lab_1_intro_rl_Tumas_Velez.ipynb

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    "<a href=\"https://colab.research.google.com/github/jbarsce/AprendizajePorRefuerzos/blob/master/lab_1_intro_rl.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4spynKMzGS4t"
   },
   "source": [
    "# Notebook 1: Introducción al aprendizaje por refuerzos\n",
    "\n",
    "Curso Aprendizaje por Refuerzos, Diplomatura en Ciencia de Datos, Aprendizaje Automático y sus Aplicaciones\n",
    "\n",
    "FaMAF, 2023"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2mlW-PAFGS4z"
   },
   "source": [
    "## Introducción\n",
    "\n",
    "En el siguiente notebook se muestra cómo ejecutar agentes de aprendizaje por refuerzos, los cuáles son necesarios para realizar este Lab."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZlIwTm_eyO0T"
   },
   "source": [
    "### Repaso rápido\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "87Luqo5UyO0T",
    "tags": []
   },
   "source": [
    "\n",
    "* Recompensa: señal $r$ recibida desde el entorno que recompensa o castiga el agente según su desempeño con respecto al objetivo de la tarea.\n",
    "\n",
    "* Valor: función $v_\\pi (s)$ que establece cuánto el agente espera percibir de recompensa al seguir la política $\\pi$ partiendo desde el estado $s$. También se la suele expresar como $Q_\\pi(s,a)$, indicando cuánto el agente espera percibir siguiendo la política $\\pi$ partiendo desde el estado $s$ y siguiendo la acción $a$.\n",
    "\n",
    "* Política: función $\\pi(s) \\to a$ que mapea un estado a una acción. Se suele expresar como probabilidad de elegir la acción $\\pi(a \\mid s)$. La política $\\epsilon$-greedy, en donde $\\epsilon$ es la probabilidad de exploración (normalmente menor que la probabilidad de explotación) está dada por $\\pi(a \\mid s) = 1 - \\epsilon$ si $a$ es la mejor acción, caso contrario pasa a estar dada por $\\pi(a \\mid s) = \\epsilon$.\n",
    "\n",
    "Por otra parte, en la política Softmax, no se busca la acción con máxima probabilidad sino que se computa la probabilidad de cada una mediante la función Softmax y se realiza un sorteo entre ellas pesado por la misma. Así, para cada acción $a$, $$\\pi(a \\mid s) = \\frac{e^{Q(s,a)/\\tau}}{\\sum_{\\widetilde{a} \\in A}e^{Q(s,\\widetilde{a})/\\tau}}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V65VukjpyO0T",
    "tags": []
   },
   "source": [
    "En este notebook vemos dos algoritmos para actualizar la función de valor (y, por lo tanto, la política de selección de acciones):\n",
    "\n",
    "* Actualización por SARSA (on-policy).\n",
    "\n",
    "$$Q(s,a) \\gets Q(s,a) + \\alpha (r + \\gamma Q(s',a') - Q(s,a))$$\n",
    "\n",
    "Algoritmo completo (a modo de referencia):\n",
    "\n",
    "![Algoritmo SARSA](https://github.com/DiploDatos/AprendizajePorRefuerzos/blob/master/images/sarsa.png?raw=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iHP2ShFqyO0U",
    "tags": []
   },
   "source": [
    "* Actualización por Q-Learning (off-policy)\n",
    "\n",
    "$$Q(s,a) \\gets Q(s,a) + \\alpha (r + \\gamma \\arg\\max_{a'} Q(s',a') - Q(s,a))$$\n",
    "\n",
    "Algoritmo completo (a modo de referencia):\n",
    "\n",
    "![Algoritmo Q-Learning](https://github.com/DiploDatos/AprendizajePorRefuerzos/blob/master/images/q_learning.png?raw=1)\n",
    "\n",
    "Fuente de las imágenes: capítulo 6 de [Reinforcement Learning: An Introduction](http://www.incompleteideas.net/book/the-book.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a7BO38B6GS4z"
   },
   "source": [
    "## Librería a usar: Librería OpenAI Gym\n",
    "\n",
    "[OpenAI Gym](https://gym.openai.com/) (Brockman et. al., 2016) es una librería de OpenAI que ofrece entornos y una interfaz estándar con la cuál probar nuestros agentes. Su objetivo es proveer benchmarks unificados para ver el desempeño de algoritmos en el entorno y así poder saber con facilidad cómo es su desempeño comparado con los demás. Parte de la siguiente sección está basada en la [documentación oficial de OpenAI](https://gym.openai.com/docs/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YY7NVUdpGS40"
   },
   "source": [
    "La interfaz principal de los ambientes de gym es la interfaz Env. La misma posee cinco métodos principales:\n",
    "\n",
    "* ```reset(self, seed)``` : Reinicia el estado del entorno, a su estado inicial, devolviendo una observación de dicho estado. Opcionalmente, establece la semilla aleatoria del generador de números aleatorios del presente entorno.\n",
    "\n",
    "* ```step(self, action)``` : \"Avanza\" un timestep del ambiente. Devuelve: ```observation, reward, terminated, truncated, info```.\n",
    "\n",
    "* ```render(self)``` : Muestra en pantalla una parte del ambiente.\n",
    "\n",
    "* ```close(self)``` : Finaliza con la instancia del agente.\n",
    "\n",
    "\n",
    "Por otra parte, cada entorno posee los siguientes tres atributos principales:\n",
    "\n",
    "* ```action_space``` : El objeto de tipo Space correspondiente al espacio de acciones válidas.\n",
    "\n",
    "* ```observation_space``` : El objeto de tipo Space correspondiente a todos los rangos posibles de observaciones.\n",
    "\n",
    "* ```reward_range``` : Tupla que contiene los valores mínimo y máximo de recompensa posible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TTMpnXzcGS40"
   },
   "source": [
    "Algunas de las ejecuciones contienen videos. Para poder verlos se necesita previamente instalar la librería ffmpeg; para instalarla desde Linux ejecutar en consola\n",
    "\n",
    "```apt-get install ffmpeg```\n",
    "\n",
    "desde Mac, reemplazar *apt-get* por *brew*\n",
    "\n",
    "desde Windows, descargarla desde\n",
    "\n",
    "[https://ffmpeg.org/download.html](https://ffmpeg.org/download.html)\n",
    "\n",
    "(Nota: las animaciones son a modo ilustrativo, si no se desea instalar la librería se puede directamente eliminar la línea de código donde se llama al método ``env.render(mode='human')``)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TZDT_O0UGS40"
   },
   "source": [
    "Código básico de importación y funciones de graficación (no modificar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hideCode": true,
    "id": "5jUjOuhYGS42",
    "tags": [
     "hide-cell"
    ]
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   "outputs": [],
   "source": [
    "# @title Código básico importacion y utilerías (no modificar)\n",
    "\n",
    "import itertools\n",
    "from typing import Any, Callable, Tuple\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def plot_reward_per_episode(reward_ep) -> None:\n",
    "    episode_rewards = np.array(reward_ep)\n",
    "\n",
    "    # se suaviza la curva de convergencia\n",
    "    episode_number = np.linspace(\n",
    "        1, len(episode_rewards) + 1, len(episode_rewards) + 1)\n",
    "    acumulated_rewards = np.cumsum(episode_rewards)\n",
    "\n",
    "    reward_per_episode = [\n",
    "        acumulated_rewards[i] / episode_number[i]\n",
    "        for i in range(len(acumulated_rewards))\n",
    "    ]\n",
    "\n",
    "    plt.plot(reward_per_episode)\n",
    "    plt.title(\"Recompensa acumulada por episodio\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def plot_steps_per_episode(timesteps_ep) -> None:\n",
    "    # se muestra la curva de aprendizaje de los pasos por episodio\n",
    "    episode_steps = np.array(timesteps_ep)\n",
    "    plt.plot(np.array(range(0, len(episode_steps))), episode_steps)\n",
    "    plt.title(\"Pasos (timesteps) por episodio\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def plot_steps_per_episode_smooth(timesteps_ep) -> None:\n",
    "    episode_steps = np.array(timesteps_ep)\n",
    "\n",
    "    # se suaviza la curva de aprendizaje\n",
    "    episode_number = np.linspace(\n",
    "        1, len(episode_steps) + 1, len(episode_steps) + 1)\n",
    "    acumulated_steps = np.cumsum(episode_steps)\n",
    "\n",
    "    steps_per_episode = [\n",
    "        acumulated_steps[i] / episode_number[i] for i in range(\n",
    "            len(acumulated_steps))\n",
    "    ]\n",
    "\n",
    "    plt.plot(steps_per_episode)\n",
    "    plt.title(\"Pasos (timesteps) acumulados por episodio\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def draw_value_matrix(q) -> None:\n",
    "    n_rows = 4\n",
    "    n_columns = 12\n",
    "    n_actions = 4\n",
    "\n",
    "    # se procede con los cálculos previos a la graficación de la matriz de valor\n",
    "    q_value_matrix = np.empty((n_rows, n_columns))\n",
    "    for row in range(n_rows):\n",
    "        for column in range(n_columns):\n",
    "            state_values = []\n",
    "\n",
    "            for action in range(n_actions):\n",
    "                state_values.append(\n",
    "                    q.get((row * n_columns + column, action), -100))\n",
    "\n",
    "            maximum_value = max(\n",
    "                state_values\n",
    "            )  # determinamos la acción que arroja máximo valor\n",
    "\n",
    "            q_value_matrix[row, column] = maximum_value\n",
    "\n",
    "    # el valor del estado objetivo se asigna en -1 (reward recibido al llegar)\n",
    "    # para que se coloree de forma apropiada\n",
    "    q_value_matrix[3, 11] = -1\n",
    "\n",
    "    # se grafica la matriz de valor\n",
    "    plt.imshow(q_value_matrix, cmap=plt.cm.RdYlGn)\n",
    "    plt.tight_layout()\n",
    "    plt.colorbar()\n",
    "\n",
    "    for row, column in itertools.product(\n",
    "        range(q_value_matrix.shape[0]), range(q_value_matrix.shape[1])\n",
    "    ):\n",
    "        left_action = q.get((row * n_columns + column, 3), -1000)\n",
    "        down_action = q.get((row * n_columns + column, 2), -1000)\n",
    "        right_action = q.get((row * n_columns + column, 1), -1000)\n",
    "        up_action = q.get((row * n_columns + column, 0), -1000)\n",
    "\n",
    "        arrow_direction = \"D\"\n",
    "        best_action = down_action\n",
    "\n",
    "        if best_action < right_action:\n",
    "            arrow_direction = \"R\"\n",
    "            best_action = right_action\n",
    "        if best_action < left_action:\n",
    "            arrow_direction = \"L\"\n",
    "            best_action = left_action\n",
    "        if best_action < up_action:\n",
    "            arrow_direction = \"U\"\n",
    "            best_action = up_action\n",
    "        if best_action == -1:\n",
    "            arrow_direction = \"\"\n",
    "\n",
    "        # notar que column, row están invertidos en orden en la línea de abajo\n",
    "        # porque representan a x,y del plot\n",
    "        plt.text(column, row, arrow_direction, horizontalalignment=\"center\")\n",
    "\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.show()\n",
    "\n",
    "    print(\"\\n Matriz de mejor acción-valor (en números): \\n\\n\", q_value_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ejemplo: agente CartPole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YPkxgtSiGS43",
    "outputId": "144a3a83-7886-431e-e98e-a9b858d3483c"
   },
   "outputs": [],
   "source": [
    "from IPython.display import clear_output\n",
    "#!pip install gymnasium\n",
    "\n",
    "try:\n",
    "  import google.colab\n",
    "  IN_COLAB = True\n",
    "\n",
    "  !pip install gymnasium\n",
    "\n",
    "except:\n",
    "  IN_COLAB = False\n",
    "\n",
    "import gymnasium as gym\n",
    "\n",
    "# no es posible mostrar videos de ejecución del agente desde Colab\n",
    "if not IN_COLAB:\n",
    "\n",
    "    env = gym.make('CartPole-v0')\n",
    "    env.reset()\n",
    "    env.render()\n",
    "\n",
    "    for _ in range(500):\n",
    "        env.render()\n",
    "        # se ejecuta una acción aleatoria\n",
    "        obs, reward, terminated, truncated, info = env.step(\n",
    "           env.action_space.sample())\n",
    "\n",
    "        done = terminated or truncated\n",
    "        if done:\n",
    "            env.reset()\n",
    "    env.close()\n",
    "    clear_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UQ_A87JtGS43"
   },
   "source": [
    "Ejemplo: agente Mountain Car"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "QHDTjlpEGS43"
   },
   "outputs": [],
   "source": [
    "if not IN_COLAB:\n",
    "    env = gym.make('MountainCar-v0')\n",
    "    obs = env.reset()\n",
    "    for t in range(500):\n",
    "        env.render()\n",
    "        action = env.action_space.sample()\n",
    "        obs, reward, terminated, truncated, info = env.step(action)\n",
    "        if done:\n",
    "            print(\"Episode finished after {} timesteps\".format(t+1))\n",
    "            break\n",
    "    env.close()\n",
    "    clear_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NrdYut6HGS44"
   },
   "source": [
    "## Ejemplo 1: The Cliff.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P6bNIHCQGS44"
   },
   "source": [
    "![](https://github.com/GIDISIA/RLDiplodatos/blob/master/images/cliffwalking.png?raw=1)\n",
    "\n",
    "donde S= starting point, G= goal\n",
    "\n",
    "(imagen de Sutton y Barto, 2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VYvKmWcsGS44"
   },
   "source": [
    "Descripción del entorno:\n",
    "\n",
    "Acciones:\n",
    "\n",
    "* $\\uparrow$ - Arriba\n",
    "* $\\downarrow$ - Abajo\n",
    "* $\\rightarrow$ - Derecha\n",
    "* $\\leftarrow$ - Izquierda\n",
    "\n",
    "Función de recompensa:\n",
    "\n",
    "* $-1$ en todos los demás estados\n",
    "* $-100$ en el acantilado\n",
    "\n",
    "Nota: caer en el acantilado devuelve al agente al estado inicial en un mismo episodio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XZ2D3hokGS44"
   },
   "source": [
    "Vemos los bloques básicos de nuestro agente"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yKgvYRiOGS44"
   },
   "source": [
    "Definimos el método de elección de acciones. En este caso el mismo utiliza la política de exploración $\\epsilon$-greedy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "T8XHFSZiGS44"
   },
   "outputs": [],
   "source": [
    "def choose_action_e_greedy(\n",
    "        state: int,\n",
    "        actions: range,\n",
    "        q: dict,\n",
    "        hyperparameters: dict,\n",
    "        random_state: np.random.RandomState,\n",
    ") -> int:\n",
    "    \"\"\"\n",
    "    Elije una acción de acuerdo al aprendizaje realizado previamente\n",
    "    usando una política de exploración épsilon-greedy\n",
    "    \"\"\"\n",
    "    # ej: para 4 acciones inicializa en [0,0,0,0]\n",
    "    q_values = [q.get((state, a), 0.0) for a in actions]\n",
    "    max_q = max(q_values)\n",
    "    # sorteamos un número: es menor a épsilon?\n",
    "    if random_state.uniform() < hyperparameters['epsilon']:\n",
    "        # sí: se selecciona una acción aleatoria\n",
    "        return random_state.choice(actions)\n",
    "\n",
    "    count = q_values.count(max_q)\n",
    "\n",
    "    # hay más de un máximo valor de estado-acción?\n",
    "    if count > 1:\n",
    "        # sí: seleccionamos uno de ellos aleatoriamente\n",
    "        best = [i for i in range(len(actions)) if q_values[i] == max_q]\n",
    "        i = random_state.choice(best)\n",
    "    else:\n",
    "        # no: seleccionamos el máximo valor de estado-acción\n",
    "        i = q_values.index(max_q)\n",
    "\n",
    "    return actions[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fH6yG3UNGS45"
   },
   "source": [
    "Definimos el esqueleto del método learn, el cuál toma una transición y cambia el dict de los valores de Q de acuerdo a algún algoritmo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# @title algoritmo SARSA\n",
    "from typing import Any, Dict, Tuple\n",
    "\n",
    "def learn_SARSA(\n",
    "    state: int,\n",
    "    action: int,\n",
    "    reward: float,\n",
    "    next_state: int,\n",
    "    next_action: int,\n",
    "    hyperparameters: Dict[str, float],\n",
    "    q: Dict[Tuple[int, int], float]\n",
    ") -> None:\n",
    "    \"\"\"\n",
    "    Realiza una actualización según el algoritmo SARSA, para una transición\n",
    "    de estado dada\n",
    "    Args:\n",
    "        state: Estado actual\n",
    "        action: Acción actual\n",
    "        reward: Recompensa observada\n",
    "        next_state: Siguiente estado\n",
    "        next_action: Siguiente acción\n",
    "        hyperparameters: Hiperparámetros del algoritmo (alpha y gamma)\n",
    "        q: Tabla Q que almacena los valores de estado-acción\n",
    "    \"\"\"\n",
    "\n",
    "    # Obtén los hiperparámetros relevantes\n",
    "    alpha = hyperparameters['alpha']\n",
    "    gamma = hyperparameters['gamma']\n",
    "\n",
    "    # Obtén los valores Q para el estado actual y siguiente estado\n",
    "    q_current = q.get((state, action), 0.0)\n",
    "    q_next = q.get((next_state, next_action), 0.0)\n",
    "\n",
    "    # Calcula el objetivo de SARSA (TD target)\n",
    "    td_target = reward + gamma * q_next\n",
    "\n",
    "    # Actualiza el valor Q para el estado-acción actual\n",
    "    q[(state, action)] = q_current + alpha * (td_target - q_current)\n",
    "\n",
    "# Define las variables con valores apropiados antes de llamar a la función learn_SARSA\n",
    "state = 1  # Define el estado actual\n",
    "action = 2  # Define la acción actual\n",
    "reward = -1.0  # Define la recompensa observada\n",
    "next_state = 3  # Define el siguiente estado\n",
    "next_action = 4  # Define la siguiente acción\n",
    "hyperparameters = {\n",
    "    'alpha': 0.5,  # Define el valor de alpha\n",
    "    'gamma': 0.9,  # Define el valor de gamma\n",
    "}\n",
    "q = {}  # Define tu tabla Q (por ejemplo, un diccionario)\n",
    "\n",
    "# Llama a la función para realizar la actualización SARSA\n",
    "learn_SARSA(state, action, reward, next_state, next_action, hyperparameters, q)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "ilHnd2pOyO0X"
   },
   "outputs": [],
   "source": [
    "# @title algoritmo Q-Learning\n",
    "def learn_Q_learning(\n",
    "    state: int,\n",
    "    action: int,\n",
    "    reward: float,\n",
    "    next_state: int,\n",
    "    hyperparameters: Dict[str, float],\n",
    "    q: Dict[Tuple[int, int], float]\n",
    ") -> None:\n",
    "    \"\"\"\n",
    "    Realiza una actualización según el algoritmo Q-learning, para una transición\n",
    "    de estado dada\n",
    "    Args:\n",
    "        state: Estado actual\n",
    "        action: Acción actual\n",
    "        reward: Recompensa observada\n",
    "        next_state: Siguiente estado\n",
    "        hyperparameters: Hiperparámetros del algoritmo (alpha y gamma)\n",
    "        q: Tabla Q que almacena los valores de estado-acción\n",
    "    \"\"\"\n",
    "\n",
    "    # Obtén los hiperparámetros relevantes\n",
    "    alpha = hyperparameters['alpha']\n",
    "    gamma = hyperparameters['gamma']\n",
    "\n",
    "    # Obtén el valor Q máximo para el siguiente estado\n",
    "    q_max_next = max(q.get((next_state, a), 0.0) for a in actions)\n",
    "\n",
    "    # Calcula el objetivo de Q-learning (TD target)\n",
    "    td_target = reward + gamma * q_max_next\n",
    "\n",
    "    # Obtén el valor Q actual para el estado-acción actual\n",
    "    q_current = q.get((state, action), 0.0)\n",
    "\n",
    "    # Actualiza el valor Q para el estado-acción actual\n",
    "    q[(state, action)] = q_current + alpha * (td_target - q_current)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9xrOq-6jGS45"
   },
   "source": [
    "Finalmente, definimos el método principal de iteraciones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Funcion run (modificada con respecto a la original)\n",
    "import gymnasium as gym\n",
    "\n",
    "from typing import Callable, Dict, Tuple\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "def run(\n",
    "    learning_function: Callable,\n",
    "    hyperparameters: Dict[str, float],\n",
    "    episodes_to_run: int,\n",
    "    env: gym.Env,\n",
    "    actions: range,\n",
    "    q: Dict[Tuple[int, int], float],\n",
    "    random_state: np.random.RandomState,\n",
    "    use_next_action: bool = False\n",
    ") -> Tuple[float, np.ndarray, np.ndarray]:\n",
    "    timesteps_of_episode = []\n",
    "    reward_of_episode = []\n",
    "\n",
    "    for _ in range(episodes_to_run):\n",
    "        state, _ = env.reset()\n",
    "        episode_reward = 0\n",
    "        done = False\n",
    "        t = 0\n",
    "\n",
    "        action = choose_action_e_greedy(\n",
    "            state, actions, q, hyperparameters, random_state)\n",
    "\n",
    "        while not done:\n",
    "            next_state, reward, terminated, truncated, _ = env.step(action)\n",
    "\n",
    "            if use_next_action:\n",
    "                next_action = choose_action_e_greedy(\n",
    "                    next_state, actions, q, hyperparameters, random_state)\n",
    "                learning_function(\n",
    "                    state, action, reward, next_state, next_action, hyperparameters, q)\n",
    "            else:\n",
    "                learning_function(\n",
    "                    state, action, reward, next_state, hyperparameters, q)\n",
    "\n",
    "            episode_reward += reward\n",
    "            done = terminated or truncated\n",
    "\n",
    "            if not done and t < 2000:\n",
    "                state = next_state\n",
    "                action = next_action if use_next_action else choose_action_e_greedy(\n",
    "                    next_state, actions, q, hyperparameters, random_state)\n",
    "            else:\n",
    "                done = True\n",
    "                timesteps_of_episode = np.append(timesteps_of_episode, [int(t + 1)])\n",
    "                reward_of_episode = np.append(\n",
    "                    reward_of_episode, max(episode_reward, -100)\n",
    "                )\n",
    "\n",
    "            t += 1\n",
    "\n",
    "    return reward_of_episode.mean(), timesteps_of_episode, reward_of_episode\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YQqcI9RTGS46"
   },
   "source": [
    "Definidos los métodos básicos, procedemos a instanciar a nuestro agente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "JWE7qhNvGS46"
   },
   "outputs": [],
   "source": [
    "# se crea el diccionario que contendrá los valores de Q\n",
    "# para cada tupla (estado, acción)\n",
    "q = {}\n",
    "\n",
    "# definimos sus híper-parámetros básicos\n",
    "hyperparameters = {\n",
    "    \"alpha\": 0.5,\n",
    "    \"gamma\": 1,\n",
    "    \"epsilon\": 0.1,\n",
    "    \"tau\": 25,\n",
    "}\n",
    "\n",
    "\n",
    "# se puede cambiar por learn_Q_learning, una vez que se implemente\n",
    "learning_function = learn_SARSA #NO FUNCIONA CON Q-LEARNING\n",
    "episodes_to_run = 500\n",
    "\n",
    "env = gym.make(\"CliffWalking-v0\")\n",
    "actions = range(env.action_space.n)\n",
    "\n",
    "# se declara una semilla aleatoria\n",
    "random_state = np.random.RandomState(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3nBgpEltGS47"
   },
   "source": [
    "Ya instanciado, ejecutamos nuestro agente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#probar esto para SARSA\n",
    "# Hiperparámetros para SARSA\n",
    "hyperparameters_sarsa = {\n",
    "    'alpha': 0.5, \n",
    "    'gamma': 0.9,  \n",
    "    'epsilon': 0.1\n",
    "}\n",
    "# Tabla Q para SARSA\n",
    "q_sarsa = {}\n",
    "\n",
    "avg_steps_per_episode_sarsa, timesteps_ep_sarsa, reward_ep_sarsa = run(\n",
    "    learning_function=learn_SARSA,\n",
    "    hyperparameters=hyperparameters_sarsa,\n",
    "    episodes_to_run=episodes_to_run,\n",
    "    env=env,\n",
    "    actions=actions,\n",
    "    q=q_sarsa,\n",
    "    random_state=random_state,\n",
    "    use_next_action=True  # SARSA necesita el argumento next_action\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#probar esto para Q-learning\n",
    "# Hiperparámetros para Q-learning\n",
    "hyperparameters_q_learning = {\n",
    "    'alpha': 0.5,\n",
    "    'gamma': 0.9,\n",
    "    'epsilon': 0.1\n",
    "}\n",
    "# Tabla Q para Q-learning\n",
    "q_q_learning = {}\n",
    "avg_steps_per_episode_q_learning, timesteps_ep_q_learning, reward_ep_q_learning = run(\n",
    "    learning_function=learn_Q_learning,\n",
    "    hyperparameters=hyperparameters_q_learning,\n",
    "    episodes_to_run=episodes_to_run,\n",
    "    env=env,\n",
    "    actions=actions,\n",
    "    q=q_q_learning,\n",
    "    random_state=random_state,\n",
    "    use_next_action=False  # Q-learning no necesita el argumento next_action\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LgNpJUV9GS47"
   },
   "source": [
    "### Análisis de la ejecución del agente\n",
    "\n",
    "#### Análisis de convergencia\n",
    "\n",
    "A diferencia de lo que sucede en el aprendizaje supervisado, en el aprendizaje por refuerzos el rendimiento se evalúa por una función específica que es la función de recompensa. En la práctica, la función de recompensa puede ser externa (y provista por el entorno) o bien puede ser una función creada por diseño (a modo de dirigir el agente hacia lo que por diseño se considera mejor, en nuestro ejemplo podría ser con una recompensa de $+1$ cada vez que el agente llega al estado objetivo). Esto se conoce como *reward shaping*, y hay que tener mucho cuidado con los posibles efectos secundarios de su uso.\n",
    "\n",
    "Como el objetivo de RL es maximizar la recompensa obtenida, es posible utilizar la información sobre la obtención de la recompensas en cada time-step o episodio para evaluar el rendimiento parcial del agente (esto depende mucho de la particularidad de la distribución de la recompensa para el problema tratado)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yiciXUMeGS47"
   },
   "source": [
    "Para analizar la ejecución del agente, vamos a ver cómo se desempeñó el mismo en dos curvas:\n",
    "\n",
    "* Recompensa obtenida en cada episodio: nos dirá cuánta recompensa obtuvo el agente sumando cada una de recompensas individuales de cada episodio. Con esta medida podremos tener una noción de cómo se desempeñó esquivando el acantilado y llegando lo antes posible a la meta.\n",
    "\n",
    "* Pasos transcurridos en cada episodio: indicará cuántos pasos le ha llevado al agente la ejecución del episodio.\n",
    "\n",
    "Se estila suavizar ambas curvas para apreciar mejor su progresión (aunque a veces suele analizarse la curva de pasos por episodio sin suavizar)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QQKMwQ2mGS47"
   },
   "source": [
    "Veamos recompensa por episodio (recordar que en este entorno cada paso otorga una recompensa de $-1$ excepto al caer al acantilado, donde la recompensa es de $-100$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "id": "SsDunXlHGS47",
    "outputId": "5f596668-2d4d-4575-a5a2-49d41ed9155d",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jjz6KRx99FBqNBg8++CDefvttzJ49Gz4+PnB2dq51fYHqn2u5XC7V2oWEhCAlJaXWcXU991qW/t1BpsNmJrK4IUOGoF+/fvjoo49QUVEBX19fDBkyBMuWLUNWVlat4y9duiT9/6GHHsKRI0fw66+/1jrO8Nf5Pffcg+zsbPz444/SPq1Wi08//RSurq5SFb+pZGZmGpVHrVbjm2++Qc+ePeHv7w8AeOSRR5CQkIBNmzbVen5RURG0Wm2DXrOsrAwVFRVG28LCwuDm5iYNDzbUYtSstdBoNPjiiy/q9Rp2dna1aqE+/fTTWjUc9flMwsLCAMCob5BOp8OXX35Zr7I0RFhYGIqLi5GUlCRty8rKqrN816rrPf/888+1+oXcc889yMzMlIaLA9WfybXvp67PQAiBjz/+uP5vCMCXX36Jqqoq6fGSJUug1WoxYsQIANX90hQKBT755BOj1/rqq69QXFyM2NjYBr2eQUN+Nutb1rrk5+cbPVYoFIiIiIAQAlVVVbCzs8Pw4cPx22+/GTVX5eTk4Pvvv8eAAQOgUqkAVH82e/fuxf79+43KuWrVqpu+X0v/7iDTYc0MWcWMGTMwevRorFixAlOmTMHnn3+OAQMGoFu3bnjqqacQGhqKnJwcJCQk4MKFC9I8HzNmzMDq1asxevRoPPnkk+jTpw8KCgqwbt06LF26FD169MDTTz+NZcuWYdKkSTh48CDatm2L1atXY/fu3fjoo49MMoS3pg4dOmDy5Mk4cOAA/Pz88PXXXyMnJwfLly83er/r1q3DyJEjMWnSJPTp0weXL1/G0aNHsXr1apw/f75BQ5hPnz6NYcOG4ZFHHkFERATs7e3x66+/IicnR+o4279/f3h6emLixIl44YUXIJPJ8O2339a7mWzkyJH49ttv4e7ujoiICCQkJGDLli3w8vIyOq4+n0mXLl1w++23Y/bs2SgoKECrVq3www8/NDjE1ceYMWMwc+ZMPPDAA3jhhRdQVlaGJUuWoEOHDkadeK/3nufPn48nnngC/fv3x9GjR7Fq1Sqj2g6gumPtZ599hgkTJuDgwYMICAjAt99+W2viuU6dOiEsLAyvvPIKLl68CJVKhTVr1jS4j49Go5E+71OnTuGLL77AgAEDcN999wGo7uA6e/ZszJs3D3fffTfuu+8+6bjbbrtN6id1K+r7s1nfstZl+PDh8Pf3xx133AE/Pz+cPHkSn332GWJjY6Wf13//+9+Ij4/HgAED8Oyzz8Le3h7Lli1DZWUlFi1aJJ3r1Vdfxbfffou7774bL774ojQ021DrciOW/t1BJmTp4VPUchiGah44cKDWPp1OJ8LCwkRYWJjQarVCCCHOnj0rJkyYIPz9/YWDg4No3bq1GDlypFi9erXRc/Pz88Vzzz0nWrduLRQKhQgKChITJ04UeXl50jE5OTniiSeeEN7e3kKhUIhu3boZDd8V4uow6GuHNxuGC//88883fT8hISEiNjZWbNq0SXTv3l0olUrRqVOnWs8VQoiSkhIxe/ZsER4eLhQKhfD29hb9+/cX//nPf6ShrNcrkxBCABBz584VQgiRl5cn4uLiRKdOnYSLi4twd3cXkZGRRkOFhRBi9+7d4vbbbxdOTk4iMDBQvPrqq9Jw5prDh+tSWFgoXUNXV1cRExMjkpOTaw1nFqJ+n8nZs2dFdHS0UCqVws/PT7z22msiPj6+zqHZdQ3TNVzruq5LXFyc0bbNmzeLrl27CoVCITp27Ci+++67eg/Nnj59uggICBBOTk7ijjvuEAkJCbWG8AohRFpamrjvvvuEs7Oz8Pb2Fi+++KLYuHFjrfdz4sQJER0dLVxdXYW3t7d46qmnxJEjR2oNKa+L4Xtux44d4umnnxaenp7C1dVVjBs3TuTn59c6/rPPPhOdOnUSDg4Ows/PT0ydOlUUFhYaHXO963sj9fnZbEhZr72ey5YtE4MGDRJeXl5CqVSKsLAwMWPGDFFcXGz0vEOHDomYmBjh6uoqnJ2dxZ133in27NlTq7xJSUli8ODBwtHRUbRu3VosWLBAfPXVVzcdmi1E/X53UNMjE+IWejMSEYDqPjFdu3bF+vXrrV0UaoZWrFiBJ554AgcOHJA60TdVtlRWan7YZ4aIiIhsGsMMERER2TSGGSIiIrJp7DNDRERENo01M0RERGTTGGaIiIjIprWISfP0ej0yMzPh5uZ2y6v4EhERkWUJIVBSUoLAwMBaC4DW1CLCTGZmZq3VdomIiMg2ZGRkICgo6Lr7W0SYMUxBnZGRIa3fQURERE2bWq1GcHDwTZeSaBFhxtC0pFKpGGaIiIhszM26iLADMBEREdk0hhkiIiKyaQwzREREZNMYZoiIiMimMcwQERGRTWOYISIiIpvGMENEREQ2jWGGiIiIbBrDDBEREdk0hhkiIiKyaU0izFRWVqJnz56QyWRITEw02peUlISBAwfC0dERwcHBWLRokXUKSURERE1Skwgzr776KgIDA2ttV6vVGD58OEJCQnDw4EEsXrwYb731Fr788ksrlJKIiIiaIquHmT///BObN2/Gf/7zn1r7Vq1aBY1Gg6+//hpdunTBmDFj8MILL+CDDz6wQkmJiGyLXi+sXYQm73ROCb7YfgaH0gutXZRGE0LgcHohNh7Lhk4vcDJLjc+3ncGm49lmf+1cdYXZX+NGrLpqdk5ODp566imsXbsWzs7OtfYnJCRg0KBBUCgU0raYmBi89957KCwshKenZ53nraysRGVlpfRYrVabvvBERE1UWv5lfL7tDNYezkRkaCvc0y0AI7sHILOoAku2n4FWL/DuQ93hqrTqLcBqKqp02HA0C6v2peNgWnWIUdjJ8d7D3eBgJ8fp7BKMjQxBYkYhhADu7uoP4OYrN1uDEALHM9X4PSkT649k4WJReZ3Hjb89BD5uShxOL8TQTr4I8XLBgHBvpOZfxoakLGxJzkX31u54PbYzlPZyHLuoxrm8UozoGgCF/dV6j7zSSng4OcDeTo7UvMvYcDQLfx7LwolMNfa+Ngy+bo6WeutGrPadLITApEmTMGXKFPTt2xfnz5+vdUx2djbatWtntM3Pz0/ad70ws3DhQsybN8/kZSYiagpy1BX4+K8UbDyWjZfv6oDHbw8BAJzPu4zPtp3Br4cvQnelVmZnSh52puTh4y0pyCmpgLhSWVNUVoWvJ91mdKNq7s5eKsX3+9Kx+uAFFJdXAQDs5DLo9AIanR4v/XhEOvaTrWeMnhvi5Yyuge5IzChCl0AV+rVrhdF9g+Hu5GDR92BwJrcE645kYf2RTJzLu1znMQp7OXoGe2B/agG+3Zsmbd926lKdxx/JKMLec/nQ6gVSr5zzRSSik78burZ2x4lMNU5kqeHu5AAfNyXO5JZKz5XLgENpRVLwszSTh5lZs2bhvffeu+ExJ0+exObNm1FSUoLZs2ebugiYPXs2Xn75ZemxWq1GcHCwyV+HiKg+9HqB35MysWpvOsZGtsGoXq1rHVNRpUP8iRx0be2Odt4udZ6nuLwKS3ecxfLdqaio0gMA3vztGPRCIDGjCL8lZkohZlAHH/Ru44GPtqQAALKvNAPcFeGH3WfysOtMHl7+KREv39UBwa2c4WDXPENNRZUOfx7Lwg/7M7AvtUDa3trDCY/1C8YjfYPh5arEgvUnsGLPebRyUaDgsqbWedLyy5CWXwYAuFhUjs0ncvC//elY8UQ/BLeqbllIzbuMzcezERXmhe5BHnWWJ7u4Aionezgr7HHuUinWHr6Ik9klGNk9AGdzS7H+aBbcnRzwyZhe0nkN0vPL8HtSJn4/konk7BJpu9JejmGdfXFv90Dc2ckXF4vKce7SZfQP84KL0h5bTuTg33+cQHArZ1yu1OJQepH0XHu5DHeEeyMqzAv//fscUq4EFLkMMLRSJmeXGL1ecXkVisurYCeXoX+YF+7pFoDhEX7wclXW/4MxMZkQwqSNqpcuXUJ+fv4NjwkNDcUjjzyC33//3ajaTqfTwc7ODuPGjcPKlSsxYcIEqNVqrF27Vjpm27ZtGDp0KAoKCq5bM3MttVoNd3d3FBcXQ6VS3dL7IiK6FQfTCjB//UkcySgCUF0T8O3kfugf5g0A0Or0+PngBXzyVwqyiivg6eyAtXF3IMTraqApqajCit3n8X+7UqUahT4hnghwd8T6pCyj17uzow9eGNYevdpU/340NEP8lngRD/YOQucAFXamXMKTKw6gSnf11//DfYLwzKBQnMopwbId56CuqMIjfYNxW9tW6NeulTkvUaOUabRwVtT+u/x0Tgm+35eOXw5dgLpCC6D6Bj20ky/GRYZgUAcf2MmNm41y1BXwcVWipFKLlJwS9Az2QNLF4upmnCOZ8FM5wk4GJJzLR466uiuDt6sCTw8KxebjOfjnmiarB3oFAQAKL2uwPikTvx6+iEPpRbCXy9DGyxnnLtVdowIAns4O+Hxcb4R6u2J9UiZ+T8qSvocAwMFOhkHtfXBvj0BER/g1qMlQCIGzl0pxPFONwR184OFc3ZUju7gCK/acRyd/N0RH+KGySodD6UXYdDwb6vIqRHf2Q49gD/xy+ALCvF1xV4QfPF0UN3m1xqnv/dvkYaa+0tPTjfqyZGZmIiYmBqtXr0ZkZCSCgoKwZMkSvP7668jJyYGDQ3VV3muvvYZffvkFycnJ9X4thhkiMqWMgjJ8/FcKXJX2eGZwKNYfycKgDj7o6O9mdMy7G5Pxx5Ww4aKwQwd/NxxOL4KnswN+ixuA0zkleHdjslRdb/hrONzXFWum9ofSXo7v9qbh821nUFhWHWI6+LliRkwnRHf2hU4vMOW7Q9hyMgdDO/nihWHt0TPYo17vYd2RTLz4w2HUvAPIZMD17ggTo0Lg5uiAfan5mBHTyeIBRwghhYXebTyxLTkXy/ekYveZfDzcJwgLH+wGnV7U6gsDVNfCPNI3GI/cFoQAdyeTlCe7uAJPrjiAE1lX72NyGdDWy0Vq9nn89jbIUVdi+6lco+BoYCeXIbJdK2QVVyCjoAyDO/jg7q7++CYhDUcvFkMuAwSufiZyGdA/zBv39ghATBd/KYQ0Z00+zFzr/PnzaNeuHQ4fPoyePXsCAIqLi9GxY0cMHz4cM2fOxLFjx/Dkk0/iww8/xNNPP13vczPMEJEpVFTpsGzHOXyx/QwqtdXNPIYA0MpFgfXPD4Cboz2+2H4WX+1KhUarh0wGPNo3GC8P7wCVowMeWZaApAvFcFbYoUyjA1D9V/hzQ9tjeIQfRi9NQLa6Aj2DPXCppFLq0Bnq7YIXo9tjZPdAoxoFIQQKLmtuqYr/YlE5zuaW4j+bTyHpQjEAwN3JAROiQrD5eA5O5ZTU+TxnhR2+ndwPfULME2gqqnQ4cL4APYM9YCeXYe3hTKzcc/665QGAHkHuSCsoQ1HZ1b4wd3X2w5h+wRjYvnYtjCmUVmoxa00Szl26jHt7BOLB3q3h46rEok2nsHTHWaNjuwSq8ECv1hjS0Rc/H8yAr5sj7usRCB83JYQQ0OqF1NRXUaXDrDVJWJuYCQC4ra0n7u0RiBFdA+DjZr2mHGtoFmEGqJ40Ly4uDgcOHIC3tzeef/55zJw5s0HnZpghosYQQiD+RA7mrz+BC4XV4aJzgAonr/xVbggm4b6uKCrTIK+0us9F/zAvvBEbgYjAq793sorLce+nu5FXWgmFvRxP3tEOU4eESR1Jj10sxuilCSivqg46/ipHTItuj4f7BMHejP1aisursOdMHga094abowP0egG9EFi1Lx07Uy5h+6lL6BbkDge5HPvPF8BNaY9pd3XA70cy4e2qwEdjetW7qeNCYRl2puTh7i7+Rs0UGQVl+G5fGn46kIHCsiq09nBCSUWV1ExUk7uTA8b0C0a4jyveWHtMCpc1+8L4qqwzsgYA1hy8gG8SziMqzBsP9m6NDn5uN39SDYaaqEAPJ7T2ME1tki2yuTBjTgwzRHSrzl0qxbzfT2DH6eoRIAHujng9tjNiuwVgZ0oelPZyBHo44d7Pdkm1AqHeLnjtns4Y1tm3zuG8Z3JLsel4Nkb1al3njWrbqVx8vCUFd3f1x6T+beHoYGfeN1kPWp0e9nZylGt0mLh8P/bX6EwLAP3atkL3IHf8eSwbo/sGIaOgHGcvleKdB7ohIlAFvV5g++lcrNiTht1n8qDTC4T7uuK7yZE4maXGt3vTsO1UrtSkUrPJq00rZ0yICsHovsHYfioXGq0eI7sHwklRfV0OphVgzaGLuKuzX519Ych2MczUwDBD1LxU6fRmH31TrtHhk60p+L+d51ClE3Cwk+GpgaGIuzMcLnXUQOw7l48P4k8jpos/Hr89pFkPeS6t1OLpb/7BiSw1HuodhJ/+yUBJHbUnQHUT2tQhYfj5nwvSSJma7OUyaGtM7jewvTfG3x6CTv4qrNqXhsjQVhjSwRdyBpQWiWGmBoYZouahTKPFOxtO4of9GXh5eAc8OyTcLK+z+0weZv9yFOkF1UNxB3fwwdx7IxDq42qW17NFhluHTCbD4fRCPPf9YQS3ckIHPzf8sD8DQzv5Iqu4HEeu9MUBAFelPcZGtsHYfm0gl8nw2H/34mJROVSO9hjdNxjjItvwGpMRhpkaGGaImjbD8OEwH1fY28nwyV8p2HM2H+891A3hvtV9DY5dLMYL/zssjRSRy4DVU/ujs79Kam5orKIyDf79x0msPngBQHWT0rz7uuCuCL8mOftrU6XXC8jlMqgrqjDl24PIKCzD45EheCyyDVSOVyeZyy+txOH0ItwR7m2yz5CaF4aZGhhmiCwrs6gcb/9xEh7ODlhwf9cbNhGUVFRh1i9H8UdSFrq1doedXIbEK/NpdG2twi9T78A3Cefx3sZkVOkE/FRKhPu6YveZ6vms5DLgu39Fon+YN4QQOJReiLZeLg0a3SOEwPqkLMz7/TjySjWQyYAJt4fglZiOcHO0zgyvRMQwY4Rhhshy9pzJQ9z3h6R5URY+2A2P9WtT57Ens9R4dtUhaer0urRp5Sw19wyP8MOih7tDJpMh5sO/pVlt/VWOWDq+D+b/fhyH0osQ3MoJf7ww0KgW4HouFpXjzbXHsDU5F0D1HC/vPdTNbMOOiaj+GGZqYJghMg/DTKJtvVxQpRNYsuMsPtuagmsXax5/ewjGRrZB54CrP38//5MhDakNdHfEQ32C8MX2s+gb4okPHu2Jf84X4MUfEgFUrzHz5sgIPB7ZRmruSckpwY8HMvDdvjRpav+a7u8ZiI8e7Xnd5iGdXuC7vWlYtDEZlzU6ONjJEHdnOKYOCYPSnk0eRE0Bw0wNDDNEtQkhGtUPpEyjxcw1R/H7kUwM6uCDXHWFtH7LQ72DMP/+LnhkWQKOZ16dITXc1xVvjozAn0ez8MOBDADAkI4++PCRnvB0UaCkogquSnvIZDIIIbDwz2ScyFTjjZGd0cm/7p/dxIwiPLRkD3R6gahQLzx+ewhe+OEwdHqBRQ93R36pBgfTCvDisA748Z902MvlePS2YLz+61FpjZrebTzw3kPd0b6Bc4EQkXkxzNTAMENkbOWe8/hwy2nMiOmIuyL84OvmiIoqHS5XauvV1yQt/zKe+fag0eJzAODjpsTceyMQ2y0AMpkMRy8U4+Gle6QJzWqSyYCXozsg7s7wRg+73X0mD7klFbivR2vYyWX49K8UvB9/+qbPc1HYYdaIThgXGcKhv0RNEMNMDQwz1FQkXSjCkQvFGNevDTafyIGHswNuD/Uy62sKIbDm0EVUanV4qHcQ5v1+HP/bn2F0zLNDwvDjgQxodHrMiOmIviGtjGatrWlbci5e/OEw1BVaeLsqERXmhd+PZKJ7kDu+HN8X/u7Gs65WVOlwMkuNV1cnSfOMtHJR4JMxvTCgvbdZ3rNOL/DYl3urZ6p1rJ4TpqRCi9YeTtLyAMM6+WLBqK4IbMGzqxI1dQwzNTDMkLUUlWnwTUIaRnYPgLpCi8e+3IvyKh0GdfDB31dmlH1qYDuM7B6IHvVcILAhhBCYv/4Elu8+D6B6ZtpzN+hsW1Ns9wBM6t8WGq0e65My8eyQcPx35zl8k5AGAOjVxgNLxvWBn0qJ45lqdPBzu+lEceculeK3xEyM6RdssgX/rqeoTIPfj2QiOsIP6nItki4U4b6egdiWfAlKBzmGdPDhcGuiJo5hpgaGGbKGy5VajPlyL45eLEZHPzdcKq1EwWXNdY9/rF8bTB7QVppX5Vb9fiQTizYlY9bdnbHrzKVatTBujvb4YlxvhLRywf/tuhpO+oR4IjGjCLoavXddlfYor9JBpxdwcrCT1gua1L8tZt/TiR1licisGGZqYJghc8otqcDWk7l4sHcQ1idlIkddiUdvC8bY/+6t1aekS6AKqXmXUabR4ba2njhwvtBof98QT6ye2h9anR5v/nYc6vIqxN0Zjuk/H8GdHX3w6t2dbliWP49m4bn/VXd+NaxtI5cBr8dG4NuE8wCALyf0NVr0LrOoHPtTC3BPtwCczFLj6MViLFh/os5+Lkp7OT59rBeGd/G/xatFRFR/DDM1MMyQuZRptLjvs904k1uKAeHe2HUmDwAQ4uWMtPwyeLsqoXK0x7m8ywjxcsbqKf1xOqcEZy+VYlxkCP63Px2bT+RITU4A8NMzUVhz8AJ+/Ke6RkVhL4dGq4dMBsS/NBjhvnVP9741OQfPfHsQVToBO7kMOr2Awk6Oj8f0xIhuAdBo9XCwk9WraaW4vApncksxa00S7u0RCH93R/x0IAOzRnRC37acf4WILINhpgaGGTIHIQRe/ukIfj18sc79Tg52WBt3B7xdFfhubzpG9w2qs7OpVqfHr4cvYuOxbPyVnAtPZwdpwrlr3d8zEB+P6VVr+66UPDy58gA0Wj3u7RGIx24LxpIdZzF1cBj6h5unky0RkbkxzNTAMEPmsHLPecxdd9xoWyd/N5zKKYEQwKeP9cK9PQLrfb60/Mu48z/bpQnn3hwZge/2pkEG4PXYzpi88h8AgMrRHl9Nug23Xakh+ed8AcZ/tR/lVTrEdPHDZ2N7m31FaSIiS6jv/bv2OvZELYAQAuoKLdydbm3dnYNpBViw/gQA4I3YzgjydMLvSVmYMzICiRlF0OoEYrsHNOicIV4ueKh3EH4+eAHTottj8oB2eKJ/W+iFgL2dHNGd/bDlZA7UFVq89stRbJw2CCez1Hhi+QGUV+kwuIMPPnmsF4MMEbU4rJmhFum9jclYsv0svhzfp8GdWfNKKxH7yU7kqCsR2z0Anz3Wy2RDfDVaPTIKyxDmU7tfTEZBGV779Sh2plT3y5kW3R7fJqQh/7IGt7X1xDdPRnLlYSJqVup7/+afcNTiHE4vxNIdZwFAmn+lpv2pBcgrrazzuXq9wEs/JiJHXYlwX1cseqi7SecqUdjL6wwyABDcyhnfTo7EK8M7AAA+2pKC/MsadAlU4atJtzHIEFGLxTBDLUqVTo/ZvxyFoT5yb2o+UvMuY/TSPRj1+W58k3AejyxLwDPfHqzz+Uv/PoudKXlwdJBjybjecFFavqV20h3t4Olc3TwW5uOCb57sV6/VoYmImiv2mSGTU1dU4Y1fjyE6wg/3NaADrLnkl1Zi7H/3IdzXFV1aq5CcXQJPZwcEeTrj6MVijP9qHy4UVk9xf+RCEQDgYFohTmSqjab0P5hWgPc3V6/3M+++LlZblNBVaY//jO6B349kYuaITvVaS4mIqDljzQzdkvT8MlTpak+qBgDfJqRh3ZFMvLvhJJpCl6y3/ziJUzkl+ONoFj68svjgmyMjMP72EACQggwA1CzuT/9cnTm3qEyDF/6XCJ1e4L4egXikb7BlCn8dwzr74aMxvcy+JAARkS1gmKEG+/NoFgYt3ibVUtSk1wv8b386ACCzuALtZm/A6oMXLF1Eyc6US/ilxjwwVTqBAeHeeKBXa4zo5g/llbWEerfxgEwGKOzkmDMyAgDwy6ELqKjSQQiBV1cn4WJROdp6OePtB7pyTR8ioiaEzUzUYFNXHQIALN1xFsM6+6JPG0/EfX8IJ7PUmD68o1FNBwC88vMRPNwnyGLlW3ckE//9+xzee6g7Xv/1GACgg58rTueUwtFBLoURN0cHTB/eAduSL+HjMT2RklsKF6U9urd2x1e7UnGxqBybjmejpEKLzSdyoLCT47OxveHG/ilERE0Kh2ZTg+SWVKDf238ZbfvXgHb4v12pAHDd2WuTF9wNRwfTjLaZ+9sxbDyejTVT+yPI09loX8FlDXoviDfa5q9yxKaXBmH57lT0CPbAnR19b/oaH29JwYdbTsPbVYGSCi0qtXq8OTICkwe0M8l7ICKim+PQbDKLtXVM3W8IMgCkIPPeQ90gr9ESk5hRZJLXP5JRhJUJachRV2LNwdpl+feViexqeuu+LnB3csC06A71CjIAMLpvdU1SXqkGlVo9BnfwwRP92zaq7EREZB4MM1Rver3Aqn3pNz2ub4gnHr2tDY7Pu1uaBXffuYIGv97awxexM+XqAoxCCLy3MVl6vOl4NoQQ+OXQBRxMK8AfSVlG/WMAYFgnX8R08Wvwawd6OGFIRx8AgJtj9eghuZz9ZIiImiL2maF625uaj7T8Mrgp7fF6bGfM+uWotE9pL0eltnp009jINgAAJ4Udbg/1wh9JWdiZcgkP9GqNNl7OdZ77WjtTLmHaj4kAgBPzY+CssMeuM3nYczYfCjs5dELgRJYaS3acxaKNp+Dh7AAXRfW38/NDw9GmlTPWHcnEvxvRWffNkRFo65WGyQPawceNw5+JiJoq9pmhepu1Jgk/HMjAY/2C8c4D3VBSqcUjSxOQnF2C8beH4FxeKYrKqrBman+pf8zpnBIM//Bv6Rxr4+5Az2CPG76OEAKjlybgn7RCadvr93TGH0ezkJhRhCfuaIvkrBIknMuv9Vx/lSO2vTKEs+ESETUDXGiSTEKj1UOj00NhJ8eGo1kAgHt7BEImk0Hl6IB/DQzFyj3n8dTA0DprXdr7Gk/Nv/5I5k3DzP7UAqMgAwBvbzgJoLoG6Nkh4fgjKbPOMDMjpiODDBFRC8M+M3RDcd8fQuTbW/DjgXSoK7TwdVMisp2XtP/hPkH4/fkB120+kslkGNXz6izAB64JKXVZ9vc5AEBIHeccG9kGPm5Ko8UhJ0aFQCYDegR74IFerev93oiIqHlgzQxdV25JBeJP5AAA3vztOABgZPdA2DWwI+zce7vgtnat8Pqvx3D0QhHUFVXXXUvoVHYJtibnQi4DVj7RDztTLmHpjnO4WFQOhb0cUwaHAajuoPtGbGcUXNbgleEdMT4qBH4qR3bSJSJqgVgzQ9e1LTm31raRPQIafB5PFwXGRYagnbcL9AL4cX8G0vIv13nssiurWY/oGoC23i4YH9UWHzzSA3IZ8MygUPipHKVj/zUwFK/e3QlyuQzhvm6czI6IqIVimKHrij9hHGZaezih1036u9xIVFh189TbG05i1Oe7kauuMFrf6WJROdYdyQQAPDM4VNoeGeqFkwvuxst3dbjl1yYiouaLYaYFqtLpkVlUfsNjyjU67DpzyWiboePvreofdrWvTWFZFfq98xde+N9hadtXO1Oh1Qv0D/NC9yAPo+cq7e24HhIREdWJYaYFevfPZPR/dysSztYeDWTwd8olVFTp0drDCZ7O1c039/UIvO7x9XF7qFetbX8ey4a6ogolFVXSKtXPXOkXQ0REVB/sANzCCCGwPqm6KWdnyiWp6edaho6/w7v44f6erVF4WYOIwMbN0ePtqsS06PZYuuMsKqquNi/tOZOHi0UVKK3UItzXFYPaezfqdYiIqGVhmGlh0gvKkKOuBACk5JbWeYxWp8dfJ6+EmQj/m84L0xDTojvgif7tEPPR38hWVwAAtiVfkuaMeeKOtmxOIiKiBmEzUwtTc42klJySOo85mFaIwrIqeDg74La2niYvg7uzAxJmD8XXk/oCANYcuoD0gjK4OzngwV5BJn89IiJq3hhmWph9qVfDTHpBGSqqdLWO2XyliWloJ1/Y25nnW0QmkyEq1BsKezm0+uoVNR7r14az9xIRUYMxzLQw+1KvdvrVC+DsJeOmJiHE1f4yEQ1fbbohnBR2iGzXCgBgJ5dhQlSIWV+PiIiaJ4aZFuRiUTkuFJbDTi5D54Dqzrxnruk3cyqnBOkFZVDayzGog4/Zy2RYlmBk9wAEejiZ/fWIiKj5YQfgFmT/lVqZroEqRAS642SWGi/+kIg1hy7iq4l94WAnx18nqyfKGxDuDWeF+b89xvVrgyAPpzqHbRMREdUHa2ZaEMO8MpGhXujgd3U1679PX8J/d57D6ZwS7D6TBwAY3NH8tTIAIJfLcGcnX/aVISKiW8aamRZCCIHdZ6rDzB3h3rC/ZkHGRRtPYdHGU1DYV+fb/mGc64WIiGwDw0wLkZZfhotF5XCwk+G2tp4ordDWeZxGq4efSokwHxcLl5CIiOjWWLWZqW3b6gnSan69++67RsckJSVh4MCBcHR0RHBwMBYtWmSl0tq23Werm496t/GEs8IePm7K6x57R5g3J64jIiKbYfWamfnz5+Opp56SHru5uUn/V6vVGD58OKKjo7F06VIcPXoUTz75JDw8PPD0009bo7g2y9AX5o7w6uYjmUyGz8b2wqq96dLsuwb9w9nEREREtsPqYcbNzQ3+/v517lu1ahU0Gg2+/vprKBQKdOnSBYmJifjggw8YZhpArxfYc/ZqfxmDkd0DMayTH+78z3ZpaYHqYziyiIiIbIfVRzO9++678PLyQq9evbB48WJotVf7ciQkJGDQoEFQKBTStpiYGJw6dQqFhYXXPWdlZSXUarXRV0t2IkuNorIquCrt0SPI3Wifk8IOm6YNwtLH+wAA2vu6IsCd870QEZHtsGrNzAsvvIDevXujVatW2LNnD2bPno2srCx88MEHAIDs7Gy0a9fO6Dl+fn7SPk/PutcNWrhwIebNm2fewtuQf85XL2FwW1vPOpcncHd2QEwXP7w/uge6tnavtZ+IiKgpM3nNzKxZs2p16r32Kzk5GQDw8ssvY8iQIejevTumTJmC999/H59++ikqKysbVYbZs2ejuLhY+srIyDDFW7NZhzOKAFR3/r0emUyGh/oEoaO/23WPISIiaopMXjMzffp0TJo06YbHhIaG1rk9MjISWq0W58+fR8eOHeHv74+cnByjYwyPr9fPBgCUSiWUyuuP1mlpDqcXAQB6tvGwajmIiIjMweRhxsfHBz4+tzZ7bGJiIuRyOXx9fQEAUVFReP3111FVVQUHBwcAQHx8PDp27HjdJiYylldaifSCMshkQI9gD2sXh4iIyOSs1gE4ISEBH330EY4cOYJz585h1apVeOmll/D4449LQWXs2LFQKBSYPHkyjh8/jh9//BEff/wxXn75ZWsV2+YkXqmVCfdxhcrRwbqFISIiMgOrdQBWKpX44Ycf8NZbb6GyshLt2rXDSy+9ZBRU3N3dsXnzZsTFxaFPnz7w9vbGnDlzOCy7ARKv9JfpxSYmIiJqpqwWZnr37o29e/fe9Lju3btj586dFihR83Q4o3oIe68bdP4lIiKyZVafZ4bMR6cXOJJRDADoyf4yRETUTDHMNGNncktRWqmFs8IOHfw45JqIiJonqy9nQKZ3IlONIxeKpMc9gjxgJ+fCkURE1DwxzDRD93xS3cfI27V6GQh2/iUiouaMzUw2TKcXWL47FQfTCqRtFVU66f95pRoA7PxLRETNG2tmbNiXf5/DexuT4eRgh5ML7gYAnMouqXUcO/8SEVFzxpoZG6XXC3z812kAQHmVDjq9AACczDJeITzI0wk+blzagYiImi+GGRt1KqcEFVV66XFWcTmA2mGGTUxERNTcMczYqLOXSo0eZxSUQwiBXWfyjLb3YhMTERE1c+wzY6POXbps9DijoAwCAmcvXYazwg4VVTroBdA7hDUzRETUvDHM2Khz19TMpBeUYW9qPgDggV6tMbC9D9LyL6NHkLs1ikdERGQxDDM26lxedc1M7zYeOJRehIzCMhxMq16H6e6u/hjY3seaxSMiIrIY9pmxQUIIqZlpcAdfAMD+1AJcKCyHXMZOv0RE1LIwzNigSyWVKK3UQi4DBnbwBgBkFVcAALoEusNVyQo3IiJqORhmbNDZK7UyQZ7OCPd1NdrXty1rZYiIqGVhmLFBqVf6y4T6uEDl6AAPZwdpX7fW7PBLREQtC8OMDTKMZAr1rq6VCfJ0kvZ1CWSYISKiloVhxgadq1EzAwBCXN0XdmUbERFRS8EwY4OkmpkrwaVcc3WlbHs7fqRERNSy8M5nY05ll+B8fhnkMqCDnxsA4I2RnQEALwwNt2bRiIiIrIJjeG3Mf3eeA1A9MZ63a/Vq2EM7+eHA69HwdlVYs2hERERWwTBjY7afugQAGH97W6PtPm5KK5SGiIjI+tjMZENKKqqQV1oJAOjSWmXl0hARETUNDDM25HxeGQDAy0UBlaPDTY4mIiJqGRhmbEhqfvWQ7LbeHH5NRERkwDBjQ85fmV+mrRfDDBERkQHDjA05f6Vmpp23s5VLQkRE1HQwzNgQqWaGzUxEREQShhkbcj6/ugMwm5mIiIiuYpixEcXlVSi4rAHAmhkiIqKaGGZshKGJycdNCVcl5zokIiIyYJixEVLnXzYxERERGWGYsRGpeYaRTAwzRERENTHM2Ii0K51/Qzgsm4iIyAjDjI24WFgOAAj2ZJghIiKqiWHGRmQWV4eZAHdHK5eEiIioaWGYsQF6vUCOugIAEODhZOXSEBERNS0MMzYg73IlqnQCMhng66a0dnGIiIiaFIYZG5BVVF0r4+umhIMdPzIiIqKaeGe0AVnFV5qY3NnEREREdC2GGRuQxc6/RERE18UwYwMMNTP+DDNERES1MMzYgAuF1RPmBXGOGSIioloYZmyANPtvK4YZIiKiazHMNHFCCKRfCTNtvBhmiIiIrmX1MPPHH38gMjISTk5O8PT0xKhRo4z2p6enIzY2Fs7OzvD19cWMGTOg1WqtU1grKC6vQkll9fvlUgZERES12VvzxdesWYOnnnoK77zzDoYOHQqtVotjx45J+3U6HWJjY+Hv7489e/YgKysLEyZMgIODA9555x0rltxy0guqa2V83JRwUthZuTRERERNj9XCjFarxYsvvojFixdj8uTJ0vaIiAjp/5s3b8aJEyewZcsW+Pn5oWfPnliwYAFmzpyJt956CwqFwhpFtyhDmGF/GSIiorpZrZnp0KFDuHjxIuRyOXr16oWAgACMGDHCqGYmISEB3bp1g5+fn7QtJiYGarUax48fv+65KysroVarjb5sTVGZBpVandT5tw3DDBERUZ2sFmbOnTsHAHjrrbfwxhtvYP369fD09MSQIUNQUFAAAMjOzjYKMgCkx9nZ2dc998KFC+Hu7i59BQcHm+ldmMelkkr0f3crxn+1HxlXamaCGWaIiIjqZPIwM2vWLMhksht+JScnQ6/XAwBef/11PPTQQ+jTpw+WL18OmUyGn3/+uVFlmD17NoqLi6WvjIwMU7w1izmYVogyjQ6J6UU4n38ZAGtmiIiIrsfkfWamT5+OSZMm3fCY0NBQZGVlATDuI6NUKhEaGor09HQAgL+/P/bv32/03JycHGnf9SiVSiiVtru6dHJ2dbOYRqfHkYxiAByWTUREdD0mDzM+Pj7w8fG56XF9+vSBUqnEqVOnMGDAAABAVVUVzp8/j5CQEABAVFQU3n77beTm5sLX1xcAEB8fD5VKZRSCmpvkrBLp/+VVOgDsAExERHQ9VhvNpFKpMGXKFMydOxfBwcEICQnB4sWLAQCjR48GAAwfPhwREREYP348Fi1ahOzsbLzxxhuIi4uz6ZqXmzmVU2L0WGkvh49b832/REREjWHVeWYWL14Me3t7jB8/HuXl5YiMjMTWrVvh6ekJALCzs8P69esxdepUREVFwcXFBRMnTsT8+fOtWWyzKtNopX4yBm1aOUMmk1mpRERERE2bTAghrF0Ic1Or1XB3d0dxcTFUKpW1i3NDRzKKcP/nu422DY/ww5cT+lqpRERERNZR3/u31ZczIGOGzr81Devsa4WSEBER2QaGmSYmObu6v4yd/Gqz0vCI64/cIiIiaukYZpoYw0im54eGw9FBjod6B8HTpfkv20BERHSrrNoBmGozjGSK7uyHZ4eEQ85+v0RERDfEMNOElGt0KLisAVA9SZ7CnhVnREREN8O7ZRNSWFYdZBzsZHBTMmcSERHVB8NME2KolfFwVnBeGSIionpimGlCDDUzrZzZ4ZeIiKi+GGaakMKyKgCAp4uDlUtCRERkOxhmmpDCK81MrTgUm4iIqN4YZpoQQ58ZTzYzERER1RvDTBMi9ZlhzQwREVG9Mcw0IYY+Mx6smSEiIqo3hpkm5GqfGXYAJiIiqi+GmSaEfWaIiIgajmGmCTH0mWGYISIiqj+GmSZCCIG80koAgI+b0sqlISIish0MM01EUVkVqnQCAODtyjBDRERUXwwzTURuSXWtjKezA1fLJiIiagDeNZuISyVsYiIiIroVDDNNRG5JBQDA183RyiUhIiKyLQwzTUQua2aIiIhuCcNME2FoZvJlmCEiImoQhpkmgjUzREREt4Zhpom4dKXPDMMMERFRwzDMNBFZxdVhxl/FDsBEREQNwTDTBOj0AhcLywEAwa2crVwaIiIi28Iw0wRkFZdDqxdQ2Mnhx5oZIiKiBmGYaQLSC8oAAK09nWAnl1m5NERERLaFYaYJuFDAJiYiIqJbxTDTBBhqZtq0crJySYiIiGwPw0wTkFFYHWaCPVkzQ0RE1FD21i5AS5acrcbbf5zEvtQCAECAB2tmiIiIGophxoqe+fYg0vLLpMfergorloaIiMg2sZnJimoGGQDwduXsv0RERA3FMNOEMMwQERE1HMNMEyGXAR5ODtYuBhERkc1hmGkiPJwVkHPCPCIiogZjmLGSMo3W2kUgIiJqFhhmrCRXXWn0WKcXVioJERGRbWOYsZIcdYXRY71gmCEiIroVDDNWkltiXDOjZ80MERHRLWGYsZK8UuMwMzayjZVKQkREZNs4A7CVFJVVAQBGdg/APd0CMLSTr5VLREREZJsYZqykuLw6zAS3csY93QKsXBoiIiLbZbVmpu3bt0Mmk9X5deDAAem4pKQkDBw4EI6OjggODsaiRYusVWSTUl8JM+6cKI+IiKhRrFYz079/f2RlZRlte/PNN/HXX3+hb9++AAC1Wo3hw4cjOjoaS5cuxdGjR/Hkk0/Cw8MDTz/9tDWKbTLFDDNEREQmYbUwo1Ao4O/vLz2uqqrCb7/9hueffx4yWfVMuKtWrYJGo8HXX38NhUKBLl26IDExER988EGzCTNcwoCIiKhxmsxopnXr1iE/Px9PPPGEtC0hIQGDBg2CQqGQtsXExODUqVMoLCy87rkqKyuhVquNvpoa1swQERGZRpMJM1999RViYmIQFBQkbcvOzoafn5/RcYbH2dnZ1z3XwoUL4e7uLn0FBwebp9CNYAgzKoYZIiKiRjF5mJk1a9Z1O/YavpKTk42ec+HCBWzatAmTJ082SRlmz56N4uJi6SsjI8Mk520svV7gqW/+wb/Xn2DNDBERkYmYvM/M9OnTMWnSpBseExoaavR4+fLl8PLywn333We03d/fHzk5OUbbDI9r9re5llKphFKpbECpLSPxQhHiTxi/H3dnhhkiIqLGMHmY8fHxgY+PT72PF0Jg+fLlmDBhAhwcjG/sUVFReP3111FVVSXti4+PR8eOHeHp6WnScluCVme8ZIFcBrgqONUPERFRY1i9z8zWrVuRmpqKf/3rX7X2jR07FgqFApMnT8bx48fx448/4uOPP8bLL79shZI23uVKrdFjdycHyOUyK5WGiIioebB6tcBXX32F/v37o1OnTrX2ubu7Y/PmzYiLi0OfPn3g7e2NOXPm2OywbEM/GQP2lyEiImo8q4eZ77///ob7u3fvjp07d1qoNObFMENERGR6Vm9makmuDTNBns5WKgkREVHzwTBjQYaVsg3CfFysVBIiIqLmg2HGgq6tmQnzdbVSSYiIiJoPhhkLqhVmfBhmiIiIGothxoLUDDNEREQmxzBjQUXlGqPHTgo7K5WEiIio+bD60OyWxNDMNDEqBA/2DrrJ0URERFQfDDMWZAgz/xoYiuBWHJZNRERkCmxmspBKrQ4VVXoAgIqT5REREZkMw4yFXK7USf93VbJCjIiIyFQYZiyktKJ6kUknBzvYcXFJIiIik2GYsZDSKytmuzqyVoaIiMiUGGYs5LLmSphhExMREZFJMcxYiKGZyUXJuWWIiIhMiWHGQqRmJtbMEBERmRTDjIVcZpghIiIyC4YZCzHUzLgwzBAREZkUw4yFMMwQERGZB8OMhRiamdwYZoiIiEyKYcZCSq/MAMyaGSIiItNimLEQNjMRERGZB8OMhVwdzcR5ZoiIiEyJYcZCrs4zwxWziYiITIlhxkI4AzAREZF5MMxYCNdmIiIiMg+GGQu5zFWziYiIzIJhxkKk0UwKhhkiIiJTYpixAK1Oj4oqPQA2MxEREZkaw4wFXL4yYR7AeWaIiIhMjWHGAkqvdP5V2MuhsOclJyIiMiXeWS3AMCybTUxERESmxzBjAVeXMuAcM0RERKbGMGMBlzn7LxERkdkwzFgA12UiIiIyH4YZCyjhitlERERmwzBjAZcZZoiIiMyGYcYCDGHGjWGGiIjI5BhmLIDNTERERObDMGMBbGYiIiIyH4YZCzAsZ8DRTERERKbHqgIz0usF0gvKUFLBeWaIiIjMhWHGjOasO4bv9qZLjzkDMBERkemxmcmMagYZgGszERERmQPDjAW5ObKZiYiIyNQYZizIx01p7SIQERE1O1YNM6dPn8b9998Pb29vqFQqDBgwANu2bTM6Jj09HbGxsXB2doavry9mzJgBrVZrpRI3ji/DDBERkclZNcyMHDkSWq0WW7duxcGDB9GjRw+MHDkS2dnZAACdTofY2FhoNBrs2bMHK1euxIoVKzBnzhxrFvuWuCjsOM8MERGRGVgtzOTl5SElJQWzZs1C9+7d0b59e7z77rsoKyvDsWPHAACbN2/GiRMn8N1336Fnz54YMWIEFixYgM8//xwajcZaRa83BzuZ9H9flaMVS0JERNR8WS3MeHl5oWPHjvjmm29w+fJlaLVaLFu2DL6+vujTpw8AICEhAd26dYOfn5/0vJiYGKjVahw/fvy6566srIRarTb6soaao5e8XBRWKQMREVFzZ7V2D5lMhi1btmDUqFFwc3ODXC6Hr68vNm7cCE9PTwBAdna2UZABID02NEXVZeHChZg3b575Cl9Pzgp7FJZVAQAc7NjXmoiIyBxMfoedNWsWZDLZDb+Sk5MhhEBcXBx8fX2xc+dO7N+/H6NGjcK9996LrKysRpVh9uzZKC4ulr4yMjJM9O4aRqvXS/+3r9HkRERERKZj8pqZ6dOnY9KkSTc8JjQ0FFu3bsX69etRWFgIlUoFAPjiiy8QHx+PlStXYtasWfD398f+/fuNnpuTkwMA8Pf3v+75lUollErrjxzSaK+GGbmMYYaIiMgcTB5mfHx84OPjc9PjysrKAAByuXHlkFwuh/5KjUZUVBTefvtt5ObmwtfXFwAQHx8PlUqFiIgIE5fc9GqGmR7BHtYrCBERUTNmtY4cUVFR8PT0xMSJE3HkyBGcPn0aM2bMQGpqKmJjYwEAw4cPR0REBMaPH48jR45g06ZNeOONNxAXF9ckal5upvJKmHmwd2s8OyTMyqUhIiJqnqwWZry9vbFx40aUlpZi6NCh6Nu3L3bt2oXffvsNPXr0AADY2dlh/fr1sLOzQ1RUFB5//HFMmDAB8+fPt1ax602vF9DqBQDgjdgIODpwkUkiIiJzsOosbn379sWmTZtueExISAg2bNhgoRKZjkZ3tYlJYc+RTERERObCu6yZVFbVCDMclk1ERGQ2vMuaSaVOBwCQyYxnAiYiIiLTYpgxE8NIJoWdHDIOyyYiIjIbhhkzkcIM+8sQERGZFe+0ZmIYlq205ygmIiIic2KYMYPSSi3++/c5AICSNTNERERmxTutGSzamIxfDl8EwGYmIiIic+Od1gx2puRJ/2fNDBERkXnxTmsGbo5X5yJkzQwREZF58U5rBkZhhhPmERERmRXvtGbgpnSQ/s+aGSIiIvPindYMatbMVNVYo4mIiIhMj2HGDOxrLF9QWqmzYkmIiIiaP4YZM9BohfT/0soqK5aEiIio+WOYMQNNjaal0gqtFUtCRETU/DHMmEGVtkaYqWSYISIiMieGGTOo2em3SiducCQRERE1lv3ND6H6EELgjbXH0MnfzaiZaVTPQCuWioiIqPljmDGRvecKsGpfOgAgsl0rAMDdXfzx7we6WbNYREREzR6bmUykQnt1CHbllT4zD/ZuDVcl8yIREZE5McyYiLLGsgUFlzUAAAfO/ktERGR2vNuaSM1uvnmllQC4LhMREZEl8G5rIpoaw7HLNNVNTlyXiYiIyPx4tzWRSm3tZQscWDNDRERkdrzbmkiltvaCkg411mgiIiIi82CYMZG6woySzUxERERmx7utidRdM8PLS0REZG6825qIhmGGiIjIKni3NZG6OgBzNBMREZH58W5rIpVVrJkhIiKyBt5tTaTm4pIGnDSPiIjI/Hi3NZG6ambYzERERGR+vNuayLV9ZuQywE7OeWaIiIjMjWHGRK4dzcT+MkRERJbBO66JXDvPDMMMERGRZfCOayLXNjNd1mitVBIiIqKWhWHGRK5tZhLCSgUhIiJqYRhmTKSu5QyIiIjI/BhmTIRhhoiIyDoYZkykrrWZiIiIyPwYZkykrrWZiIiIyPwYZkyENTNERETWwTBjIoY+MwHujlYuCRERUcvCMGMihjDzzoPd0N7XFe880M3KJSIiImoZ7K1dAFt3qaQSOeoKqZmpnZcL4l8ebOVSERERtRxWrZk5dOgQ7rrrLnh4eMDLywtPP/00SktLjY5JT09HbGwsnJ2d4evrixkzZkCrbTqz69729haM/HQXSiury6R0YGUXERGRJVntzpuZmYno6GiEh4dj37592LhxI44fP45JkyZJx+h0OsTGxkKj0WDPnj1YuXIlVqxYgTlz5lir2DeltLezdhGIiIhaFKs1M61fvx4ODg74/PPPIZdXZ6qlS5eie/fuOHPmDMLDw7F582acOHECW7ZsgZ+fH3r27IkFCxZg5syZeOutt6BQKOo8d2VlJSorK6XHarXaIu8JABT2rJkhIiKyJKvdeSsrK6FQKKQgAwBOTk4AgF27dgEAEhIS0K1bN/j5+UnHxMTEQK1W4/jx49c998KFC+Hu7i59BQcHm+ld1KZkmCEiIrIoq915hw4diuzsbCxevBgajQaFhYWYNWsWACArKwsAkJ2dbRRkAEiPs7Ozr3vu2bNno7i4WPrKyMgw07swprSXw8GOYYaIiMiSTH7nnTVrFmQy2Q2/kpOT0aVLF6xcuRLvv/8+nJ2d4e/vj3bt2sHPz8+otuZWKJVKqFQqoy9LcFVycBgREZGlmfzuO336dKNOvHUJDQ0FAIwdOxZjx45FTk4OXFxcIJPJ8MEHH0j7/f39sX//fqPn5uTkSPuaGldHhhkiIiJLM/nd18fHBz4+Pg16jqHp6Ouvv4ajoyPuuusuAEBUVBTefvtt5ObmwtfXFwAQHx8PlUqFiIgI0xbcBFgzQ0REZHlWvft+9tln6N+/P1xdXREfH48ZM2bg3XffhYeHBwBg+PDhiIiIwPjx47Fo0SJkZ2fjjTfeQFxcHJRKpTWLXieGGSIiIsuz6t13//79mDt3LkpLS9GpUycsW7YM48ePl/bb2dlh/fr1mDp1KqKiouDi4oKJEydi/vz5Viz1VXq9MHrsxmYmIiIii7Pq3febb7656TEhISHYsGGDBUrTcNprwowLa2aIiIgsjuOIG0F3TZhhMxMREZHlMcw0glavN3rM0UxERESWxzDTCNfWzLixZoaIiMjiGGYa4do+M2xmIiIisjyGmUa4tmaGHYCJiIgsj2GmEa6tmeHQbCIiIstjmGkEnY41M0RERNbGMNMI145m4orZRERElse7byPohXHNjFwms1JJiIiIWi6GmUa4ts9MqI+LlUpCRETUcjHMNIK2Rp+Z+JcGwdu16S1+SURE1NwxzDSCYWh2aw8ntPdzs3JpiIiIWiaGmUYwNDPZ27GvDBERkbUwzDSCoWbGTs4wQ0REZC0MM41gGJptzzBDRERkNQwzjXC1ZoaXkYiIyFp4F24Eqc8Ma2aIiIishmGmEQzLGbDPDBERkfUwzDQCa2aIiIisj2GmEQx9ZuQMM0RERFbDMNMIHM1ERERkfQwzjcB5ZoiIiKyPYaYRdOwzQ0REZHUMM43AeWaIiIisj3fhRuBoJiIiIutjmGkEqWaGC00SERFZDcNMI7BmhoiIyPoYZhpBd2VoNkczERERWQ/DTCOwZoaIiMj6GGYa4eraTLyMRERE1sK7cCOwZoaIiMj6GGYagTMAExERWR/DTCOwZoaIiMj6GGYagaOZiIiIrI9hphG0bGYiIiKyOoaZRuBCk0RERNbHMNMIXGiSiIjI+ngXbgSpZoZrMxEREVkNw0wjsM8MERGR9THMNAL7zBAREVkfw0wjsGaGiIjI+hhmGsEwzwxrZoiIiKyHYaYRtFxokoiIyOp4F24E9pkhIiKyPoaZRmCfGSIiIuszW5h5++230b9/fzg7O8PDw6POY9LT0xEbGwtnZ2f4+vpixowZ0Gq1Rsds374dvXv3hlKpRHh4OFasWGGuIjcY55khIiKyPrOFGY1Gg9GjR2Pq1Kl17tfpdIiNjYVGo8GePXuwcuVKrFixAnPmzJGOSU1NRWxsLO68804kJiZi2rRp+Ne//oVNmzaZq9gNouVCk0RERFZnb64Tz5s3DwCuW5OyefNmnDhxAlu2bIGfnx969uyJBQsWYObMmXjrrbegUCiwdOlStGvXDu+//z4AoHPnzti1axc+/PBDxMTEXPe1KysrUVlZKT1Wq9Wme2M1SMsZyBhmiIiIrMVqfWYSEhLQrVs3+Pn5SdtiYmKgVqtx/Phx6Zjo6Gij58XExCAhIeGG5164cCHc3d2lr+DgYNO/AbDPDBERUVNgtTCTnZ1tFGQASI+zs7NveIxarUZ5efl1zz179mwUFxdLXxkZGSYufbWH+wTh2SFhCPVxMcv5iYiI6OYaFGZmzZoFmUx2w6/k5GRzlbXelEolVCqV0Zc5jIsMwat3d0K4r5tZzk9EREQ316A+M9OnT8ekSZNueExoaGi9zuXv74/9+/cbbcvJyZH2Gf41bKt5jEqlgpOTUz1LTURERM1Zg8KMj48PfHx8TPLCUVFRePvtt5GbmwtfX18AQHx8PFQqFSIiIqRjNmzYYPS8+Ph4REVFmaQMREREZPvM1mcmPT0diYmJSE9Ph06nQ2JiIhITE1FaWgoAGD58OCIiIjB+/HgcOXIEmzZtwhtvvIG4uDgolUoAwJQpU3Du3Dm8+uqrSE5OxhdffIGffvoJL730krmKTURERDZGJoQQ5jjxpEmTsHLlylrbt23bhiFDhgAA0tLSMHXqVGzfvh0uLi6YOHEi3n33XdjbX60w2r59O1566SWcOHECQUFBePPNN2/a1HUttVoNd3d3FBcXm63/DBEREZlWfe/fZgszTQnDDBERke2p7/2bazMRERGRTWOYISIiIpvGMENEREQ2jWGGiIiIbBrDDBEREdk0hhkiIiKyaQwzREREZNMYZoiIiMimNWhtJltlmBdQrVZbuSRERERUX4b79s3m920RYaakpAQAEBwcbOWSEBERUUOVlJTA3d39uvtbxHIGer0emZmZcHNzg0wmM9l51Wo1goODkZGRwWUSzIzX2jJ4nS2D19lyeK0tw1zXWQiBkpISBAYGQi6/fs+YFlEzI5fLERQUZLbzq1Qq/pBYCK+1ZfA6Wwavs+XwWluGOa7zjWpkDNgBmIiIiGwawwwRERHZNIaZRlAqlZg7dy6USqW1i9Ls8VpbBq+zZfA6Ww6vtWVY+zq3iA7ARERE1HyxZoaIiIhsGsMMERER2TSGGSIiIrJpDDNERERk0xhmiIiIyKYxzDTC559/jrZt28LR0RGRkZHYv3+/tYtkU/7++2/ce++9CAwMhEwmw9q1a432CyEwZ84cBAQEwMnJCdHR0UhJSTE6pqCgAOPGjYNKpYKHhwcmT56M0tJSC76Lpm/hwoW47bbb4ObmBl9fX4waNQqnTp0yOqaiogJxcXHw8vKCq6srHnroIeTk5Bgdk56ejtjYWDg7O8PX1xczZsyAVqu15Ftp0pYsWYLu3btLM6BGRUXhzz//lPbzGpvHu+++C5lMhmnTpknbeK1N46233oJMJjP66tSpk7S/SV1nQbfkhx9+EAqFQnz99dfi+PHj4qmnnhIeHh4iJyfH2kWzGRs2bBCvv/66+OWXXwQA8euvvxrtf/fdd4W7u7tYu3atOHLkiLjvvvtEu3btRHl5uXTM3XffLXr06CH27t0rdu7cKcLDw8Vjjz1m4XfStMXExIjly5eLY8eOicTERHHPPfeINm3aiNLSUumYKVOmiODgYPHXX3+Jf/75R9x+++2if//+0n6tViu6du0qoqOjxeHDh8WGDRuEt7e3mD17tjXeUpO0bt068ccff4jTp0+LU6dOiddee004ODiIY8eOCSF4jc1h//79om3btqJ79+7ixRdflLbzWpvG3LlzRZcuXURWVpb0denSJWl/U7rODDO3qF+/fiIuLk56rNPpRGBgoFi4cKEVS2W7rg0zer1e+Pv7i8WLF0vbioqKhFKpFP/73/+EEEKcOHFCABAHDhyQjvnzzz+FTCYTFy9etFjZbU1ubq4AIHbs2CGEqL6uDg4O4ueff5aOOXnypAAgEhIShBDVwVMul4vs7GzpmCVLlgiVSiUqKyst+wZsiKenp/i///s/XmMzKCkpEe3btxfx8fFi8ODBUpjhtTaduXPnih49etS5r6ldZzYz3QKNRoODBw8iOjpa2iaXyxEdHY2EhAQrlqz5SE1NRXZ2ttE1dnd3R2RkpHSNExIS4OHhgb59+0rHREdHQy6XY9++fRYvs60oLi4GALRq1QoAcPDgQVRVVRld606dOqFNmzZG17pbt27w8/OTjomJiYFarcbx48ctWHrboNPp8MMPP+Dy5cuIioriNTaDuLg4xMbGGl1TgN/PppaSkoLAwECEhoZi3LhxSE9PB9D0rnOLWDXb1PLy8qDT6Yw+IADw8/NDcnKylUrVvGRnZwNAndfYsC87Oxu+vr5G++3t7dGqVSvpGDKm1+sxbdo03HHHHejatSuA6uuoUCjg4eFhdOy117quz8Kwj6odPXoUUVFRqKiogKurK3799VdEREQgMTGR19iEfvjhBxw6dAgHDhyotY/fz6YTGRmJFStWoGPHjsjKysK8efMwcOBAHDt2rMldZ4YZohYkLi4Ox44dw65du6xdlGapY8eOSExMRHFxMVavXo2JEydix44d1i5Ws5KRkYEXX3wR8fHxcHR0tHZxmrURI0ZI/+/evTsiIyMREhKCn376CU5OTlYsWW1sZroF3t7esLOzq9VrOycnB/7+/lYqVfNiuI43usb+/v7Izc012q/ValFQUMDPoQ7PPfcc1q9fj23btiEoKEja7u/vD41Gg6KiIqPjr73WdX0Whn1UTaFQIDw8HH369MHChQvRo0cPfPzxx7zGJnTw4EHk5uaid+/esLe3h729PXbs2IFPPvkE9vb28PPz47U2Ew8PD3To0AFnzpxpct/TDDO3QKFQoE+fPvjrr7+kbXq9Hn/99ReioqKsWLLmo127dvD39ze6xmq1Gvv27ZOucVRUFIqKinDw4EHpmK1bt0Kv1yMyMtLiZW6qhBB47rnn8Ouvv2Lr1q1o166d0f4+ffrAwcHB6FqfOnUK6enpRtf66NGjRuExPj4eKpUKERERlnkjNkiv16OyspLX2ISGDRuGo0ePIjExUfrq27cvxo0bJ/2f19o8SktLcfbsWQQEBDS972mTdiduQX744QehVCrFihUrxIkTJ8TTTz8tPDw8jHpt042VlJSIw4cPi8OHDwsA4oMPPhCHDx8WaWlpQojqodkeHh7it99+E0lJSeL++++vc2h2r169xL59+8SuXbtE+/btOTT7GlOnThXu7u5i+/btRkMsy8rKpGOmTJki2rRpI7Zu3Sr++ecfERUVJaKioqT9hiGWw4cPF4mJiWLjxo3Cx8eHQ1lrmDVrltixY4dITU0VSUlJYtasWUImk4nNmzcLIXiNzanmaCYheK1NZfr06WL79u0iNTVV7N69W0RHRwtvb2+Rm5srhGha15lhphE+/fRT0aZNG6FQKES/fv3E3r17rV0km7Jt2zYBoNbXxIkThRDVw7PffPNN4efnJ5RKpRg2bJg4deqU0Tny8/PFY489JlxdXYVKpRJPPPGEKCkpscK7abrqusYAxPLly6VjysvLxbPPPis8PT2Fs7OzeOCBB0RWVpbRec6fPy9GjBghnJychLe3t5g+fbqoqqqy8Ltpup588kkREhIiFAqF8PHxEcOGDZOCjBC8xuZ0bZjhtTaNRx99VAQEBAiFQiFat24tHn30UXHmzBlpf1O6zjIhhDBtXQ8RERGR5bDPDBEREdk0hhkiIiKyaQwzREREZNMYZoiIiMimMcwQERGRTWOYISIiIpvGMENEREQ2jWGGiIiIbBrDDBEREdk0hhkiIiKyaQwzREREZNP+H86eSNX5aKcOAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_reward_per_episode(reward_ep_sarsa)\n",
    "plot_reward_per_episode(reward_ep_q_learning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vry9OTk4V7i+of9c2NjbGWsXQ0FCOHz9eoVxl771cXf/bIcxHmtVEvdezZ086derEBx98QEFBAX5+fvTs2ZMvvviCCxcuVCh/8eJF4/P77ruPvXv38ttvv1UoV1Z7cOedd5KYmMhPP/1k3FdSUsJHH32Ei4uLsUnDXBISEkziycrK4rvvvqN9+/YEBAQAMHToULZs2cLKlSsrvD8jI4OSkpJqnTMvL4+CggKTbU2bNsXV1dU4nLyslqV8rUpRURGffvpplc5ha2tboZbso48+qlADU5XPpGnTpgAmfav0ej1ffvlllWKpjqZNm5KZmcm+ffuM2y5cuFBpfJer7JoXL15coV/NnXfeSUJCgnF6AVA/k8uvp7LPQFEUPvzww6pfEPDll19SXFxsfP3ZZ59RUlJC//79AbVfn1ar5b///a/Jub755hsyMzMZMGBAtc5Xpjp/m1WNtTKpqakmr7VaLVFRUSiKQnFxMba2tvTr148//vjDpHkuKSmJH374gW7duuHm5gaon83WrVvZvn27SZyLFi265vXW9b8dwnyk5kg0CFOmTGHIkCHMnz+fsWPH8sknn9CtWzfatGnD448/Tnh4OElJSWzZsoVz584Z55mZMmUKS5YsYciQIYwaNYoOHTqQlpbG0qVL+fzzz2nXrh1jxozhiy++YOTIkezcuZMmTZqwZMkSNm3axAcffGCWId/ltWjRgtGjR7Njxw78/f359ttvSUpKYt68eSbXu3TpUgYOHMjIkSPp0KEDubm57N+/nyVLlhAXF1etIe/Hjh2jT58+DB06lKioKOzs7Pjtt99ISkoydmTu0qULnp6ejBgxgmeeeQaNRsPChQur3Cw4cOBAFi5ciLu7O1FRUWzZsoXVq1fj7e1tUq4qn0mrVq249dZbmTFjBmlpaXh5efHjjz9WOymsigcffJBp06Zxzz338Mwzz5CXl8dnn31GixYtTDpVX+maX3nlFR577DG6dOnC/v37WbRokUltDKgdnT/++GOGDx/Ozp07CQwMZOHChRUmUoyMjKRp06Y899xznD9/Hjc3N3755Zdq95EqKioyft5Hjx7l008/pVu3btx9992A2uF4xowZvPzyy9xxxx3cfffdxnK33HKLsZ9ZTVT1b7OqsVamX79+BAQE0LVrV/z9/Tl8+DAff/wxAwYMMP69vvbaa8TGxtKtWzeefPJJ7Ozs+OKLLygsLGTu3LnGY02dOpWFCxdyxx13MGHCBONQ/rJaoaup6387hBnV9fA4IWqqbGjvjh07KuzT6/VK06ZNlaZNmyolJSWKoijKyZMnleHDhysBAQGKvb290qhRI2XgwIHKkiVLTN6bmpqqPPXUU0qjRo0UrVarBAcHKyNGjFBSUlKMZZKSkpTHHntM8fHxUbRardKmTRuT4d6KcmnY/OXD4cuGly9evPia1xMaGqoMGDBAWblypdK2bVtFp9MpkZGRFd6rKIqSnZ2tzJgxQ2nWrJmi1WoVHx8fpUuXLso777xjHPp8pZgURVEA5aWXXlIURVFSUlKU8ePHK5GRkYqzs7Pi7u6udO7c2WRouaIoyqZNm5Rbb71VcXR0VIKCgpSpU6cah7+XH25emfT0dOM9dHFxUWJiYpQjR45UGP6uKFX7TE6ePKn07dtX0el0ir+/v/L8888rsbGxlQ7lr2xYd9m9ruy+jB8/3mTbqlWrlNatWytarVaJiIhQvv/++yoP5Z88ebISGBioODo6Kl27dlW2bNlSYci3oijKmTNnlLvvvltxcnJSfHx8lAkTJigrVqyocD2HDh1S+vbtq7i4uCg+Pj7K448/ruzdu7fCFASVKfudW79+vTJmzBjF09NTcXFxUYYNG6akpqZWKP/xxx8rkZGRir29veLv76+MGzdOSU9PNylzpft7NVX526xOrJffzy+++ELp3r274u3treh0OqVp06bKlClTlMzMTJP37dq1S4mJiVFcXFwUJycnpVevXsrmzZsrxLtv3z6lR48eioODg9KoUSPl1VdfVb755ptrDuVXlKr92yGsj0ZRatAbVAhRK5o0aULr1q1ZtmyZpUMRDdD8+fN57LHH2LFjh3FQg7WqT7GKhkf6HAkhhBBClCPJkRBCCCFEOZIcCSGEEEKUI32OhBBCCCHKkZojIYQQQohyJDkSQgghhChHJoGsJoPBQEJCAq6urjVeIVwIIYQQdUtRFLKzswkKCqqwGPDlJDmqpoSEhAoreQshhBCifjh79izBwcFXLSPJUTWVTfd+9uxZ49o7QgghhLBuWVlZhISEVGnZFkmOqqmsKc3NzU2SIyGEEKKeqUqXGOmQLYQQQghRjiRHQgghhBDlSHIkhBBCCFGO9DmqJXq9nuLiYkuHIWqBvb09tra2lg5DCCFELZHkyMwURSExMZGMjAxLhyJqkYeHBwEBATLXlRBCNECSHJlZWWLk5+eHk5OTfHk2MIqikJeXR3JyMgCBgYEWjkgIIYS5Najk6O6772bPnj0kJyfj6elJ3759eeuttwgKCjKW2bdvH+PHj2fHjh34+vry9NNPM3XqVLOcX6/XGxMjb29vsxxTWB9HR0cAkpOT8fPzkyY2IYRoYBpUh+xevXrx888/c/ToUX755RdOnjzJ/fffb9yflZVFv379CA0NZefOnbz99tvMnj2bL7/80iznL+tj5OTkZJbjCetV9hlLvzIhhGh4GlTN0aRJk4zPQ0NDmT59OoMHD6a4uBh7e3sWLVpEUVER3377LVqtllatWrFnzx7ee+89xowZY7Y4pCmt4ZPPWAghGq4GVXNUXlpaGosWLaJLly7Y29sDsGXLFrp3745WqzWWi4mJ4ejRo6Snp1d6nMLCQrKyskweQgghhGi4GlxyNG3aNJydnfH29iY+Pp4//vjDuC8xMRF/f3+T8mWvExMTKz3enDlzcHd3Nz5k0VkhhBCiYbP65Gj69OloNJqrPo4cOWIsP2XKFHbv3s2qVauwtbVl+PDhKIpS4/PPmDGDzMxM4+Ps2bPmuCyrdPHiRcaNG0fjxo3R6XQEBAQQExPDpk2bTMpt2bIFW1tbBgwYUOEYcXFxJp+Nl5cXPXr0YMOGDSbl8vLymDFjBk2bNsXBwQFfX1969OhhksyWOXfuHFqtltatW5v3goUQQohKWH2fo8mTJzNy5MirlgkPDzc+9/HxwcfHhxYtWtCyZUtCQkLYunUr0dHRBAQEkJSUZPLestcBAQGVHlun06HT6a7vIuqJ++67j6KiIhYsWEB4eDhJSUn8/fffpKammpT75ptvePrpp/nmm29ISEgwGQ1YZvXq1bRq1YqUlBRef/11Bg4cyLFjx4w1dWPHjmXbtm189NFHREVFkZqayubNmyucC2D+/PkMHTqUf/75h23bttG5c+fauQFCCCEsSlEUkrMLKSjWE+rtbLE4rD458vX1xdfXt0bvNRgMgNpvCCA6OpqZM2caO2gDxMbGEhERgaenp3kCrqcyMjLYsGED69ato0ePHoDaqb1Tp04m5XJycvjpp5/4999/SUxMZP78+Tz//PMVjuft7U1AQAABAQE8//zz/Pjjj2zbto27774bgKVLl/Lhhx9y5513AtCkSRM6dOhQ4TiKojBv3jw+/fRTgoOD+eabbyQ5EkKIOpJbWEKx3oCHk/baha9yjLPpeZxLyychM58SvYLWzoYQLyeSswqIS80lLiWP0ym5xKXmklek57bmPiwcbbl/660+Oaqqbdu2sWPHDrp164anpycnT57kxRdfpGnTpkRHRwPw8MMP8/LLLzN69GimTZvGgQMH+PDDD3n//fdrLS5FUcgv1tfa8a/G0d62yqOqXFxccHFx4ffff+fWW2+9Ym3Zzz//TGRkJBERETzyyCNMnDiRGTNmXPE8+fn5fPfddwAmHeEDAgJYvnw59957L66urleMa+3ateTl5dG3b18aNWpEly5deP/993F2ttz/KIQQoqEoq6k5k5pHfFoe8am5nEnLM75Oyy0C4LbmPtwa7s2FzHwuZBSgVxT6tvTnoU6NKTEYOJeez9m0PM6m53MuLU99na7+LDtGVdlooFhvqI3LrTKNcj0dcqzI/v37mTBhAnv37iU3N5fAwEDuuOMOXnjhBRo1amQsV34SSB8fH55++mmmTZtW5fNkZWXh7u5OZmYmbm5uJvsKCgo4ffo0YWFhODg4AJBXVELUrJXmuchqOvRKDE7aque/v/zyC48//jj5+fncfPPN9OjRgwcffJC2bdsay3Tt2pWhQ4cyYcIESkpKCAwMZPHixfTs2RNQ+xyFhYXh6OiIjY0NeXl5KIpChw4d2LJli7HG7p9//mHYsGEkJSXRrl07unXrxv3330/Xrl1NYho2bBh+fn7GBLZ9+/ZMnDjxmk2tta2yz1oIIayRwaAmQHGpuZxJzeXUxVxOpajP49PyKCiueSLi4WRPZn4x18ok3B3tCfZ0pJGHIzp7W7ILiolPzSPQw4Em3s6E+aiPJj7OhHg6obUzf5foq31/X67BJEd1pSEnR6Bew4YNG9i6dSt//fUX27dv5+uvv2bkyJEcPXqU1q1bc/78efz8/AB46qmnyMzMZOHChcCl5Gjp0qVERkZy4MABpk6dym+//VahQ3VxcTFbt25l8+bN/P3336xevZqXX36ZF198EVCb+gIDA9m4caOxye2dd97hjz/+qNDBu65JciSEsCZ6g0JCRj5nUvOIK0164lJyOZOax5m03KsmQDYaCPJwJNTbicZezoR6OxHq5URjbycaezmRmFnA5+tPodFAoLsDAe4OpOcW8fHaE8bjOmltaezlRLCnE8GejoR4lf70dCLYyxE3B/u6uhVXJMlRLapuclRfmtWu5D//+Q+xsbGcOXOGqVOn8vbbb5ssl6EoCjqdjgsXLuDu7m5Mjnbv3k379u0BWLx4Mc8//zwHDhy4auf21157jVdeeYWcnBy0Wi2ffvop48ePr3A+g8HA0aNHadGixXVd2/WQ5EgIUdcMBoXzGfmcSsklrrR/TlkydC4tn6KrNEXZ2mgI9nQk1NuZcB9nwn2dCfV2JtTLiUaejtjbVr+mJiWnkPi0PEI8nfBx0Vr95LjVSY4aTJ8ja6XRaKpde2NNoqKi+P333ykpKeG7777j3XffpV+/fiZlBg8ezP/+9z/Gjh1b6THuv/9+Zs2axaeffmoyi3ll5yopKaGgoACtVss333xT6WjFJ598km+//ZY333zzuq9PCCGsTU5hCacu5nDyYo7aBHYxl5MXczidkkthyZUTIK2tDSFejjTxVhOfJj5O150AXY2Piw4fl4Y5mrv+fmsLs0pNTWXIkCGMGjWKtm3b4urqyr///svcuXMZNGgQy5YtIz09ndGjR+Pu7m7y3vvuu49vvvnmismRRqPhmWeeYfbs2TzxxBM4OTnRs2dPHnroITp27Ii3tzeHDh3i+eefp1evXri5ubFnzx527drFokWLiIyMNDneQw89xCuvvMJrr72GnZ38Cgsh6p+yZrATxgSo9GdKDklZhVd8n9bWhlBvJ2P/nFBvp9JkyIlAd0dsbay79qa+kG8WAaij1Tp37sz777/PyZMnKS4uJiQkhMcff5znn3+eoUOH0rdv3wqJEajJ0dy5c9m3b98VqypHjBjBzJkz+fjjj5k6dSoxMTEsWLCA559/nry8PIKCghg4cCCzZs0C1LmUoqKiKiRGAPfccw9PPfUUy5cvN04NIIQQ1kRRFC7mFBKXkse59DzOp+cTXzoKLCO/iDOpeVetBfJx0RHu60xTXxea+jobnzfycMTOzDVAoiLpc1RN1e1zJBom+ayFEGUKS/QcT8rhSGI2Ry5kcSQxm8MXski9xhB2ra0NTXycaOrrQrivM+E+pT99XXB3tHwH5oZG+hwJIYQQNaQoasfnE8k5pR2f88grKuGxrmF4Omk5fCGLw4lZHLmQzZHELE5ezEVvqFjPoNFgHLHVyMORYE8nmvg44eGkpYm3OrJLmsGskyRHQgghGhSDQeFMWh6HErIo1huIaRWAo9a20rKKonA2LZ/95zPZfz6Tgwnqz4y84gplf/733BXP6e5oT2SAKy0D3WgZ6EpkgBst/F2veF5h3SQ5EkIIUW/pDQqnLuZwICGTA+ez2H8+k0MJWeQUlhjL3NzYg29H3oKLzo5TKbkcSsjiYEImBxOyOHA+k6yCkgrHtbfVmExMuDs+g+2n07C10RDu40zLQDciA11pGaD+DHBzsPqh7KLqJDkSQghRZwqK9ZxLz8PLWYeXc9XX6yob3VW2/tbJ5BwOJGRxKCGr0rnkdHY2hPk4cyQxm13xGbR/JRadnU2lnaC1tjZEBrrSupE7bUofzf1d0NmZzrF2PiMfHxcdDvZSG9TQSXIkhBDiuqTmFPLvmXROXcwl2NORO9sEYqOB+LQ8dsSlk19UwvHkHPaczeDwhSyK9Qr2threuKcNd7cPMklCcgtLOHxBrdE5UFqzk5ZbRHpeEcX6yscPOWltiQp0o3Uj99KHG818XbCzteFYUjaPfrONpKxCCksMOGttaRnoRqsgN6KC1Pc093O95nIVGo2GYE8ns943Yb1ktFo1yWg1AfJZixtXQbGevWczjLU2u+LTOZ2Sa1LmpsYeJGYWcCGz4JrH09rZ0LaRO1o7Gy5kqiu0X+lbSWtrQ+PSeX3CfZ2NCVGYj/NVOzbnFpZw4Hwmfm4OhHo5YSOdoG9IMlpNCCGEWSRk5LP9dBqHL2Sx+2wGe85mUFRJ01QLfxcC3R1Zf+wiu+MzALXfTttgDzxLR2e1C/GgfYgHQR6OzF1xhCU7z5GaW8S/Z9JNjuXvpqN1kDutGrnTOsiNIA9H3B3tCfKo2SSHzjo7Ood71+j6xY1JkiMhhBAAlOgNHEzIYuupVLafTmP/+UySsyvO1uzrquPmxh5EBrjRPsSDmxt74u6kzsuz9kgye85mcEsTLzqEel5xtNaMO1syvX8kcal57DmbjgYNvq46mvu74OcqtbHCsiQ5EkKIG5TBoBiToS2nUtlxOo3sQtORWzYaaBPsQdtG7rQKcqNTmBdhPs5XHJnVK9KPXpF+VTq/RnNpRJgQ1kSSI3HdRo4cSUZGBr///rulQ2H+/PlMnDiRjIwMS4diFsV6A3vPZtDMz4Xfdp/nTGoeU2IicNbJn66ombTcIjYcv8jaI8n8czyFtMtmcXZ1sKNzmDe3hntxU2NPIgNc5fdN3HDkN14YnT17lpdeeokVK1aQkpJCYGAggwcPZtasWXh714/2+gceeIA777zT0mFct/TcIj5ff5Jl+y5wPiPfZJ+3s5an+zS3UGSivinRG9h3PpMNx1JYd0xt8irf4dlFZ0fnMC9uDfcmuqk3LQPdZNZmccOT5EgAcOrUKaKjo2nRogX/+9//CAsL4+DBg0yZMoW//vqLrVu34uXlZbH4ioqK0GqvPSeKo6Mjjo6OdRBR7VAUhaV7E3jlz0MV1mVyc7Ajq6CEBVvOMKZHuMnwZ3HjysgrYu+5TBp7ORmbpy5k5vPPsYusP3aRjcdTKkxyGBngSs8IP3pF+HJzqCf2spCpECbkL0IAMH78eLRaLatWraJHjx40btyY/v37s3r1as6fP8/MmTOrfCyDwcCcOXMICwvD0dGRdu3asWTJEuN+vV7P6NGjjfsjIiL48MMPTY4xcuRIBg8ezOuvv05QUBARERHExcWh0Wj49ddf6dWrF05OTrRr144tW7YY3zd//nw8PDyMr2fPnk379u1ZuHAhTZo0wd3dnQcffJDs7GxjmezsbIYNG4azszOBgYG8//779OzZk4kTJ1b/Rl6Hc+l5PDZ/BxN+3ENqbhEt/F34+OGb2DurH0vGRrP1+T4EuDmQklPI0j0JdRqbsB4FxXo2HL/IG8sPc8cH/9D+lVhGfLudgf/dwOylB4l5/x+i56xh2i/7Wb4/kayCEtwc7LizTQBv3tuGLTN6s2Jid6b3j6RzuLckRkJUQmqOapuiQHGeZc5t76SufHgNaWlprFy5ktdff71CrUtAQADDhg3jp59+4tNPP63S9Phz5szh+++/5/PPP6d58+b8888/PPLII/j6+tKjRw8MBgPBwcEsXrwYb29vNm/ezJgxYwgMDGTo0KHG4/z999+4ubkRGxtrcvyZM2fyzjvv0Lx5c2bOnMlDDz3EiRMnsLOr/Nf55MmT/P777yxbtoz09HSGDh3Km2++yeuvvw7As88+y6ZNm1i6dCn+/v7MmjWLXbt20b59+2teqznoDQoLNsfxzqqj5BXp0dra8FTvZozt0dQ4MV3HJmqt3ciuTXjzryN8s/E093cIluUKbgC5hSVsOpHCvnOZ7D2nLmFx+SzPrg52ZBeUMH9zHKD+2bcL9qBHC1+6t/ClfYiHNJUJUQ2SHNW24jx4I8gy534+AbTXHgVy/PhxFEWhZcuWle5v2bIl6enpXLx4ET+/q49CKSws5I033mD16tVER0cDEB4ezsaNG/niiy/o0aMH9vb2vPzyy8b3hIWFsWXLFn7++WeT5MjZ2Zmvv/7a2JwWFxcHwHPPPceAAQMAePnll2nVqhUnTpwgMjKy0pgMBgPz58/H1dUVgEcffZS///6b119/nezsbBYsWMAPP/xAnz59AJg3bx5BQXXzmZ1OyeW5xXvZWTrPyy1NPJlzbxua+blWWv6hWxrz37+PcyQxm40nUrituW+dxCnq1tm0PNYcSebvI8lsPZlKkd40GQpwc+C25j50b+FLl6be2NnYMGvpAextbejRwpduzXzwrMbSHEIIU5IcCaNrTZZeUFCAi4uL8fXzzz/P888/b1LmxIkT5OXlcfvtt5tsLyoq4qabbjK+/uSTT/j222+Jj48nPz+foqKiCjU1bdq0qbSfUdu2bY3PAwMDAUhOTr5ictSkSRNjYlT2nuTkZEDta1VcXEynTp2M+93d3YmIiKj0WOZiMCh8v+0Mc5YfIb9Yj4vOjun9I3m4U+Orzt7r7mTP0I4hzN8cx9cbTkty1ECU6A3sis/g7yNJrD2SzLGkHJP9od5O3BrmTatGbtwa7k1zP5cKtYYfPngTQgjzkOSottk7qTU4ljp3FTRr1gyNRsPhw4e55557Kuw/fPgwvr6+BAUFsWfPHuP2yjpo5+So/6j/3//9H40aNTLZp9PpAPjxxx957rnnePfdd4mOjsbV1ZW3336bbdu2mZR3dq681sve3t74vOwLwmCoOGNvZeXL3nO18rXtWFI2z/+63zgrcJem3sy9v22V120a1TWMBVviWH/sIseSsmnhX3ktk7AeFzLz+Wt/IklZBTzZqxnujvboDQrbTqWydG8CKw4mkpFXbCxva6OhY6gnfVr60TvSn6a+V55XSAhhfpIc1TaNpkpNW5bk7e3N7bffzqeffsqkSZNM+h0lJiayaNEixo8fj52dHc2aNbvqsaKiotDpdMTHx9OjR49Ky2zatIkuXbrw5JNPGredPHnSPBdTTeHh4djb27Njxw4aN24MQGZmJseOHaN79+5mPVex3sDn607y3zXHKdYrOGltmXZHJI/eGlqttZ4aezsRExXAioOJfLPhNG/d3/babxJ1LrugmL8OJPL77vNsOZVqHD6/4mAi0eHerD6cTErOpdmnPZzs6dnCl94t/enR3Nc447QQou5JciQA+Pjjj+nSpQsxMTG89tprJkP5W7RowaxZs6p0HFdXV5577jkmTZqEwWCgW7duZGZmsmnTJtzc3BgxYgTNmzfnu+++Y+XKlYSFhbFw4UJ27NhBWFhYLV9l5fGOGDGCKVOm4OXlhZ+fHy+99BI2NjZm/Z/60cRsJi/ew4HzWQD0ifTjlcGtaeRRs2kHHu8exoqDify25zzPxUTg66ozW6yi5opKDGw8cZHfdiew6mCiScfpYE9HzqXncyY1jzOp6iANDyd7+rcO4K62QXQK88JORo4JYRUkORIANG/enB07djB79myGDh1KcnIyiqJw7733snDhQpycqtbkA/Dqq6/i6+vLnDlzOHXqFB4eHtx8883G/klPPPEEu3fv5oEHHkCj0fDQQw/x5JNP8tdff9XW5V3Ve++9x9ixYxk4cCBubm5MnTqVs2fP4uBw/es7legNfL7+JB/+rdYWuTva8/LdrRjUPui6kq+bG3vSPsSDPWczWLj1DM/e3uK6YxU1U1CsZ8WBRH7cEc+/cemUGC713Wvq68y9Nwdzd7sgQryc2HM2g993n8dRa0unMC+6NvUxjkgUQlgPjXKtXrjCRFZWFu7u7mRmZuLm5mayr6CggNOnTxMWFmaWL1ZLe+mll3jvvfeIjY3l1ltvtXQ4dSY3N5dGjRrx7rvvMnr06ErLVOWzPpaUzXOL97LvXCYAfVv688Y9rfFzM8/vxrJ9CTz1w248nezZPL3PFRf4FOaXX6Rn3dFktpxK5Y89CWTmX+ov5OOi5a52QdxzUyPaNHKXvkJCWImrfX9fTmqOxBW9/PLLNGnShK1bt9KpUydsbBrm/3B3797NkSNH6NSpE5mZmbzyyisADBo0qMbH/GFbPLOXHqRIb8DNwY6XB7VicPtGZv2ivKNVACFejpxNy+fz9SeZJLVHtUJRFEoMCvmlNUR/7k3g37h08ov1xjKNPBwZ2jGEe25qRIiXoyREQtRzkhyJq3rssccsHUKdeOeddzh69CharZYOHTqwYcMGfHx8qn2cEr2B15cfZt6mOAB6Rfjy5n1t8TdTbVF5drY2jOnelBd/P8CHfx/HUWvL2B5NzX6eG1VCRj6L/z3HL7vOcTY9j8vr2IM9HbmtuS8xrfy5rbmvTLIoRAMiyZG44d10003s3Lnzuo+TnlvExJ/2sP7YRQCe69eC8b2a1WotwrBOjTmZnMP8zXF8suYED3dujJtDxVFOBoNC7OEk1h29yH9uC6Opr0slRxN6g8I/xy+yaOsZ1hxJxnBZQtTMz4XB7YPoG+VPCz/Xao0yFELUH5IcCWEGB85nMnrBDpKyCnGwt+H9oe3p3yaw1s9rY6Nh1sAoNp1I4XhyDh/9fZyZA6KM+wtL9PyxO4HP/znJqYu5ABxJzOLXcV2k6aeUoijsis9g0dYzbDyRQnL2peH1t4Z78eAtjWkT7A5AuI/MNyTEjUCSo1ogfdwbvvKfceyhJCb8uJu8Ij1NfZ3570M30SrIvc5isbHR8FxMBE8s3MlXG07TtZkPN4d6smhrPPM2nTb5sgfYHZ9B3/fW4+/mwAcPtsfPtf4PHqiJwhI9/7fvAvM3xxk7zQO4Odhxf4cQHu7cmGZ+UsMmxI1IRqtV09V6u+v1eo4dO4afnx/e3t4WilDUhdTUVJKSkvhibz5/7E0EoGszbz57pEOlzVp14aU/DrBgyxmCPR3JKSwxzrgc4ObAqG5NeLhzKF/+c4r//n3c+J4QL0fCfVwIcHPgzfvaXLNWJCOviK83nFZHyvVuzv0dgmv1mmpDclYB32+L54dt8cZJGLV2NtzdLoh7b2rEzaGeONjLyD8hGprqjFaT5KiarnVzL1y4QEZGBn5+fjg5OUkVfAOjKAp5eXkkJSUReyKH9zdfxN5Ww/DoJky7I9Kic9ak5hTS7a21xlFUTX2dGdezGXe3CzLGpSgKfx9OZsGWODYcTzF5/02NPXjl7tbGJqTy0nOL+GbjaeZvjiOnsAQAZ60ta5/rabapCWrb7vh05m+OY/n+CxTr1X/2AtwceDQ6lAdvCcHbRSbSFKIhk+SoFl3r5iqKQmJiIhkZGXUfnKgTeoPCimNZfLItFWedHV+P6Mit4dZRU7hw6xl+2BbPo7eGMrRj8FVnXH7+t/1sOH6Rs2n5xm0+LlrWTemFi05tcU/PLeLrjaeYvymO3CI16WoZ6IbBoHA0KZv7OwTzzpB2tXtR16GoxMBfBy7w7aY49p7NMG7vGOrJyK5NiGkVgL3MSi3EDUGSo1pU1Zur1+spLi6+4n5RP6XkFDL2hz0cSMjB08me70Z1rrSmpT7JLijmiYU72XwyFYAJfZoztkdTPl9/kq83nDImRVGBbkzo25zbW/qz91wG93y6GYA/xnelXYhHncVbrDdcMaFJyy1i//lM9bPZcoa/9l8wxq+1teGudkGM7NKk3n9mQojqk+SoFlXn5oqGw2BQ+HNfAnNXHOV8Rj4Bbg4sHN2J5v6ulg7NbP5v3wXG/7ALJ60t7o72XMgsANSkaGLf5twe5W/STDz55738suscAHPubcMDHUNqbWh7Zn4xC7fE8fO/54hPy6OFvwtfDe9IqLcziqKw91wm/7cvgUXb4skr0pu8189VxyO3hvJw58b4SNOZEDcsSY5qkSRHN56iEgPP/ryHZfsuABDq7cT3ozsT4lX19ebqA0VRGPzpZmPzUyMPR2bcGcmANoGV9p1Lziqg1zvrjDUzL9/dihFdmlQodzwpm/3nM7mrXRD2tjYkZxfgpLUzNt1dLZ4dcenEHkrkf9vPGvs6lWnq60yPFn78eybNZLRZmd6RfjzZsyk3N/aU+YiEEJIc1SZJjm4shSV6nvnfblYeTMLeVsMT3ZsyqlsYXs5aS4dWKw4lZDF76UFua+7D493Drzlq648955nw4x4AXB3sWPtcT2PtzMGETD5ff4r/25eAQYE72wQQl5LHoQtZBLk78NeE7rg7VT6yb9+5DN5Yfpitp9KM2yL8XRnXsymh3k48+s12k2RJa2tDn5Z+3N8hmF4RfhTpDTLiTAhh4oZPjgoLC+ncuTN79+5l9+7dtG/f3rhv3759jB8/nh07duDr68vTTz/N1KlTq3xsSY5uHPlFeh6bv52tp9LQ2trw9YiOdG/ha+mwrI7eoHD3xxs5mJAFwIjoUE5ezGXjiZSrvm9g20DeG9reZITf6ZRc3os9xp97EwB1iP0drQK4q10QfSL9jDVAB85n8snaExSVGIhpFUD3Fr4EuNePUXNCCMu44ReenTp1KkFBQezdu9dke1ZWFv369aNv3758/vnn7N+/n1GjRuHh4cGYMWMsFK2oK8nZBfi66Ko0vUJmfjFPLtrJ1lNpuOrs+OyRDnRrXv211m4EtjYaXhnUmvs+UztoL9hyBgAbDQxsG8QTPcJZuOUMS/cm8OitoXQI9eSJ73eybN8F1hxJZuaAlnQO8+LtlUdZeTAJAI0G7rmpEZP7RdDIw7HCOVs3cuezRzrU3UUKIW4oDa7m6K+//uLZZ5/ll19+oVWrViY1R5999hkzZ84kMTERrVZtFpk+fTq///47R44cqdLxpeao/jEYFGb8up+f/j3LW/e14YFbGl+1/MmLOfxnwb+cTsnF0d6WhaM70bGJVx1FW3/9sec8B85nci49H383B0Z1DaOxt9ovq+yfmbLEdNXBRJ5bvJesghJsbTQoioJBUZOi3hF+PNuvRZ3OMi6E2aSehKzz0OQ29Re6PlMUyDwLzn5g7wBppyDzHIR0BrtaHtxg0IONeZvGb9iao6SkJB5//HF+//13nJwqdpbdsmUL3bt3NyZGADExMbz11lukp6fj6elZ4T2FhYUUFl5afiErK6t2ghe1orBEz+Sf9xo7Uy/aFs9d7YJw0qq/+ufS85i6ZB/dW/gytkdTjiZm8/BXW0nNLSLI3YEvh3ekdSP5kq6KQe0bMah9o0r3XV5b169VALsi/Ri94F/jQr39ovx5LiaCFg1oBGCDoChwcg1s/xKyEuDm4RDYHhrdDEeXw8Hfod1D0LyvpSO1nMxzcOAX2L8YEver2yIHQp9ZcGYTuDcG/1Zw+h8I7wGuAZaN92qKCyBuIxxbAcdWQmY82OrA2Rey1NGp+LWCHlPgwl6wd4bQaPCJAEcPiN8Kx1dBdiJ0GgMht0BxPpzdDh6NwSvs0rn0JWAoURMvfbFa5kQsHF8NQe1h0MeWuANAA0qOFEVh5MiRjB07lo4dOxIXF1ehTGJiImFhYSbb/P39jfsqS47mzJnDyy+/XCsxi6op0RtIzi4kqJLmlWuZvfSgMTEC2Hcuk6hZK/m/Z7rh5+rA4E82kZJTxOaTqbQL9uCpH3aRmltEqyA3FozqJEO/a5GdrQ3/fegmvt96huim3tzcuOLfn6gDJUVwYAls+VT9onrof+oXWGEO7P2fmhSlHLtUfvlzgAbcGl36sjz4G9z7JbS53yKXYBG5qXDod9i/BOI3V9x/ZJn6qIydAzSOhqIcKMiE0C5qbUy7h8xf26QvBttrLGmUnagmQsdWwqm1UJx32TEK1c/axg7sHCH5ICweWfE4tjq1bJkDS6BxFzi/E0pKJ5t18IDAdqBzhbgNUJgNvpGQeR4Ky406zU9TE3ML1b5ZfXI0ffp03nrrrauWOXz4MKtWrSI7O5sZM2aY9fwzZszg2WefNb7OysoiJCTErOcQV6Y3KIxa8C//HLvIL+OiaebrioKCh9O1R4st/vcs/9t+Fo0Gvh1xC68uO8SpFHVl+u+3xnMoIZOUnCJj+Ye+2gpAm0buLBzdqUrnENfH3dGe8b2aWTqM+kVR4MTfsOEdyL4AD3wPAW1My5QUql/aSQfhtmfBuZL+cvkZsGsBbP0cshMubZ8/EFoOhD0/QGFpTbnWFW4aBud3wbntgKJ+WTp6gl+UWjvyy3/UuJr1gVb3XGoSMRgg6QD4tFBrCOqzwmw4slz90j+5Rk0my4R2gzb3QdRgSDsNPw9Xm9ecvCAv1fQ4JQVqElIm5RjsnA9H/g8GfQIO7nBhDxxaqtZK9ZwO3k1Nj6EoapmMeGjaWz3G4WVwYrV6Tq+mag1QyjGIGgQD31e3g/qZJO4tTYhWQMJu02O7BkKLGGhxh5q0HV+lJjNNblN/t1ZMh/P/qrVFFw+rMYCaGDn5QPPbQV+k1qad2Wh67IIMOL3edFvyIfWno5f6+9PsdvWnBZslrb7P0cWLF0lNTb1qmfDwcIYOHcqff/5pUn2v1+uxtbVl2LBhLFiwgOHDh5OVlcXvv/9uLLN27Vp69+5NWlpapTVHl5M+R3Xrv38f571Y9X+td7cLYvPJVDQa+GdKLwAc7G0q7WB9KCGLez7dRGGJgWdvb8EzfZqz8mAiTyzcaVLO08mezmHerDioLh57R6sA3rq/Le6Ollk8VtygDAa1P4d30yt/ISiK+kW2/i3TLzNnX3hsBfg0g4Is+Pdb2Pop5Kid2/FvDSP+vPTFmBGv1hLtXqjWXAC4BECnx2Hvj5B6aWFivJpC5yeg/cPql6OiqI8934PGBlrdq9aCLJ+snrc872bQvJ/6BZx2Ut0W3ElNsjxCIbynGoutFtwCr/sWXreSQjgeC0W50GYI2JSOoiwuUJt69i9R739JwaX3BLRVy7a+F9wvW4S5OF99OHlB8mH1/jn5qElV8iE4twOCO8KFfer+svvuFqyeuyzhADU5vesDNck5s7m0Vmr5pZq7qnANhO5T1ITq2CrISTTd36iDmgy1iFGvqzqJicGgJsj2jhB086V7d2q9eq1Nuqm/h0kH1GuN26jWPDbro97vxP3Q+FYIusns/YzKuyGH8sfHx5v0B0pISCAmJoYlS5bQuXNngoODjR2yk5KSsLdXv/yef/55fv31V+mQbYW2nkrl4a+2YqjkN/SpXs34euMp7rkpmDn3mv6vOT23iMGfbuJMah49I3z5dsQtxiHg+UV62r2yiqISAwBLxkbj5+rA8G+30a9VANPviJQJA0XdKUt41rwOSfvVZKIwBzLOwJAFan8NgwGO/An/vH2pP4udI3QcBXH/qNvcgqHtEDVBKShtmnANAkMx5F5UmzHueEvdf+AXUEpnEfeLglufhLZD1Q622Ynw6xg14en0ODTtc+mL7lrXsfZ12PzxpeaTqrJ3gqHfqbUNdSX5COz9Qa3pCmgLWme1Nig/Xd0fORBuelRtNju8DIqyL73Xu7nafNj6PvBpbr6Yzu9Sm6oy1NGe2Dmq9yQnGc6qtdpoXU1jsXO4lKzZO6vlA9tC4gE1eY3or/Zv+nOiadJbVr5pLzUhat4PXP3Ndy1W6oZMji4XFxdHWFiYyWi1zMxMIiIi6NevH9OmTePAgQOMGjWK999/v8pD+SU5qhsHzmfy2PwdXMwupE+kH38fSb5i2a+Hd+Trjad4dVBrwnycGfb1NradTqORhyPLnu6G52UTNn6/9Qwbj6fwTJ/mRAXJZygsoKyT89rX1f4YlXH0hF4zYcc3atMFqF9onR6H6KfAxRdyLsK8/qZffN7NodsktUYj7RTMv7Nis05YD+j6jJr8mLvp4uwOtcnowC9qwtVhpFpjtP0LOLlWHf10OY0t3P0RuDdS78vNIyo2I11NXhoohorNh3lpan+ofT+rNWmhXdXaiwt7Kj+OS4Da10VfZLrdLVitHWpzf/VrVaojPwN2zgOvcGjWV03a9CXwz1xYPxdQwMlbTXoiB6q1bwa9+vn7trxys2VRLqx5Ta11Cums1g416Vb7I86sjCRHVJ4cgekkkD4+Pjz99NNMmzatyseV5Kj2/X04ice/+xeDoi4R8efT3Rj08SaOJ+dwSxNPdsSlV/q+u9sFEeLlyCdrT+Kis+PXJ7vIyCdhfeI2qV9UZZ147Z3UUT0+LdTOz837wcm/TZMmnZvavHXrk5eax8pknoMfHwYbezXhiRxo2jSRuB8W3KXWKEUNgq4T1OYLSynKg4Rdak1Jy4Gw7i3Y96NpGUdPuPMdtempKFe9pmN/qYlJx1FqclJcoDYv7f2fmnTZ6eCeL0qb8laoCdHxVWrt2eVs7NRytlo49y807qw2HYb3Upssfx6h9p+JGqwmRMGdqlaDVpsS96u1isG3gK3Vdxe2SpIc1SJJjmpXem4Rt7//Dyk5hXRp6s1b97UlxMuJ40nZHEnMplOYF93nrsXORoO/m4Oxg/Xl3n+gHffcFFzpPiFM6EvUpgmdS+2e5/wu+PuVSx1xbXVwy2i1lsfFz7Rsbip8N0itZbn1STUxcvSo+bnzSmtDrHEIuaLA6tmw6QO1ZswtEFJPXLn8TY+oyeLe/11qBitP53apIzmoCVXkQLUDcUGm2k+qzf2Vd1IvY9CrzVL1fZ4iYUKSo1okyVHteuZ/u1m6N4Hmfi78+XS3StfHOpaUjbPOjt3x6Tz1w27CfZxNkqQXBrRkdLewKs2ELW4Q+hL1i658jYqiwKE/1JE3xfnw2HJ1LhpzSz+jJkUHlqivbezVuYJum6w2I12JQQ9oLF9jUVcuHlWTNxt7+G2M2uE4qD0kHVI/u/CecPQvoNxXllsjNVlqfZ/a/Lj9i9LtpX2w2j4Afi0tcDHCGklyVIskOao9P26PZ/qv+7G10fDruC60C/G4anlFUdh/PpMW/q6MX7SLv48kMyI6lJcHta6bgEXdURR1WLRbo6r9b/7EanWCus5j1RFYq19WR3ON+FPtx5F+BpZPgeMrL73HozH0mA4hnczT0TY/Aza+pw6V1xcCGvXLutcM8Gxy/cdv6EqKwE6rjiJTDOpIqCPLYeUMdeTTzSPU0U7lE96Ta9QaucbRN05SKapMkqNaJMlR7fjf9nhe+P0AeoPCc/1a8FTv6n05JWUVsO10Gne2DsDOVv5RbFAy4mHp03BqndoROeb1K5fNTYUV09SZikHtu1K+6aXNUAhoDeveVCe6s7FXOwwfXnpp6LvOHR5fo9ZibP8SEvfB7a+oyVNVlBSpo8LWv6V27gUI6w63v6rWhAghLEKSo1okyZH5nbyYQ7/3/0FvUBjSIZi37msrw+mF2hS2eyGsetF0+PL936pzslxe+3Lwd/i/yZCXovYXAbXGQesKzXqrE+qVb5IJ7apOjOcboc41M+/OS+fxCldrLLLOq6/9omB06UR4V6IocPhPWP2SOkoM1Jl/b39VHWItzbxCWJQkR7VIkiPzG7twJysOJtIrwpdvR94ifYVuVNmJ8NdUSD2lrtv096uXhqiHdAbPMNNRTU17Q8Sd6mzMy59Th2yDOqR58Cdq09n5nWptk1sgbPovxL6o1ib1ew3aDzNNWHJT1dl75w+8NGN0+XlkWtwB934FexapI8u8m8HBX9X+LjnJsHLmpflonP2g1/PqXDkyskgIqyDJUS2S5Mi8dp5J477PtmCjgRUTu8vQ+/qoMBv+mq7OuDvoE3W0kNZJnYPH2adqNSYHf4NlkyqOPnL0Umf17fyEulTDV33UyRLL07qoMz1rbNWlMrpPufL8Led3qTVOlw+HLy9hj9ofKew2uO05dUbf+XeqSZK9MxTnqueysVP7EuncL60JZecIXZ5Wh9RfrZZJCFHnJDmqRZIcmY+iKNz/+RZ2nknnwVtCePO+tpYOqX4pLlDXYwq5RZ287tDv6lwtjrW8gGvqSdj2hTpKKD8d/nwG0uMu7fdupvax+fdbtemq5d3Q7sHKh6Lnpqijxcr6CAW0UdelKspRh1wPfM/0evLT1XmCtn6qLldQxi8KBn9ae/P37F8Cv4xWn9s5XpoF2lizpFGXxeg1E9yCaicGIcR1keSoFklyZD4rDlxg7Pe7cLC3Yf2UXvi71fNFKWubosCWT9S1ivq9BktGqXPmOHioI7DK+se0ukedQO9q87hUR3GB2nnZyUsdDbR4pDpfjIO7upYXVfwnpFlfdTTYxvfVRKlpb1j2rFrrorFRh7Z3n6omWrnJamJ1pVong0E976YP1Rqczk/U/my/B35Vm8/aPQi7vlOTQP9WsPt7iLq74uKvQgirIslRLZLkyDzOpuUx6JNNpOUW8VSvZjwXE2HpkKxTSaE6i29JAax6AXZ8rW538lE7Hl9N4y7w0P/URERR1BofrzB1Tp+Uo2qn5ms5vwt+GKpOINhxtJqMlK3LVebm4RAzR52N+MgyddkIB3d1tuFDv1/7HAFtYcB7ag2YEELUEkmOapEkR9fPYFB46KutbDudRptG7vz8RDSO2tpbibleyU2Fnx9VZ0yOHAi/j1OTkqQDELfBtKzOTZ09+Z+56rwvvhGXmqfKdJ0IfV5SOyz/+w00j1E7OaedUmuXOj1+5VhOrIafhqt9bMpr95DamXn9W2qH43YPmO5POa72FXLygpRjEL+1dKLFPHW/W/Cl1cS7ToDes6TTshCi1klyVIskObp+S3ae47nFe3G0t2XVpO6EeDlZOiTrUJQH392tricFgAaTJiutK9z7JaybA9kX4OGfodHN6tparoHqsPW002oStO1z9T22OnWRyspqcBw84JndlXdO3vsj/DFe7QTduIu63pS+CGLeUJuwajKiMG4jJB1UJ+87sgxc/NVOz0IIUQckOapFkhxdn4JiPb3fWUdCZgHT7ohkXM9qrLzdkOlL4Kdh6oKZ5dlq1aTEVgvDlkB4D3VZCX3xlVfgBrUZbf5AOLNRfa2xVROpczvUuXfQXFrpvfcL6qissoRny6fqLMSgTpo46JNLK6lXZ6V0IYSwItX5/paphEWd+n7rGRIyCwh0d+Cxrk0sHY755GeoQ9prQlHg/yapiZGdAwxdqA43bx4Do1aoHZOHfqcmRqAul3C1xAjUROf2V1DX5rKDIfPgsb/gwf+pkxneOfdS2TWvqaOxFEV9XpYY3TpeXeXcTqsmRZIYCSFuEFJzVE1Sc1Rz2QXFdJ+7lvS8Yt68tw0PdqricgzWLj0Ovuqtzrczfvu1E5fLrXsL1r2hjtgauhBaDjRfbOf+VePyi6y4L3aW2sEa1Ga55v1g1wL1de8X1dFjMiGnEKKBkJojYZW+2nCa9Lxiwn2dub9DsKXDMQ99MSwZDXmpkHFGnTHZYIBT69U+RHGbSpe/yKv8/ft+VhMjgDvfNm9iBBDcsfLECNSapZlJai1V9oXSxEijLqnR/TlJjIQQNywZIiLqRGJmAV9vUNebmtIvonqLwxblqYmHX8taiq6a8tLUDsVthqgjts7/e2nfti/U1eC3fQ5N+8DZbeqEhg5u6szN5cVvVTs9A3R5Bm75T91dQxl7B7jjTfjfg+oirPd9pc6TJIQQNzBpVqsmaVarPkVRGDlvB+uPXaR9iAe/Pdmleuun/e8hOLochv8B4T1rLc4q0ZfAvP5wbjs0uU0dgYWiztOzYrraeboyzn4wcf+lJrfUk/B1X3XV9siBanOajQUrck+uUWfZ9o+yXAxCCFGLpFlNWJWdZ9JZf+wiWjsb3hnStmJiFPsSfHtH6WzLl0nYoyZGAN8NUlddN+grlqsr6+aoiRGUzjukQIeRcMtodQHSMrZa9aezL7gGqTM+l81BlJ+uTqyYn6Yud3Hvl5ZNjECdrVoSIyGEACQ5EnVg0bZ4AAa3D6KZ32WLcWbEw6YPIH4L7PhKXZk9/Yxaq7JrIWz+r2n5HV/D6X/qJnBQa4r+mgYb3oPTG2DDu+p2rYv607elOjs0QPR4NSkKuRWGL1XXF3tgEdw6Tt2/5WO1j9LPIyD1hDoZ4kM/qkt/CCGEsBrS50jUqnPpefzfvgsAPNw5tGKB3d9fev73K7DtS7UW49wOdULD/LSK7zmxGpr2Mk+AaadgwSBo3lftiHy5LR9fmlDR2RdQ1Fmhm98O27+CAe+qK9CDurbWs4fVmavttDDiT3W7XySsnwsXj8DHHdXRbfbO8PBP4BpgnusQQghhNlJzJGrVe7HHKNIbiA73pl2wu+nOojzY8Y3ptpxE2PuD+jwvRZ31uWkf007CJ/42T3CKojbTZcarq9tnJ5ruj98Ga9+49Dr3orrYaP+3IGoQjFymLtlRnrOPmhiV5+AOHUaoz9PjAI3a8TmgtXmuQwghhFlJciRqTUpOIUv3JAAwvX9kxb5Gu79XEyD7aywf0nUC3PctTDqkzgV08bC6ZEZ1nFwLX/aEczsvbTv4m9oRGdQkbP9iOLIcvr5dTYx+Hg76QtRlPCgdzfV1zZrBOo9V3w/qkP3IAdU/hhBCiDohzWqi1vyxJ4ESg0K7EA/ahXiY7lQU2P6F+vz2V9QalS0fX9rvFa42eQXdpPbd0WjAvRE06qh2iP6wPQz6GNo9eO1AinJh4WD1+Q9D1PXBIu6EFaUzQfu1guSD8O+36sKvhZmw4C41MfJuBmPWqft8I9V4asIjRK1p0hfLemJCCGHlJDkStWbJTrV2p9IJH+M2qp2StS5qgqNzhY6j4ONbwNETRi5XZ2/u+JjpZITN+qrJkaEYfnsCWt1bsRnrcpvKderOS1XfF9BGbcLzDINHf4UP2qjJWBl9ofrzrv+qsXWdUMO7UE7jW6//GEIIIWqdNKuJWnEwIZPDF7LQ2tpwV9tAdWN2InzZS01Wds5Tt7UZoiYfoK7d9dhyGLUS3AKh/5sV+/Q062v6umxh1SvJuWhaI1Umcb/6s99raqfoFneor23swT1Efd7hMWjStWoXLIQQosGQ5EiYXVpuES/8fgCA26P88XAqrdn5+1VI2AWxL8Lh0pFcHUaavrnxreDT7MoHb3Qz9Hz+0utjK68ezD9vqzNUB90ED/6griFWpsltl/r+dJ2gTtQY87partdMNXESQghxw5FmNWF2z/68h93xGdjaaBjZtYm6UVEgrtz8RPoiCGwPQe2rd3CNBnpOA/9W8NMwOPCrWrvU6h61Oa68tNNqXyGAvrPV2bUjB8B3g9VlPWLeuNRkF9wRphy/9N7AttWLSwghRIMhyZEwq80nUlh39CJ2Nhq+/09nbmnipe5I3KdO+Fhe2fD2mgjvqU64mJsMyyapNUhRg6HtULCxVcusm6P2TQrvZbrsyMM/qZ20nbxqfn4hhBANljSrieujKLD1Mzj4OwALt54B4KFOjbk13PtSuX0/m77P3gla31/z8+pcwKdcf6RjK+D3sbDrO/V10qFL5+w72/S9djpJjIQQQlyRJEfi+iTsUhdc/e0JcvLyWXMkGYAHbgm5VMagh/1L1Oe3PQcaW3VJDYfrXLi385iK27Z/pf5c/yagqLVJ1W26E0IIcUOTZjVxfY6vVn+WFLB9+2YKSwyE+TjTKqhc4nP6H3XYvKMn9JimPmztr//c7R8BJx/YtUCtOQJ1iY7TG+DQH4BGPZcQQghRDVJzJK7PidXGp3H7twBwV9tA09mwy5q3Wt2jzklkpzWdu6imbGwg8k4Y9CkMeA+8m4OiV2e2Bmg1WFaaF0IIUW2SHImay0uD8/8aX9omq3MH3dUu6FKZojw4vFR93vaB2onD2RtuGa12xobSxWql1kgIIUTNSHIkau7UOnVNslKRmjgi/F1p7u96qcyxv9R5hjwaQ0jn2o2n5V2Xnre6B/xa1u75hBBCNEiSHImaO/G3+rNpbwCiNGcYG3oeCrMvlSlrUmv7gHma0q7GNxKCbwF7Z+g5vXbPJYQQosGS5EjUjMFg7G+U1e5xChV7XDX53LNvLPxVmpgUZF1KoNoMqf2YNBoY/gdM2FNx2REhhBCiimS0mqiZC3vUEWhaF5blNKeVEkI7TenCrXu+h4O/wU2PqJMwejeru2RF66w+hBBCiBpqUDVHTZo0QaPRmDzefPNNkzL79u3jtttuw8HBgZCQEObOnWuhaOu5o3+pP5v14Y/9KZxQgkz3F+fC9i/U52WLugohhBD1QIOrOXrllVd4/PHHja9dXS91Ds7KyqJfv3707duXzz//nP379zNq1Cg8PDwYM6aSCQXFlZUmR1mht7N9dxq32XpfuWxE/zoKSgghhLh+DS45cnV1JSAgoNJ9ixYtoqioiG+//RatVkurVq3Ys2cP7733niRH1ZERD0n7QWNDbHE7FOUsm/0f5imvfDi+yrSsgweE3GqRMIUQQoiaaFDNagBvvvkm3t7e3HTTTbz99tuUlJQY923ZsoXu3buj1WqN22JiYjh69Cjp6emVHq+wsJCsrCyTxw3vaOls1I2jWX6iEICurZvBsMUwZL5p2eb9wLbB5eBCCCEasAaVHD3zzDP8+OOPrF27lieeeII33niDqVOnGvcnJibi7+9v8p6y14mJiZUec86cObi7uxsfISEhlZa7oRxTm9SKmsWw8UQKAH1blt7XVvfAjPNw8wj1dW1N/CiEEELUEqtPjqZPn16hk/XljyNHjgDw7LPP0rNnT9q2bcvYsWN59913+eijjygsLKzx+WfMmEFmZqbxcfbsWXNdWv1UUgTxWwHYYduewhIDIV6OtPB3uVRG5wJ3vAlP/QvN+1ooUCGEEKJmrL69Y/LkyYwcOfKqZcLDwyvd3rlzZ0pKSoiLiyMiIoKAgACSkpJMypS9vlI/JZ1Oh06nq37gDVXCLijOAydv/jjnBuTQt6W/6VpqAFon8GlukRCFEEKI62H1yZGvry++vr41eu+ePXuwsbHBz88PgOjoaGbOnElxcTH29uqq8LGxsURERODp6Wm2mBu0uA0AKKHd+Puo2qR2e0v/q71DCCGEqFesvlmtqrZs2cIHH3zA3r17OXXqFIsWLWLSpEk88sgjxsTn4YcfRqvVMnr0aA4ePMhPP/3Ehx9+yLPPPmvh6OuRuI0AJHh2JDW3CBedHbeEeVk4KCGEEMJ8rL7mqKp0Oh0//vgjs2fPprCwkLCwMCZNmmSS+Li7u7Nq1SrGjx9Phw4d8PHxYdasWTKMv6pKCiF+GwDriyKBYqKbemNv22BybCGEEKLhJEc333wzW7duvWa5tm3bsmHDhjqIqAE6vwtK8sHZl9/PuQDpdG9RsyZPIYQQwlrJf/lF1ZX2NyoO6cKu+AwAejSX5EgIIUTDIsmRuLasC1CUZ0yOjju1p8Sg0MTbicbeThYOTgghhDCvBtOsJmpJ8hH4vCs06wtntwMQm6cO0ZcmNSGEEA2R1ByJS1JPwob31I7XZbZ+CoYSOLYCSgpQnP348ZQjAD0kORJCCNEASc2RUOlL4KOb1eeOHtBxlPo833TNuQy/Tlw4XIiz1pauzXzqNkYhhBCiDkjNkVDt/d+l5+f+vfQ8cZ9Jsa1KKwB6RfrhYG9bF5EJIYQQdUqSI6E6vvLS85zSJVZyLkJ6nEmx3zPCALg9SmbFFkII0TBJciRUSYcuPU85rv789xuTIgZnP2KTXAHoJLNiCyGEaKAkORLqMP20U5deZ8RDQSZs+1x93e1ZsLHjbON7MCgaGns5EejuaJlYhRBCiFomyZGAi0cABZx8QOcOKOh3fa92xnYJgN4vwPMJ/Og2EoBbmkitkRBCiIZLkiMByYfVn/5R4NNM3bT6vwCUhPcCG1uw07HxRBoAXZp6WyRMIYQQoi5IciQgubS/kV8UeKsTPAYaEgE463krAKk5hRxIyATgtuYyhF8IIUTDJcmRgKSD6k+/SzVHZbbThhK9gY0nUlAUiAxwxc/NwQJBCiGEEHVDJoEUl2qO/FuhZLqjKd182uDPtJWJ/HlyBy469VelV6SfZWIUQggh6ogkRze63NRL8xr5RpKQA41Kd+1W1Ca2jSdSjMUHtAms4wCFEEKIuiXNaje65NImNc8moHNhe5aHcdcZg+lEj2E+zrQKcqu72IQQQggLkOToRnd4mfrTvzUAO87nc1FxByDW0MGk6O1R/mg0GoQQQoiGTJKjG1nmedg5T33e6XEAtp9O4+7C19jc91feeHIYt4ZfmtOomyw0K4QQ4gYgydGN7NQ60BdBow4Q3pOkrAJOJOeQqPGm1c3daR/iYZIQyZIhQgghbgSSHN3ILpZO/tioIwCbSjtet23kjruTPQD3dQjG08mehzo1xsHe1iJhCiGEEHVJRqvdyC4eVX/6RgCw6UQqAF3L1RYFujuye1Y/FEWp8/CEEEIIS5CaoxtZ8hH1p19LAHafTQfglkqaz6QjthBCiBuFJEc3qsIcyIxXn/tGkl1QzKmLuYDarCaEEELcqCQ5ulGllDapOfuBkxf7z6vrpjXycMTbRWfBwIQQQgjLkuToRmVsUosEYP85NTlqFyK1RkIIIW5skhzdqMpGqvmq/Y12xKn9jdoGe1goICGEEMI6SHJ0oyobqeYXSYnewNZTpSPVmspEj0IIIW5skhzdqMqa1Xwj2Xsuk5zCEjyc7ImStdOEEELc4Go8z9G///7Lzz//THx8PEVFRSb7fv311+sOTNSiy0aqbd2m1hpFh3tjayND9oUQQtzYalRz9OOPP9KlSxcOHz7Mb7/9RnFxMQcPHmTNmjW4u0uHXquXflr96eQNTl4cupAFQLsQD8vFJIQQQliJGiVHb7zxBu+//z5//vknWq2WDz/8kCNHjjB06FAaN25s7hiFuaWfUX96hAJwLDEbgIgAV0tFJIQQQliNGiVHJ0+eZMCAAQBotVpyc3PRaDRMmjSJL7/80qwBilqQUZoceYZSWKLnVIo6+WOkJEdCCCFEzZIjT09PsrPV2oZGjRpx4MABADIyMsjLyzNfdKJ2lKs5OnUxF71Bwc3BjgA3B8vGJYQQQliBGnXI7t69O7GxsbRp04YhQ4YwYcIE1qxZQ2xsLH369DF3jMLcytUcHS7tbxQR4CrrpwkhhBDUMDn6+OOPKSgoAGDmzJnY29uzefNm7rvvPl544QWzBihqQXqc+tMjlG170gBoL52xhRBCCKCGyZGX16VV221sbJg+fbrZAhK1TFEgQx3Gr3iEsvGE+rxLM5n8UQghhIAa9jnatWsX+/fvN77+448/GDx4MM8//3yFOY+ElclOhOI80NgSb/DmfEY+9rYaOjXxuvZ7hRBCiBtAjZKjJ554gmPHjgFw6tQpHnjgAZycnFi8eDFTp041a4DCzFLUzw3PJuw4q45SaxfsgbOuxvOBCiGEEA1KjZKjY8eO0b59ewAWL15Mjx49+OGHH5g/fz6//PKLOeOrtv/7v/+jc+fOODo64unpyeDBg032x8fHM2DAAJycnPDz82PKlCmUlJRYJlhLSD2u/vRpzoHzmYAsNiuEEEKUV6PqAkVRMBgMAKxevZqBAwcCEBISQkpKivmiq6ZffvmFxx9/nDfeeIPevXtTUlJinGYAQK/XM2DAAAICAti8eTMXLlxg+PDh2Nvb88Ybb1gs7jqVUi45OqkmR22CZT01IYQQokyNkqOOHTvy2muv0bdvX9avX89nn30GwOnTp/H39zdrgFVVUlLChAkTePvttxk9erRxe1RUlPH5qlWrOHToEKtXr8bf35/27dvz6quvMm3aNGbPno1Wq7VE6HWrNDkyeDXn4AZ1GH+bRrLkixBCCFGmRs1qH3zwAbt27eKpp55i5syZNGvWDIAlS5bQpUsXswZYVbt27eL8+fPY2Nhw0003ERgYSP/+/U1qjrZs2UKbNm1MEriYmBiysrI4ePBgpcctLCwkKyvL5FEv6YvVn6XJUZwmiPxiPU5aW8J8XCwYmBBCCGFdalRz1LZtW5PRamXefvttbG1trzuomjh16hQAs2fP5r333qNJkya8++679OzZk2PHjuHl5UViYmKFmq2y14mJiZUed86cObz88su1G3xt2/YlrHoBhn6HknkWDfDNYTugiJ4RvtjayOSPQgghRJka1RyVKSoq4ty5c8THxxMfH09ycjIXLlwwV2wATJ8+HY1Gc9XHkSNHjH2gZs6cyX333UeHDh2YN28eGo2GxYsX1/j8M2bMIDMz0/g4e/asuS6t7hz8FfSFGLZ/iQaFdMWFRQfUkWpP9mxm4eCEEEII61KjmqNjx44xevRoNm/ebLJdURQ0Gg16vd4swQFMnjyZkSNHXrVMeHi4MSkr38dIp9MRHh5OfLw60WFAQADbt283eW9SUpJxX2V0Oh06na6m4VuevgQu7AXA5uTfAJxSAgENIV6OtJb+RkIIIYSJGiVHjz32GHZ2dixbtozAwMBaXZPL19cXX1/fa5br0KEDOp2Oo0eP0q1bNwCKi4uJi4sjNDQUgOjoaF5//XWSk5Px8/MDIDY2Fjc3N5OkqkFJOapO+ljOSUMQAOHS10gIIYSooEbJ0Z49e9i5cyeRkZHmjqfG3NzcGDt2LC+99BIhISGEhoby9ttvAzBkyBAA+vXrR1RUFI8++ihz584lMTGRF154gfHjx9fv2qGrSdhdYZNacwRhPs51HY0QQghh9WqUHEVFRVl0PqMrefvtt7Gzs+PRRx8lPz+fzp07s2bNGjw9PQGwtbVl2bJljBs3jujoaJydnRkxYgSvvPKKhSOvRed3VdhUlhyF+0pyJIQQQlxOoyiKUt03rVmzhhdeeIE33niDNm3aYG9vb7Lfza3hTiqYlZWFu7s7mZmZ9eM6v+xZofaofcEXZODKwtGduK35tZsshRBCiPquOt/fNao56tu3LwB9+vQx2V4bHbLFdSgphMQDJpuycSIDVwBCvaTmSAghhLhcjZKjtWvXmjsOURuSDoKhGBw92epxJzcl/MSC8LcZ59+UvMISQrwcLR2hEEIIYXVqlBz16NHD3HGI2lA6hJ/A9ryZ9TBHC/vwauvO3N8h2LJxCSGEEFasxpNAbtiwgUceeYQuXbpw/vx5ABYuXMjGjRvNFpy4TlkJABS7N2F/Qhb5ONA5zMvCQQkhhBDWrUbJ0S+//EJMTAyOjo7s2rWLwsJCADIzM2+c1e3rg9xkAM6XuKI3KDTycCTEy8nCQQkhhBDWrUbJ0Wuvvcbnn3/OV199ZTJSrWvXruzaVXHouLCM9OTSGr396iSQUmskhBBCXFuNkqOjR4/SvXv3Ctvd3d3JyMi43piEmZw7d0b9WaTOhN2+sYcFoxFCCCHqhxolRwEBAZw4caLC9o0bNxIeHn7dQYmaKyzRc/9nm3n+t/246TMASFHU9dOkSU0IIYS4tholR48//jgTJkxg27ZtaDQaEhISWLRoEc899xzjxo0zd4yiGjYeT+HfM+n8sC0eH00mACmUJkeeMnRfCCGEuJYaDeWfPn06BoOBPn36kJeXR/fu3dHpdDz33HM8/fTT5o5RVENabhEAjhTgrFE7ypfVHAV7Ss2REEIIcS01So40Gg0zZ85kypQpnDhxgpycHKKionBxkVXeLe1sej6AsdYoX9GSiwMADva2FotLCCGEqC9qlByV0Wq1uLq64urqKomRlTiTmguAL6VNaoo7oLFgREIIIUT9UqM+RyUlJbz44ou4u7vTpEkTmjRpgru7Oy+88ALFxcXmjlFUQ1yKmhxd3t9ICCGEEFVTo5qjp59+ml9//ZW5c+cSHR0NwJYtW5g9ezapqal89tlnZg1SVF1cqjqnkZ8mA4ASZ38ogkl9W1gwKiGEEKL+qFFy9MMPP/Djjz/Sv39/47a2bdsSEhLCQw89JMmRhVzMLiQzX625C9SkAtAyIpJV0d1p6ivNnkIIIURV1Cg50ul0NGnSpML2sLAwtFrt9cYkaujA+Uzj80BNGgDOPo1p4e9qqZCEEEKIeqdGfY6eeuopXn31VeOaagCFhYW8/vrrPPXUU2YLTlTPvnNqctQu2J1A1ORI4x5syZCEEEKIeqdGNUe7d+/m77//Jjg4mHbt2gGwd+9eioqK6NOnD/fee6+x7K+//mqeSMU17T+fAcDgmxrRcXs+ZAJuQRaNSQghhKhvapQceXh4cN9995lsCwkJMUtAoubKao7aBLlhn3tB3SjJkRBCCFEtNUqO5s2bZ+44xHVKzy0iOVtt5oz0KIGSAnWHa6AFoxJCCCHqnxr1ORLW58TFHAAaeTjiUpCkbnT2BTudBaMSQggh6p8a1RylpqYya9Ys1q5dS3JyMgaDwWR/WlqaWYITVXc8SU2Omvm5QPppdaO7NHUKIYQQ1VWj5OjRRx/lxIkTjB49Gn9/fzQaWZ7C0o4nZwPQwt8FUk+oG32aWzAiIYQQon6qUXK0YcMGNm7caBypJizvRLJac9TczxXOlSZH3pIcCSGEENVVoz5HkZGR5OfnmzsWcR1Ol66pFu7rDKnH1Y0+zSwYkRBCCFE/1Sg5+vTTT5k5cybr168nNTWVrKwsk4eoW4qiGEeq+bs5XGpW85bkSAghhKiuGs9zlJWVRe/evU22K4qCRqNBr9ebJThRNVkFJRSVqJ3ifW1zID9d3eHV1IJRCSGEEPVTjZKjYcOGYW9vzw8//CAdsq3AxWx1TqP3Hb7E4aNR6kaXANA6WTAqIYQQon6qUXJ04MABdu/eTUREhLnjETWQnF2IPSXcwzooKd3oJpM/CiGEEDVRoz5HHTt25OzZs+aORdTQxexC/DXpphtlZmwhhBCiRmpUc/T0008zYcIEpkyZQps2bbC3tzfZ37ZtW7MEJ6rmYnYhgaSabnTxt0wwQgghRD1Xo+TogQceAGDUqFHGbRqNRjpkW8jF7EKCNCmmG6XmSAghhKiRGiVHp0+fNncc4jqoydFlS7a4BlgmGCGEEKKeq1FyFBoaau44RA2V6A0cTcqmveayZjWpORJCCCFqpEbJEcDJkyf54IMPOHz4MABRUVFMmDCBpk1lbp269NWG0xxMyCJYe3lyJH2OhBBCiJqo0Wi1lStXEhUVxfbt22nbti1t27Zl27ZttGrVitjYWHPHKK7i3zi1Oa2tW47pDhdpVhNCCCFqokY1R9OnT2fSpEm8+eabFbZPmzaN22+/3SzBiWvLKigGwKWkbCi/BoLag7OvxWISQggh6rMa1RwdPnyY0aNHV9g+atQoDh06dN1B1cS6devQaDSVPnbs2GEst2/fPm677TYcHBwICQlh7ty5FonXXLLySwAFbWFpcjRxP/xnDdjU6KMVQgghbng1+gb19fVlz549Fbbv2bMHPz+/642pRrp06cKFCxdMHv/5z38ICwujY8eOAGRlZdGvXz9CQ0PZuXMnb7/9NrNnz+bLL7+0SMzmkFVQjCv52CilU2M7+0hiJIQQQlyHGjWrPf7444wZM4ZTp07RpUsXADZt2sRbb73Fs88+a9YAq0qr1RIQcKmfTXFxMX/88QdPP/20ce23RYsWUVRUxLfffotWq6VVq1bs2bOH9957jzFjxlgk7uuVlV+MtyZbfWHvBPaOlg1ICCGEqOdqlBy9+OKLuLq68u677zJjxgwAgoKCmD17Ns8884xZA6yppUuXkpqaymOPPWbctmXLFrp3745WqzVui4mJ4a233iI9PR1PT88KxyksLKSwsND4Oisrq3YDr4YSvYHcIj3NypIjJ2/LBiSEEEI0ADVqf9FoNEyaNIlz586RmZlJZmYm586dY8KECcZaGkv75ptviImJITg42LgtMTERf3/TIe5lrxMTEys9zpw5c3B3dzc+QkJCai/oasouUJvSPI3JkZcFoxFCCCEahholR6dPn+b48eMAuLq64urqCsDx48eJi4szW3CgjoC7UkfrsseRI0dM3nPu3DlWrlxZaafx6poxY4YxAczMzLSqBXezs9KJsdlOmF1pZ2ypORJCCCGuW42a1UaOHMmoUaNo3ry5yfZt27bx9ddfs27dOnPEBsDkyZMZOXLkVcuEh4ebvJ43bx7e3t7cfffdJtsDAgJISkoy2Vb2unx/pfJ0Oh06na6aUdcNt9jJfKH989IGSY6EEEKI61aj5Gj37t107dq1wvZbb72Vp5566rqDKs/X1xdf36rP2aMoCvPmzWP48OHY29ub7IuOjmbmzJkUFxcb98XGxhIREVFpfyNr53HqT9MNkhwJIYQQ163GfY6ys7MrbM/MzESv1193UNdjzZo1nD59mv/85z8V9j388MNotVpGjx7NwYMH+emnn/jwww8tNsLO7Bylz5EQQghxvWqUHHXv3p05c+aYJEJ6vZ45c+bQrVs3swVXE9988w1dunQhMjKywj53d3dWrVrF6dOn6dChA5MnT2bWrFn1dhh/BdIhWwghhLhuNWpWe+utt+jevTsRERHcdtttAGzYsIGsrCzWrFlj1gCr64cffrjq/rZt27Jhw4Y6iqaOSbOaEEIIcd1qVHMUFRXFvn37GDp0KMnJyWRnZzN8+HCOHDlC69atzR2jqMSmEykVN/q0qPtAhBBCiAamRjVHoE76+MYbb5gzFlENO08kUKFLvCRHQgghxHWr8SJcGzZs4JFHHqFLly6cP38egIULF7Jx40azBSeuzJCXVnGjnbbiNiGEEEJUS42So19++YWYmBgcHR3ZtWuXcXmNzMxMqU2qK7mppq+1rpaJQwghhGhgapQcvfbaa3z++ed89dVXJnMJde3alV27dpktOHFlNgWX1RxF3V15QSGEEEJUS436HB09epTu3btX2O7u7k5GRsb1xiSqwLY0OSrQeuHQaQR0m2ThiIQQQoiGoUY1RwEBAZw4caLC9o0bN1ZYykPUDm2hup5aht8t0Hc2OLhbNiAhhBCigahRcvT4448zYcIEtm3bhkajISEhgUWLFjF58mTGjRtn7hhFJbTFmeoTh/q37IkQQghhzWrUrDZ9+nQMBgN9+vQhLy+P7t27o9PpmDJlSqXLdgjz05aoy7fYOHlYNhAhhBCiganx2mozZ84kLS2NAwcOsHXrVi5evIi7uzthYWHmjlFUQqfPAcDOUZrThBBCCHOqVnJUWFjIjBkz6NixI127dmX58uVERUVx8OBBIiIi+PDDD5k0SToG1zZFUXDU5wJg7+xh2WCEEEKIBqZazWqzZs3iiy++oG/fvmzevJkhQ4bw2GOPsXXrVt59912GDBmCra1tbcUqSuUV6XEhDwCdiyw2K4QQQphTtZKjxYsX891333H33Xdz4MAB2rZtS0lJCXv37kWj0dRWjOIyOYUluGnU5MjeWZrVhBBCCHOqVrPauXPn6NChAwCtW7dGp9MxadIkSYzqWHZBCa6lNUcaGcIvhBBCmFW1kiO9Xo9We2n9Ljs7O1xcXMwelLi6nMISXEtrjtC5WTYYIYQQooGpVrOaoiiMHDkSnU4HQEFBAWPHjsXZ2dmk3K+//mq+CEUFOQUluJXWHMnkj0IIIYR5VSs5GjFihMnrRx55xKzBiKrJy8tBpylRXzhIzZEQQghhTtVKjubNm1dbcYhqyM1S11UzoMFG62rhaIQQQoiGpUaTQArLik9IBKDI1hls5CMUQgghzEm+WeuhcxfU5MggtUZCCCGE2UlyVM8U6w1cTL0IgJ2sqyaEEEKYnSRH9cyRC9mydIgQQghRiyQ5qme+3XTaODu2TAAphBBCmJ8kR/VIYmYBv+85b5wdWyaAFEIIIcxPkqN65Gx6HooCIU7F6gapORJCCCHMrlrzHAnL+e/fx1m88ywA3rb5UIxMACmEEELUAkmO6gG9QeG92GPG1+42+eoTaVYTQgghzE6a1eqBnIISk9dumtLkSJrVhBBCCLOT5KgeyCooNnntijqUX5rVhBBCCPOT5KgeyL6s5shZKRutJjVHQgghhLlJclQPZF9Wc+RoKKs5kuRICCGEMDdJjuqBnELTmiOdPkd9Is1qQgghhNlJclQPmDarKdiXlCZHMlpNCCGEMDtJjuqB8s1qThRio+jVF9KsJoQQQpidJEf1QFa5miPj0iE2dmDvaKGIhBBCiIZLJoGsB3IKS9Bg4Efta3iTpW7UuYFGY9nAhBBCiAZIkqN6ILugmMaaZDrbHLm0UTpjCyGEELVCmtXqgcvnOQLAwaPO4xBCCCFuBA0qOTp27BiDBg3Cx8cHNzc3unXrxtq1a03KxMfHM2DAAJycnPDz82PKlCmUlFSSfFiR7IISdJjOdYRbkGWCEUIIIRq4BpUcDRw4kJKSEtasWcPOnTtp164dAwcOJDExEQC9Xs+AAQMoKipi8+bNLFiwgPnz5zNr1iwLR351OZUmR40sE4wQQgjRwDWY5CglJYXjx48zffp02rZtS/PmzXnzzTfJy8vjwIEDAKxatYpDhw7x/fff0759e/r378+rr77KJ598QlFRkYWv4MqyCorRcVl87pIcCSGEELWhwSRH3t7eRERE8N1335Gbm0tJSQlffPEFfn5+dOjQAYAtW7bQpk0b/P39je+LiYkhKyuLgwcPVnrcwsJCsrKyTB51LbugBAfN5TVHwXUehxBCCHEjaDCj1TQaDatXr2bw4MG4urpiY2ODn58fK1aswNPTE4DExESTxAgwvi5rervcnDlzePnll2s3+GvIL9ZLzZEQQghRR6y+5mj69OloNJqrPo4cOYKiKIwfPx4/Pz82bNjA9u3bGTx4MHfddRcXLlyo8flnzJhBZmam8XH27FkzXl3V5BfpcZA+R0IIIUSdsPqao8mTJzNy5MirlgkPD2fNmjUsW7aM9PR03NzUOYA+/fRTYmNjWbBgAdOnTycgIIDt27ebvDcpKQmAgICASo+t0+nQ6XTXfyE1ZDAoas2RzWU1RzJaTQghhKgVVp8c+fr64uvre81yeXnqsho2NqaVYTY2NhgMBgCio6N5/fXXSU5Oxs/PD4DY2Fjc3NyIiooyc+TmUViixl6hz5GtvQWiEUIIIRo+q29Wq6ro6Gg8PT0ZMWIEe/fu5dixY0yZMoXTp08zYMAAAPr160dUVBSPPvooe/fuZeXKlbzwwguMHz/eorVDV5NfrC4ya+xzpHWFcVssGJEQQgjRsDWY5MjHx4cVK1aQk5ND79696dixIxs3buSPP/6gXbt2ANja2rJs2TJsbW2Jjo7mkUceYfjw4bzyyisWjv7KypIjZ5vSiSqj7gZ/66zlEkIIIRoCq29Wq46OHTuycuXKq5YJDQ1l+fLldRTR9csvUpMjF9vS5MjOwYLRCCGEEA1fg6k5aqgKSmuOnCQ5EkIIIeqEJEdW7lKzWmmHbHtJjoQQQojaJMmRlcsrbVZz1EjNkRBCCFEXJDmycmV9jpzKao4kORJCCCFqVYPqkN0QFRTrmWv3BT0L1qsbJDkSQgghapXUHFm5/GI9Q+3WX9ogfY6EEEKIWiXJkZUra1YzkpojIYQQolZJcmTlykarGdlZ50zeQgghREMhyZGVKygqMd1g52iZQIQQQogbhCRHVq6osMB0g9QcCSGEELVKkiMrV1R0WXJkLzVHQgghRG2S5MjK6QvzTTdIzZEQQghRqyQ5snIll9ccSZ8jIYQQolZJcmTlSoovS45sbC0TiBBCCHGDkOTIWsVtgvkDCcw7Zrpd52aZeIQQQogbhCwfYq3m3wnAJDZc2jZsCbj4WiggIYQQ4sYgNUf1hN6jCTS/3dJhCCGEEA2eJEf1hK2sqSaEEELUCUmO6gtbraUjEEIIIW4IkhzVF7LgrBBCCFEnJDmqL2TyRyGEEKJOSHJkrS5vRpNmNSGEEKJOSHJkrS6fCVua1YQQQog6IcmRtbq8Gc1Oao6EEEKIuiDJkbXSF5m+tpU+R0IIIURdkOTIWpUUmr6WDtlCCCFEnZDkyBopCpTkm26T5EgIIYSoE5IcWaPLa41ARqsJIYQQdUSSI2t0ea0RyGg1IYQQoo5IcmSNigsqbpNmNSGEEKJOSHJkjSqrOZJmNSGEEKJOSHJkjSqtOZJmNSGEEKIuSHJkjSrtcyQ1R0IIIURdkOTIGpWOVstXyiVE0qwmhBBC1AlJjqxRsVpzdFbxvbStKNdCwQghhBA3FkmOrFGJ2ucol3KLz+alWSgYIYQQ4sYiyZE1Kq05KijfrOYZaqFghBBCiBtLg0qOdu3axe23346Hhwfe3t6MGTOGnJwckzLx8fEMGDAAJycn/Pz8mDJlCiUlJRaKuBIGvbHmqAB7Rti+CX1nQ5uhlo1LCCGEuEE0mOQoISGBvn370qxZM7Zt28aKFSs4ePAgI0eONJbR6/UMGDCAoqIiNm/ezIIFC5g/fz6zZs2yXODlpZ2GuWGw4nkACtBy3K4FdJsEtnYWDk4IIYS4MTSYb9xly5Zhb2/PJ598go2NmvN9/vnntG3blhMnTtCsWTNWrVrFoUOHWL16Nf7+/rRv355XX32VadOmMXv2bLRaC48I2/AuFGQaXxagxc62weSvQgghRL3QYL55CwsL0Wq1xsQIwNFR7dC8ceNGALZs2UKbNm3w9/c3lomJiSErK4uDBw9e8bhZWVkmj1pz2RIhBYoWe1tN7Z1PCCGEEBU0mOSod+/eJCYm8vbbb1NUVER6ejrTp08H4MKFCwAkJiaaJEaA8XViYmKlx50zZw7u7u7GR0hISO1dxGWzYBegxV5qjoQQQog6ZfXfvNOnT0ej0Vz1ceTIEVq1asWCBQt49913cXJyIiAggLCwMPz9/U1qk6prxowZZGZmGh9nz54149Vdxt7R5GUWTmjtrP4jEkIIIRoUq+9zNHnyZJNO1ZUJDw8H4OGHH+bhhx8mKSkJZ2dnNBoN7733nnF/QEAA27dvN3lvUlKScV9ldDodOp2u0n1md1nNUZbiJDVHQgghRB2z+uTI19cXX1/faxcsp6yp7Ntvv8XBwYHbb78dgOjoaF5//XWSk5Px8/MDIDY2Fjc3N6KioswbeE1UqDlylj5HQgghRB2z+uSoOj7++GO6dOmCi4sLsbGxTJkyhTfffBMPDw8A+vXrR1RUFI8++ihz584lMTGRF154gfHjx9dd7dDVaExriaTmSAghhKh7DSo52r59Oy+99BI5OTlERkbyxRdf8Oijjxr329rasmzZMsaNG0d0dDTOzs6MGDGCV155xYJRl6MvNnmZjROOkhwJIYQQdapBJUfffffdNcuEhoayfPnyOoimBgymyVGW4oSbJEdCCCFEnZJvXmti0Ju8zMIZexmtJoQQQtQp+ea1JvqKNUfSIVsIIYSoW5IcWROD6QK42TihlWY1IYQQok7JN681uazPkR5bNFJxJIQQQtQpSY6syWV9jgDSc4srKSiEEEKI2iLJkTXRV0yE0nKLLBCIEEIIceOS5MiaXNbnCCA1t9ACgQghhBA3LkmOrImhYs1RiUGxQCBCCCHEjUuSI2tSrs/R9OL/APDOkHaWikYIIYS4IUlyZE1K+xwt8niCH/W9ef+BdtzSxMvCQQkhhBA3FkmOrElpn6MixRYAOxv5eIQQQoi6Jt++1qQsOTKoH4u9TAAphBBC1Dn59rUmpc1qRUpZciQzQAohhBB1TZIja2KsOSptVpOaIyGEEKLOybevNSkdyl8oNUdCCCGExUhyZE1Kh/IXS58jIYQQwmLk29ealPY5KpDkSAghhLAY+fa1JqV9jsqa1exspFlNCCGEqGuSHFmT0j5HZUP5tXby8QghhBB1Tb59rUlpn6OyZjWpORJCCCHqniRH1qS0z1GhXk2KpM+REEIIUffk29ealPY5Kiid50iSIyGEEKLuybevNTFOAqnWHNnJPEdCCCFEnZPkyJqUJkcl2AFScySEEEJYgnz7WpPSPkclyAzZQgghhKVIcmRNDGXJkfQ5EkIIISxFvn2tSelQ/rLkSIbyCyGEEHVPkiNrUtaspthib6tBo5HkSAghhKhrkhxZE2OHbFvsbOSjEUIIISxBvoGthaKAcqlZTTpjCyGEEJYhyZG1KK01AijGVjpjCyGEEBYi38DWorS/EYAeW5kAUgghhLAQSY6sRbmaoxKpORJCCCEsRr6BrYUkR0IIIYRVkG9ga1EuOdJjIx2yhRBCCAuR5MhalPY5MmjsAI0M5RdCCCEsRL6BrUXp0iGKTemis3by0QghhBCWIN/A1qJ06RBFU7qumiwdIoQQQlhEvUmOXn/9dbp06YKTkxMeHh6VlomPj2fAgAE4OTnh5+fHlClTKCkpMSmzbt06br75ZnQ6Hc2aNWP+/Pm1H3xVmDSrIUP5hRBCCAupN8lRUVERQ4YMYdy4cZXu1+v1DBgwgKKiIjZv3syCBQuYP38+s2bNMpY5ffo0AwYMoFevXuzZs4eJEyfyn//8h5UrV9bVZVxZaYfssuRIRqsJIYQQlmFn6QCq6uWXXwa4Yk3PqlWrOHToEKtXr8bf35/27dvz6quvMm3aNGbPno1Wq+Xzzz8nLCyMd999F4CWLVuyceNG3n//fWJiYurqUipVVFyIFigpa1aT5EgIIYSwiAbzDbxlyxbatGmDv7+/cVtMTAxZWVkcPHjQWKZv374m74uJiWHLli1XPG5hYSFZWVkmj9pwKikTgPR8BQA76XMkhBBCWESDSY4SExNNEiPA+DoxMfGqZbKyssjPz6/0uHPmzMHd3d34CAkJqYXoQYNCnqKjUKPF1cGOO1oH1Mp5hBBCCHF1Fk2Opk+fjkajuerjyJEjlgyRGTNmkJmZaXycPXu2Vs4T0bEPTi8n0+zlQ+yfHcO9NwfXynmEEEIIcXUW7XM0efJkRo4cedUy4eHhVTpWQEAA27dvN9mWlJRk3Ff2s2xb+TJubm44OjpWelydTodOp6tSDEIIIYSo/yyaHPn6+uLr62uWY0VHR/P666+TnJyMn58fALGxsbi5uREVFWUss3z5cpP3xcbGEh0dbZYYhBBCCFH/1Zs+R/Hx8ezZs4f4+Hj0ej179uxhz5495OTkANCvXz+ioqJ49NFH2bt3LytXruSFF15g/PjxxpqfsWPHcurUKaZOncqRI0f49NNP+fnnn5k0aZIlL00IIYQQVkSjKIpi6SCqYuTIkSxYsKDC9rVr19KzZ08Azpw5w7hx41i3bh3Ozs6MGDGCN998Ezu7SxVk69atY9KkSRw6dIjg4GBefPHFazbtlZeVlYW7uzuZmZm4ubld72UJIYQQog5U5/u73iRH1kKSIyGEEKL+qc73d71pVhNCCCGEqAuSHAkhhBBClCPJkRBCCCFEOZIcCSGEEEKUI8mREEIIIUQ5khwJIYQQQpQjyZEQQgghRDmSHAkhhBBClCPJkRBCCCFEORZdeLY+KptQPCsry8KRCCGEEKKqyr63q7IwiCRH1ZSdnQ1ASEiIhSMRQgghRHVlZ2fj7u5+1TKytlo1GQwGEhIScHV1RaPRmPXYWVlZhISEcPbsWVm3rRbJfa47cq/rhtznuiH3ue7Uxr1WFIXs7GyCgoKwsbl6ryKpOaomGxsbgoODa/Ucbm5u8odXB+Q+1x2513VD7nPdkPtcd8x9r69VY1RGOmQLIYQQQpQjyZEQQgghRDmSHFkRnU7HSy+9hE6ns3QoDZrc57oj97puyH2uG3Kf646l77V0yBZCCCGEKEdqjoQQQgghypHkSAghhBCiHEmOhBBCCCHKkeRICCGEEKIcSY6sxCeffEKTJk1wcHCgc+fObN++3dIh1Tv//PMPd911F0FBQWg0Gn7//XeT/YqiMGvWLAIDA3F0dKRv374cP37cpExaWhrDhg3Dzc0NDw8PRo8eTU5OTh1ehXWbM2cOt9xyC66urvj5+TF48GCOHj1qUqagoIDx48fj7e2Ni4sL9913H0lJSSZl4uPjGTBgAE5OTvj5+TFlyhRKSkrq8lKs3meffUbbtm2Nk+BFR0fz119/GffLfa4db775JhqNhokTJxq3yb02j9mzZ6PRaEwekZGRxv3WdJ8lObICP/30E88++ywvvfQSu3btol27dsTExJCcnGzp0OqV3Nxc2rVrxyeffFLp/rlz5/Lf//6Xzz//nG3btuHs7ExMTAwFBQXGMsOGDePgwYPExsaybNky/vnnH8aMGVNXl2D11q9fz/jx49m6dSuxsbEUFxfTr18/cnNzjWUmTZrEn3/+yeLFi1m/fj0JCQnce++9xv16vZ4BAwZQVFTE5s2bWbBgAfPnz2fWrFmWuCSrFRwczJtvvsnOnTv5999/6d27N4MGDeLgwYOA3OfasGPHDr744gvatm1rsl3utfm0atWKCxcuGB8bN2407rOq+6wIi+vUqZMyfvx442u9Xq8EBQUpc+bMsWBU9Rug/Pbbb8bXBoNBCQgIUN5++23jtoyMDEWn0yn/+9//FEVRlEOHDimAsmPHDmOZv/76S9FoNMr58+frLPb6JDk5WQGU9evXK4qi3lN7e3tl8eLFxjKHDx9WAGXLli2KoijK8uXLFRsbGyUxMdFY5rPPPlPc3NyUwsLCur2AesbT01P5+uuv5T7XguzsbKV58+ZKbGys0qNHD2XChAmKosjvtDm99NJLSrt27SrdZ233WWqOLKyoqIidO3fSt29f4zYbGxv69u3Lli1bLBhZw3L69GkSExNN7rO7uzudO3c23uctW7bg4eFBx44djWX69u2LjY0N27Ztq/OY64PMzEwAvLy8ANi5cyfFxcUm9zkyMpLGjRub3Oc2bdrg7+9vLBMTE0NWVpaxVkSY0uv1/Pjjj+Tm5hIdHS33uRaMHz+eAQMGmNxTkN9pczt+/DhBQUGEh4czbNgw4uPjAeu7z7LwrIWlpKSg1+tNPmwAf39/jhw5YqGoGp7ExESASu9z2b7ExET8/PxM9tvZ2eHl5WUsIy4xGAxMnDiRrl270rp1a0C9h1qtFg8PD5Oyl9/nyj6Hsn3ikv379xMdHU1BQQEuLi789ttvREVFsWfPHrnPZvTjjz+ya9cuduzYUWGf/E6bT+fOnZk/fz4RERFcuHCBl19+mdtuu40DBw5Y3X2W5EgIUSPjx4/nwIEDJn0GhHlFRESwZ88eMjMzWbJkCSNGjGD9+vWWDqtBOXv2LBMmTCA2NhYHBwdLh9Og9e/f3/i8bdu2dO7cmdDQUH7++WccHR0tGFlF0qxmYT4+Ptja2lbokZ+UlERAQICFomp4yu7l1e5zQEBAhU7wJSUlpKWlyWdxmaeeeoply5axdu1agoODjdsDAgIoKioiIyPDpPzl97myz6Fsn7hEq9XSrFkzOnTowJw5c2jXrh0ffvih3Gcz2rlzJ8nJydx8883Y2dlhZ2fH+vXr+e9//4udnR3+/v5yr2uJh4cHLVq04MSJE1b3Oy3JkYVptVo6dOjA33//bdxmMBj4+++/iY6OtmBkDUtYWBgBAQEm9zkrK4tt27YZ73N0dDQZGRns3LnTWGbNmjUYDAY6d+5c5zFbI0VReOqpp/jtt99Ys2YNYWFhJvs7dOiAvb29yX0+evQo8fHxJvd5//79JolobGwsbm5uREVF1c2F1FMGg4HCwkK5z2bUp08f9u/fz549e4yPjh07MmzYMONzude1Iycnh5MnTxIYGGh9v9Nm7d4tauTHH39UdDqdMn/+fOXQoUPKmDFjFA8PD5Me+eLasrOzld27dyu7d+9WAOW9995Tdu/erZw5c0ZRFEV58803FQ8PD+WPP/5Q9u3bpwwaNEgJCwtT8vPzjce44447lJtuuknZtm2bsnHjRqV58+bKQw89ZKlLsjrjxo1T3N3dlXXr1ikXLlwwPvLy8oxlxo4dqzRu3FhZs2aN8u+//yrR0dFKdHS0cX9JSYnSunVrpV+/fsqePXuUFStWKL6+vsqMGTMscUlWa/r06cr69euV06dPK/v27VOmT5+uaDQaZdWqVYqiyH2uTeVHqymK3GtzmTx5srJu3Trl9OnTyqZNm5S+ffsqPj4+SnJysqIo1nWfJTmyEh999JHSuHFjRavVKp06dVK2bt1q6ZDqnbVr1ypAhceIESMURVGH87/44ouKv7+/otPplD59+ihHjx41OUZqaqry0EMPKS4uLoqbm5vy2GOPKdnZ2Ra4GutU2f0FlHnz5hnL5OfnK08++aTi6empODk5Kffcc49y4cIFk+PExcUp/fv3VxwdHRUfHx9l8uTJSnFxcR1fjXUbNWqUEhoaqmi1WsXX11fp06ePMTFSFLnPteny5EjutXk88MADSmBgoKLVapVGjRopDzzwgHLixAnjfmu6zxpFURTz1kUJIYQQQtRf0udICCGEEKIcSY6EEEIIIcqR5EgIIYQQohxJjoQQQgghypHkSAghhBCiHEmOhBBCCCHKkeRICCGEEKIcSY6EEA1aXFwcGo2GPXv21No5Ro4cyeDBg42ve/bsycSJE2vtfEKI2iXJkRDCqo0cORKNRlPhcccdd1Tp/SEhIVy4cIHWrVvXcqSX/Prrr7z66qt1dj4hhHnZWToAIYS4ljvuuIN58+aZbNPpdFV6r62tbZ2vjO7l5VWn5xNCmJfUHAkhrJ5OpyMgIMDk4enpCYBGo+Gzzz6jf//+ODo6Eh4ezpIlS4zvvbxZLT09nWHDhuHr64ujoyPNmzc3Sbz2799P7969cXR0xNvbmzFjxpCTk2Pcr9frefbZZ/Hw8MDb25upU6dy+SpMlzerpaenM3z4cDw9PXFycqJ///4cP368Fu6UEMIcJDkSQtR7L774Ivfddx979+5l2LBhPPjggxw+fPiKZQ8dOsRff/3F4cOH+eyzz/Dx8QEgNzeXmJgYPD092bFjB4sXL2b16tU89dRTxve/++67zJ8/n2+//ZaNGzeSlpbGb7/9dtX4Ro4cyb///svSpUvZsmULiqJw5513UlxcbL6bIIQwH7MvZSuEEGY0YsQIxdbWVnF2djZ5vP7664qiKAqgjB071uQ9nTt3VsaNG6coiqKcPn1aAZTdu3criqIod911l/LYY49Veq4vv/xS8fT0VHJycozb/u///k+xsbFREhMTFUVRlMDAQGXu3LnG/cXFxUpwcLAyaNAg47byq7ofO3ZMAZRNmzYZ96ekpCiOjo7Kzz//XLObIoSoVdLnSAhh9Xr16sVnn31msq18v57o6GiTfdHR0VccnTZu3Djuu+8+du3aRb9+/Rg8eDBdunQB4PDhw7Rr1w5nZ2dj+a5du2IwGDh69CgODg5cuHCBzp07G/fb2dnRsWPHCk1rZQ4fPoydnZ3Je7y9vYmIiLhi7ZYQwrIkORJCWD1nZ2eaNWtmlmP179+fM2fOsHz5cmJjY+nTpw/jx4/nnXfeMcvxhRD1n/Q5EkLUe1u3bq3wumXLllcs7+vry4gRI/j+++/54IMP+PLLLwFo2bIle/fuJTc311h206ZN2NjYEBERgbu7O4GBgWzbts24v6SkhJ07d17xXC1btqSkpMTkPampqRw9epSoqKhqX6sQovZJzZEQwuoVFhaSmJhoss3u/9u7X1blwTCO4z/xaBCWRcwDQViZhsGwWIUh2GQsGcVg0eKfZjJZDCII4sBgEdG34FsRxGryaXIfOD7lWXgOfD+wNDZulr5cXLCvr/ci9X6/V6VSke/72m63ul6vWq1WP75rNBrJdV2Vy2U9n08dj8d3SLXbbY3HY0VRpMlkotvtpm63qzAMlc/nJUm9Xk+z2Uy2batUKmk+n+vxeHw8u23bCoJAnU5Hy+VSlmVpMBioWCwqCIIEvg6ApDE5AvDfO5/PKhQK3y7f99/3p9Op4jiW4zjabDba7XYfpzLZbFbD4VCO46hWqymdTiuOY0lSLpfT5XLR/X5XtVpVq9VSvV7XYrF4P9/v9xWGoaIokud5sixLzWbzr+dfr9dyXVeNRkOe5+n1eul0OimTySTwdQAkLfX6tEUIAL9AKpXS4XD49vsOAPgXTI4AAAAMxBEAAICBhWwAvxqbAQCSxuQIAADAQBwBAAAYiCMAAAADcQQAAGAgjgAAAAzEEQAAgIE4AgAAMBBHAAAABuIIAADA8AdNE7Gc2HvZbwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def plot_reward_per_episode_together(reward_ep_sarsa, reward_ep_q_learning):\n",
    "    def smooth_rewards(reward_ep):\n",
    "        episode_rewards = np.array(reward_ep)\n",
    "        episode_number = np.linspace(\n",
    "            1, len(episode_rewards) + 1, len(episode_rewards) + 1)\n",
    "        accumulated_rewards = np.cumsum(episode_rewards)\n",
    "        return [accumulated_rewards[i] / episode_number[i] for i in range(len(accumulated_rewards))]\n",
    "\n",
    "    sarsa_smoothed = smooth_rewards(reward_ep_sarsa)\n",
    "    q_learning_smoothed = smooth_rewards(reward_ep_q_learning)\n",
    "\n",
    "    plt.plot(sarsa_smoothed, label=\"SARSA\")\n",
    "    plt.plot(q_learning_smoothed, label=\"Q-learning\")\n",
    "\n",
    "    plt.title(\"Recompensa acumulada por episodio\")\n",
    "    plt.xlabel(\"Episodio\")\n",
    "    plt.ylabel(\"Recompensa\")\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "# Luego puedes usar la función así:\n",
    "plot_reward_per_episode_together(reward_ep_sarsa, reward_ep_q_learning)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EjznrgIeGS48"
   },
   "source": [
    "Veamos pasos por episodio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "id": "7AaVw7EEGS48",
    "outputId": "42c8b5af-85fd-4c40-ea72-b2307db603af"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_steps_per_episode(timesteps_ep_sarsa)\n",
    "plot_steps_per_episode(timesteps_ep_q_learning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_steps_per_episode_together(timesteps_ep_sarsa, timesteps_ep_q_learning):\n",
    "    plt.plot(timesteps_ep_sarsa, label=\"SARSA\")\n",
    "    plt.plot(timesteps_ep_q_learning, label=\"Q-learning\")\n",
    "\n",
    "    plt.title(\"Pasos (timesteps) por episodio\")\n",
    "    plt.xlabel(\"Episodio\")\n",
    "    plt.ylabel(\"Pasos (timesteps)\")\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "plot_steps_per_episode_together(timesteps_ep_sarsa, timesteps_ep_q_learning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_BB8NDDhGS49"
   },
   "source": [
    "Suavizando..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "id": "NaAXjfdKGS49",
    "outputId": "4392abcb-8a3e-4f84-982c-111677245ab7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_steps_per_episode_smooth(timesteps_ep_sarsa)\n",
    "plot_steps_per_episode_smooth(timesteps_ep_q_learning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def plot_steps_per_episode_smooth_together(timesteps_ep_sarsa, timesteps_ep_q_learning):\n",
    "    def smooth_curve(timesteps_ep):\n",
    "        episode_steps = np.array(timesteps_ep)\n",
    "        episode_number = np.linspace(1, len(episode_steps) + 1, len(episode_steps) + 1)\n",
    "        acumulated_steps = np.cumsum(episode_steps)\n",
    "        steps_per_episode = [acumulated_steps[i] / episode_number[i] for i in range(len(acumulated_steps))]\n",
    "        return steps_per_episode\n",
    "\n",
    "    smoothed_sarsa = smooth_curve(timesteps_ep_sarsa)\n",
    "    smoothed_q_learning = smooth_curve(timesteps_ep_q_learning)\n",
    "\n",
    "    plt.plot(smoothed_sarsa, label=\"SARSA\")\n",
    "    plt.plot(smoothed_q_learning, label=\"Q-learning\")\n",
    "\n",
    "    plt.title(\"Pasos (timesteps) acumulados por episodio (suavizados)\")\n",
    "    plt.xlabel(\"Episodio\")\n",
    "    plt.ylabel(\"Pasos (timesteps) acumulados (suavizados)\")\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "plot_steps_per_episode_smooth_together(timesteps_ep_sarsa, timesteps_ep_q_learning)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QI7pxgtoGS49"
   },
   "source": [
    "#### Análisis de matriz de acción-valor y política óptima\n",
    "\n",
    "Siendo que este es un ejemplo tabular y de pocos estados / acciones, es posible realizar un análisis de convergencia desde otro punto de vista: desde el valor de la función $Q(s,a)$ para la mejor acción de cada estado, al finalizar el entrenamiento del agente, (sería la acción que el agente ejecutaría en cada estado bajo una política *greedy*). Ambos nos brindarán información sobre la convergencia alcanzada por el agente.\n",
    "\n",
    "Tener en cuenta que este análisis se hace principalmente con fines educativos, para entornos más complejos el mismo puede no ser factible. En tales casos, un análisis alternativo podría consistir en hacer que el agente ejecute su política para la que fue entrenado, para hacer una evaluación a partir del comportamiento del mismo (esto último sería el *test de la política*, frente al *entrenamiento de la política* previo)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 620
    },
    "id": "rMpKRT2NGS4-",
    "outputId": "d725517c-9a9c-4d89-e003-16dc16e4954e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Matriz de mejor acción-valor (en números): \n",
      "\n",
      " [[  -8.09915993   -7.79813286   -7.51927293   -7.31157512   -7.15500301\n",
      "    -6.98221287   -7.10058065   -6.3303956    -5.39501416   -4.38253103\n",
      "    -3.53746464   -2.72656992]\n",
      " [  -8.37520072   -8.81020429   -8.19073549   -7.62325487   -7.71565019\n",
      "    -7.50796094   -6.81907389   -8.05698196   -4.70528898   -3.90469458\n",
      "    -2.95804277   -1.90019246]\n",
      " [  -8.7304137    -9.22505676   -9.04753721   -8.86867489   -8.63536761\n",
      "    -8.32668616   -8.5508939    -8.76884126   -7.86903212   -9.78222744\n",
      "    -4.02887184   -1.        ]\n",
      " [  -9.03710791 -100.         -100.         -100.         -100.\n",
      "  -100.         -100.         -100.         -100.         -100.\n",
      "  -100.           -1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Matriz de mejor acción-valor (en números): \n",
      "\n",
      " [[  -7.30321559   -7.029416     -6.80869232   -6.55149991   -6.20535527\n",
      "    -5.82917913   -5.41094579   -4.95656722   -4.46486699   -3.88270977\n",
      "    -3.332171     -2.70430523]\n",
      " [  -7.40266424   -7.17122946   -6.86069109   -6.51304103   -6.12573685\n",
      "    -5.69529697   -5.21702236   -4.68558677   -4.09509898   -3.43899958\n",
      "    -2.70999991   -1.9       ]\n",
      " [  -7.17570464   -6.86189404   -6.5132156    -6.12579511   -5.6953279\n",
      "    -5.217031     -4.68559      -4.0951       -3.439        -2.71\n",
      "    -1.9          -1.        ]\n",
      " [  -7.45813417 -100.         -100.         -100.         -100.\n",
      "  -100.         -100.         -100.         -100.         -100.\n",
      "  -100.           -1.        ]]\n"
     ]
    }
   ],
   "source": [
    "draw_value_matrix(q_sarsa)\n",
    "draw_value_matrix(q_q_learning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "ce50gtxcGS4-"
   },
   "outputs": [],
   "source": [
    "env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9VQRIwfLGS4-",
    "tags": []
   },
   "source": [
    "## Actividades\n",
    "\n",
    "1. Implementar y ejecutar el algoritmo SARSA en \"The Cliff\".\n",
    "\n",
    "2. Implementar y ejecutar el algoritmo Q-Learning en \"The Cliff\". ¿Cómo converge con respecto a SARSA? ¿A qué se debe? Comentar.\n",
    "\n",
    "3. Ejecutando con distintos híper-parámetros, realizar una breve descripción sobre cómo afectan a la convergencia los distintos valores de $\\alpha$, $\\epsilon$ y $\\gamma$.\n",
    "\n",
    "4. (Opcional) Implementar política de exploración Softmax, en donde cada acción tiene una probabilidad $$\\pi(a \\mid s) = \\frac{e^{Q(s,a)/\\tau}}{\\sum_{\\widetilde{a} \\in A}e^{Q(s,\\widetilde{a})/\\tau}}$$\n",
    "\n",
    "5. (Opcional) Implementar Dyna-Q a partir del algoritmo Q-Learning, incorporando una actualización mediante un modelo. Comentar cómo se desempeña respecto a los demás algoritmos.\n",
    "\n",
    "\n",
    "Para dejar el lab listo para su corrección, dejar link a repo de Github con un notebook ejecutando el agente en la planilla enviada en Slack."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Se puede visualizar en los resultados mas arriba.\n",
    "\n",
    "2. Q-learning aprende valores para la política óptima, viaja justo al borde del acantilado. Resultando en que ocasionalmente se caiga por el precipicio debido a la acción de $\\epsilon$-greedy. SARSA, por otro lado, tiene en cuenta la selección de acciones y aprende el camino más largo pero más seguro a través de la parte superior de la cuadrícula. Aunque Q-learning en realidad aprende los valores de la política óptima, su rendimiento es peor que el de SARSA, el cual llega a la politica óptima de forma indirecta.\n",
    "\n",
    "3. Efecto de los hiperparámetros en la convergencia:\n",
    "\n",
    "    - $\\alpha$ (tasa de aprendizaje):\n",
    "    Un valor alto de $\\alpha$ puede hacer que el aprendizaje sea inestable y oscile alrededor de los valores óptimos.Un valor bajo de $\\alpha$ (cerca de 0.0) hace que el aprendizaje sea más estable pero lento. Converge de manera suave pero podría llevar mucho tiempo alcanzar la política óptima.\n",
    "\n",
    "    - $\\epsilon$ (tasa de exploración):\n",
    "    Un valor alto de $\\epsilon$ hará que el agente explore más. Si es demasiado alto, el agente podría tardar más en converger a una política óptima.\n",
    "    Un valor bajo de $\\epsilon$ hará que el agente explore menos lo que puede ser útil para la explotación. Sin embargo, podría llevar a una convergencia temprana a una política subóptima.\n",
    "\n",
    "    - $\\gamma$ (factor de descuento):\n",
    "    Un valor alto de $\\gamma$ da más peso a las recompensas a largo plazo. Esto puede llevar a una política más enfocada en las recompensas futuras, pero también puede requerir más exploración para encontrar la política óptima.\n",
    "    Un valor bajo de $\\gamma$ se enfoca más en las recompensas inmediatas y puede llevar a una convergencia más rápida, pero con la consecuencia de pasar por alto recompensas futuras."
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