_observations_per_state)\n",
    "\n",
    "obs = env.reset()\n",
    "for step in range(10):\n",
    "    obs, _, _, _ = env.step(1)\n",
    "    preprocessed_observations.append(preprocess_observation(obs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img1 = combine_observations_multichannel(preprocessed_observations)\n",
    "img2 = combine_observations_singlechannel(preprocessed_observations)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.subplot(121)\n",
    "plt.title(\"멀티 채널 상태\")\n",
    "plt.imshow(img1, interpolation=\"nearest\")\n",
    "plt.axis(\"off\")\n",
    "plt.subplot(122)\n",
    "plt.title(\"싱글 채널 상태\")\n",
    "plt.imshow(img2, interpolation=\"nearest\", cmap=\"gray\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 연습문제 해답"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. to 7."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "부록 A 참조."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. BipedalWalker-v2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*문제: 정책 그래디언트를 사용해 OpenAI 짐의 ‘BypedalWalker-v2’를 훈련시켜보세요*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "env = gym.make(\"BipedalWalker-v2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "노트: 만약 `BipedalWalker-v2` 환경을 만들 때 \"`module 'Box2D._Box2D' has no attribute 'RAND_LIMIT'`\"와 같은 이슈가 발생하면 다음과 같이 해보세요:\n",
    "```\n",
    "$ pip uninstall Box2D-kengz\n",
    "$ pip install git+https://github.com/pybox2d/pybox2d\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs = env.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = env.render(mode=\"rgb_array\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img)\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.74731824e-03, -2.46529956e-05,  1.91775329e-03, -1.59998345e-02,\n",
       "        9.18128639e-02, -2.53078272e-03,  8.60352650e-01,  3.27264580e-03,\n",
       "        1.00000000e+00,  3.22182216e-02, -2.53060926e-03,  8.53903249e-01,\n",
       "        1.78979384e-03,  1.00000000e+00,  4.40814108e-01,  4.45820212e-01,\n",
       "        4.61422890e-01,  4.89550292e-01,  5.34102917e-01,  6.02461159e-01,\n",
       "        7.09149063e-01,  8.85932028e-01,  1.00000000e+00,  1.00000000e+00])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 24개의 숫자에 대한 의미는 [온라인 문서](https://github.com/openai/gym/wiki/BipedalWalker-v2)를 참고하세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Box(4,)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.action_space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1., -1., -1., -1.], dtype=float32)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.action_space.low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1.], dtype=float32)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.action_space.high"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이는 각 다리의 엉덩이 관절의 토크와 발목 관절 토크를 제어하는 연속적인 4D 행동 공간입니다(-1에서 1까지). 연속적인 행동 공간을 다루기 위한 한 가지 방법은 이를 불연속적으로 나누는 것입니다. 예를 들어, 가능한 토크 값을 3개의 값 -1.0, 0.0, 1.0으로 제한할 수 있습니다. 이렇게 하면 가능한 행동은 $3^4=81$개가 됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(81, 4)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "possible_torques = np.array([-1.0, 0.0, 1.0])\n",
    "possible_actions = np.array(list(product(possible_torques, possible_torques, possible_torques, possible_torques)))\n",
    "possible_actions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "# 1. 네트워크 구조를 정의합니다\n",
    "n_inputs = env.observation_space.shape[0]  # == 24\n",
    "n_hidden = 10\n",
    "n_outputs = len(possible_actions) # == 625\n",
    "initializer = tf.variance_scaling_initializer()\n",
    "\n",
    "# 2. 신경망을 만듭니다\n",
    "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
    "\n",
    "hidden = tf.layers.dense(X, n_hidden, activation=tf.nn.selu,\n",
    "                         kernel_initializer=initializer)\n",
    "logits = tf.layers.dense(hidden, n_outputs,\n",
    "                         kernel_initializer=initializer)\n",
    "outputs = tf.nn.softmax(logits)\n",
    "\n",
    "# 3. 추정 확률에 기초하여 무작위한 행동을 선택합니다\n",
    "action_index = tf.squeeze(tf.multinomial(logits, num_samples=1), axis=-1)\n",
    "\n",
    "# 4. 훈련\n",
    "learning_rate = 0.01\n",
    "\n",
    "y = tf.one_hot(action_index, depth=len(possible_actions))\n",
    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(labels=y, logits=logits)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
    "grads_and_vars = optimizer.compute_gradients(cross_entropy)\n",
    "gradients = [grad for grad, variable in grads_and_vars]\n",
    "gradient_placeholders = []\n",
    "grads_and_vars_feed = []\n",
    "for grad, variable in grads_and_vars:\n",
    "    gradient_placeholder = tf.placeholder(tf.float32, shape=grad.get_shape())\n",
    "    gradient_placeholders.append(gradient_placeholder)\n",
    "    grads_and_vars_feed.append((gradient_placeholder, variable))\n",
    "training_op = optimizer.apply_gradients(grads_and_vars_feed)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "아직 훈련되지 않았지만 이 정책 네트워크를 실행해 보죠."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_bipedal_walker(model_path=None, n_max_steps = 1000):\n",
    "    env = gym.make(\"BipedalWalker-v2\")\n",
    "    frames = []\n",
    "    with tf.Session() as sess:\n",
    "        if model_path is None:\n",
    "            init.run()\n",
    "        else:\n",
    "            saver.restore(sess, model_path)\n",
    "        obs = env.reset()\n",
    "        for step in range(n_max_steps):\n",
    "            img = env.render(mode=\"rgb_array\")\n",
    "            frames.append(img)\n",
    "            action_index_val = action_index.eval(feed_dict={X: obs.reshape(1, n_inputs)})\n",
    "            action = possible_actions[action_index_val]\n",
    "            obs, reward, done, info = env.step(action[0])\n",
    "            if done:\n",
    "                break\n",
    "    env.close()\n",
    "    return frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n"
     ]
    },
    {
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       "AAEAAAAATGF2ZjU3LjgzLjEwMA==\n",
       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = run_bipedal_walker()\n",
    "video = plot_animation(frames)\n",
    "HTML(video.to_html5_video())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "안되네요, 걷지를 못합니다. 그럼 훈련시켜 보죠!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 1000/1000"
     ]
    }
   ],
   "source": [
    "n_games_per_update = 10\n",
    "n_max_steps = 1000\n",
    "n_iterations = 1000\n",
    "save_iterations = 10\n",
    "discount_rate = 0.95\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for iteration in range(n_iterations):\n",
    "        print(\"\\rIteration: {}/{}\".format(iteration + 1, n_iterations), end=\"\")\n",
    "        all_rewards = []\n",
    "        all_gradients = []\n",
    "        for game in range(n_games_per_update):\n",
    "            current_rewards = []\n",
    "            current_gradients = []\n",
    "            obs = env.reset()\n",
    "            for step in range(n_max_steps):\n",
    "                action_index_val, gradients_val = sess.run([action_index, gradients],\n",
    "                                                           feed_dict={X: obs.reshape(1, n_inputs)})\n",
    "                action = possible_actions[action_index_val]\n",
    "                obs, reward, done, info = env.step(action[0])\n",
    "                current_rewards.append(reward)\n",
    "                current_gradients.append(gradients_val)\n",
    "                if done:\n",
    "                    break\n",
    "            all_rewards.append(current_rewards)\n",
    "            all_gradients.append(current_gradients)\n",
    "\n",
    "        all_rewards = discount_and_normalize_rewards(all_rewards, discount_rate=discount_rate)\n",
    "        feed_dict = {}\n",
    "        for var_index, gradient_placeholder in enumerate(gradient_placeholders):\n",
    "            mean_gradients = np.mean([reward * all_gradients[game_index][step][var_index]\n",
    "                                      for game_index, rewards in enumerate(all_rewards)\n",
    "                                          for step, reward in enumerate(rewards)], axis=0)\n",
    "            feed_dict[gradient_placeholder] = mean_gradients\n",
    "        sess.run(training_op, feed_dict=feed_dict)\n",
    "        if iteration % save_iterations == 0:\n",
    "            saver.save(sess, \"./my_bipedal_walker_pg.ckpt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
      "INFO:tensorflow:Restoring parameters from ./my_bipedal_walker_pg.ckpt\n"
     ]
    },
    {
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       "AQAAAABMYXZmNTcuODMuMTAw\n",
       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = run_bipedal_walker(\"./my_bipedal_walker_pg.ckpt\")\n",
    "video = plot_animation(frames)\n",
    "HTML(video.to_html5_video())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "최상의 결과는 아니지만 적어도 직립해서 (느리게) 오른쪽으로 이동합니다(훨씬 더 오랜 학습이 필요할 것 같습니다 :). 이 문제에 대한 더 좋은 방법은 액터-크리틱(actor-critic) 알고리즘을 사용하는 것입니다. 이 방법은 행동 공간을 이산화할 필요가 없으므로 훨씬 빠르게 수렴합니다. 이에 대한 더 자세한 내용은 Yash Patel가 쓴 멋진 [블로그 포스트](https://towardsdatascience.com/reinforcement-learning-w-keras-openai-actor-critic-models-f084612cfd69)를 참고하세요."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.\n",
    "**Comming soon**"
   ]
  }
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