FMJabato_FinalProject.ipynb

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    "# Curso de Especialista en Ciencia de Datos y Aplicaciones Escalables en la Nube II (CIDAEN)\n",
    "\n",
    "## Trabajo final - Fernando Moreno Jabato\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "El siguiente documento contiene información sobre el trabajo final de D. Fernando Moreno Jabato para el Curso de Especialista en CIencia de Datos y Aplicaciones Escalables en la Nube (CIDAEN - Segunda edición).\n",
    "\n",
    "## 1. El Proyecto\n",
    "\n",
    "El proyecto consiste en la configuración y estudio de una Inteligencia Artificial (IA) basada en keras que aprenda a jugar al juego <a href=\"https://en.wikipedia.org/wiki/Snake_(video_game_genre)\">Snake</a>, en concreto la versión clásica que no permite colisionar con las paredes. \n",
    "\n",
    "Para el desarrollo del mismo nos basaremos en el código por el usuario de github <a href=\"https://github.com/maurock\">maurock</a> el cual ya es funcional y se encuentra en el siguiente <a href=\"https://github.com/maurock/snake-ga\">repositorio</a>.\n",
    "\n",
    "Vamos a modificar el código para que almacene diferente información sobre el aprendizaje del modelo así como para implementar diferentes configuraciones de redes neuronales para identificar cuál obtiene mejor rendimiento.\n",
    "\n",
    "## 2. Código y depencias\n",
    "\n",
    "El código base del que se parte pertence al usuario de github <a href=\"https://github.com/maurock\">maurock</a> que está almacenado en el siguiente <a href=\"https://github.com/maurock/snake-ga\">repositorio</a>.\n",
    "\n",
    "Para el correcto funcionamiento de este código se necesita satisfacer las siguientes dependencias:\n",
    "* **pygame:** permite crear entornos de vista de videojuegos así como simular escenarios basados en reglas (juegos).\n",
    "* **random:** generación de modelos aleatorios.\n",
    "* **numpy:** estructuras de datos.\n",
    "* **keras:** gestor de modelos de machine learning.\n",
    "* **matplotlib:** graficado.\n",
    "* **seaborn:** graficado.\n",
    "* **pandas:** estructuras de datos.\n",
    "* **operator:** funciones eficientes de operaciones intrínsecas de Python.\n",
    "* **time:** para obtener el tiempo del sistema\n",
    "\n",
    "El código ha sido modificado para que no se ejecute direectamente utilizando la función `run()` de la clase principal `snakeClass`. Así mismo, algunos métodos serán sobrescritos en esta misma libreta de Jupyter para facilitar la gestión de las redes y dicho método principal será simulado en eesta libreta."
   ]
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   "source": [
    "# Import necessary packages\n",
    "import pygame\n",
    "import random\n",
    "import os\n",
    "import time\n",
    "from random import randint\n",
    "from DQN import DQNAgent\n",
    "from snakeClass import *\n",
    "import numpy as np\n",
    "from keras.utils import to_categorical\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from keras.optimizers import Adam\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout\n",
    "from keras.layers import Embedding, LSTM\n",
    "import pandas as pd\n",
    "from operator import add"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "### 2.1 Estructura del código\n",
    "\n",
    "\n",
    "## 3. Modificaciones del código\n",
    "A continuación se detallan las modificaciones realizadas sobre el código:\n",
    "* Se crea un nuevo main genérico que almacena las tuplas `{current_score,max_score}` así como los pesos de la red entrenada.\n",
    "* Se modififca el código en el fichero `snakeClass.py` para evitar desplegar el visor del juego a menos que se solicite.\n",
    "* Se incluyen métodos para medir el tiempo de ejcución de cada simulación.\n",
    "\n",
    "**Nota:** Al utilizar el código original, se observan `warnings`por métodos obsoletos de la API de `Keras`. Estos métodos se mantendrán para respetar el código original del autor aunque la nueva versión de la API será utilizada para los modelos propios.\n",
    "\n",
    "El nuevo main genérico creado es el siguiente:"
   ]
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   "cell_type": "code",
   "execution_count": 99,
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    {
     "name": "stderr",
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     "text": [
      "C:\\Users\\FMJabato\\OneDrive\\Documentos\\Estudios\\003_Curso especialista Ciencia de Datos\\Capstones\\cs2-fmjabato-TFinal\\Snake\\DQN.py:79: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", input_dim=11, units=120)`\n",
      "  model.add(Dense(output_dim=120, activation='relu', input_dim=11))\n",
      "C:\\Users\\FMJabato\\OneDrive\\Documentos\\Estudios\\003_Curso especialista Ciencia de Datos\\Capstones\\cs2-fmjabato-TFinal\\Snake\\DQN.py:81: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", units=120)`\n",
      "  model.add(Dense(output_dim=120, activation='relu'))\n",
      "C:\\Users\\FMJabato\\OneDrive\\Documentos\\Estudios\\003_Curso especialista Ciencia de Datos\\Capstones\\cs2-fmjabato-TFinal\\Snake\\DQN.py:83: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", units=120)`\n",
      "  model.add(Dense(output_dim=120, activation='relu'))\n",
      "C:\\Users\\FMJabato\\OneDrive\\Documentos\\Estudios\\003_Curso especialista Ciencia de Datos\\Capstones\\cs2-fmjabato-TFinal\\Snake\\DQN.py:85: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"softmax\", units=3)`\n",
      "  model.add(Dense(output_dim=3, activation='softmax'))\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Function to execute a DQNAgent with snake game.\n",
    "    Inputs:\n",
    "        - agent : DQNAgent object (or child) to be executed\n",
    "        - games : simulations to be performed\n",
    "        - weights_file : output file where trained model nodes weights will be stored\n",
    "        - verbose : boolean to activate verbose mode.\n",
    "        - display_option: boolean to activate graphic UI to show simulations\n",
    "    Outputs:\n",
    "        - sim_scores : maximum score obtained in the simulation\n",
    "        - sim_times : current simulation\n",
    "'''\n",
    "def run(agent = DQNAgent(),games = 150,weights_file = \"weights.hdf5\",verbose = False, display_option = False):\n",
    "    # Initialize variables\n",
    "    pygame.init()\n",
    "    os.environ[\"SDL_VIDEODRIVER\"] = \"dummy\"\n",
    "    #agent = DQNAgent()\n",
    "    counter_games = 0\n",
    "    sim_scores = []\n",
    "    sim_times =[]\n",
    "    record = 0\n",
    "    if display_option:\n",
    "        pygame.font.init()\n",
    "        speed = 50\n",
    "    # Simulate\n",
    "    while counter_games < games:\n",
    "        # Initialize classes\n",
    "        game = Game(440, 440)\n",
    "        player1 = game.player\n",
    "        food1 = game.food\n",
    "\n",
    "        # Perform first move\n",
    "        initialize_game(player1, game, food1, agent)\n",
    "        if display_option:\n",
    "            display(player1, food1, game, record)\n",
    "        \n",
    "        # Time point\n",
    "        start = time.time()\n",
    "        \n",
    "        while not game.crash:\n",
    "            #agent.epsilon is set to give randomness to actions\n",
    "            agent.epsilon = 80 - counter_games\n",
    "            \n",
    "            #get old state\n",
    "            state_old = agent.get_state(game, player1, food1)\n",
    "            \n",
    "            #perform random actions based on agent.epsilon, or choose the action\n",
    "            if randint(0, 200) < agent.epsilon:\n",
    "                final_move = to_categorical(randint(0, 2), num_classes=3)\n",
    "            else:\n",
    "                # predict action based on the old state\n",
    "                prediction = agent.model.predict(state_old.reshape((1,11)))\n",
    "                final_move = to_categorical(np.argmax(prediction[0]), num_classes=3)\n",
    "                \n",
    "            #perform new move and get new state\n",
    "            player1.do_move(final_move, player1.x, player1.y, game, food1, agent)\n",
    "            state_new = agent.get_state(game, player1, food1)\n",
    "            \n",
    "            #set reward for the new state\n",
    "            reward = agent.set_reward(player1, game.crash)\n",
    "            \n",
    "            #train short memory base on the new action and state\n",
    "            agent.train_short_memory(state_old, final_move, reward, state_new, game.crash)\n",
    "            \n",
    "            # store the new data into a long term memory\n",
    "            agent.remember(state_old, final_move, reward, state_new, game.crash)\n",
    "            record = get_record(game.score, record)\n",
    "            if display_option:\n",
    "                display(player1, food1, game, record)\n",
    "                pygame.time.wait(speed)\n",
    "                \n",
    "        # Time point\n",
    "        end = time.time()\n",
    "        exec_time = end - start\n",
    "        \n",
    "        # Store game statistics\n",
    "        agent.replay_new(agent.memory)\n",
    "        counter_games += 1\n",
    "        if verbose:\n",
    "            print('Game', counter_games, '\\tScore:', game.score, 'Time:', exec_time, '\\tRecord:',record)\n",
    "        sim_scores.append(game.score)\n",
    "        sim_times.append(exec_time)\n",
    "    agent.model.save_weights(weights_file)\n",
    "    # Close display\n",
    "    if display_option:\n",
    "        pygame.font.quit()\n",
    "    # return statistics\n",
    "    return sim_scores, sim_times"
   ]
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   "source": [
    "### 3.1 Desactivación de código\n",
    "Hay ciertas partes del código que tienen que ser desactivadas para poder ser re-implementadas en este trabajo. En concreto, la más importante es la anulación de la llamada a `run()`del script principal almacenado en `snakeClass.py`\n",
    "\n",
    "### 3.2 Puntos de control\n",
    "\n",
    "\n",
    "### 3.3 Modelo usado por la IA\n",
    "El modelo usado por la IA es una red neuronal de Keras. El modelo base configurado por `maurock`consiste en una red con la siguiente disposición:\n",
    "* Tres redes densas de 120 nodos con activación `ReLU` con un dropout con `ratio = 0.15`para cada una.\n",
    "* Una red densa de 3 nodos como salida y activación tipo `Softmax`.\n",
    "* Un optimizador tipo Adam con ratio de aprendizaje 0.0005 y una aproximación por _Mean Square Error (mse)_\n",
    "\n",
    "Dicha red está guardada en el método `network`de la clase `DQN`. Para modificarlo vamos a proceder a crear clases herederas de ésta que sobrescriban el método de la red. Del mismo modo, si quisiéramos modificar alguno de los parámetros de configuración de la clase podríamos jugar con los siguientes:\n",
    "self.reward = 0\n",
    "* **gamma:** Default = 0.9\n",
    "* **learning_rate:** Default = 0.0005\n",
    "\n",
    "Aunque para modificar el `learning_rate vamos` a modificarlo directamente en la creación de las redes.\n",
    "\n",
    "Vamos a ejecutar cinco redes:\n",
    "1. (`Original`) Red original del repositorio.\n",
    "2. (`Modified`) Red original del repositorio modificando las redes densas y el ratio de `dropout`\n",
    "3. (`Pyramid`) Red de estructura piramidal manteniendo el número de `layers` pero incrementando su tamaño y asignando valores específicos de `dropout` a cada capa.\n",
    "4. (`Pyramid_Low`) Misma estructura que `Pyramid` pero bajando el ratio de `dropout`.\n",
    "5. (`LSTM`) Red tipo _Long Short-Term Memory_.\n",
    "\n",
    "Para realizar las modificacione snecesitamos definir clases hija de la clase `DQNAgent`, que sobrescriban el método que devuelve la red del modelo (`network()`). Todas configuradas a continuación:"
   ]
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   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Model Name : Modified\n",
    "Changes performed:\n",
    "    - Added nodes to all dense layers (less output dimension)\n",
    "    - Modify Droput ratios\n",
    "'''\n",
    "class ModifiedDQNAgent(DQNAgent):\n",
    "    def network(self, weights=None):\n",
    "        model = Sequential()\n",
    "        model.add(Dense(180, activation='relu', input_dim = 11))\n",
    "        model.add(Dropout(0.3))\n",
    "        model.add(Dense(180, activation='relu'))\n",
    "        model.add(Dropout(0.3))\n",
    "        model.add(Dense(180, activation='relu'))\n",
    "        model.add(Dropout(0.3))\n",
    "        model.add(Dense(3, activation='softmax'))\n",
    "        opt = Adam(self.learning_rate)\n",
    "        model.compile(loss='mse', optimizer=opt)\n",
    "\n",
    "        if weights:\n",
    "            model.load_weights(weights)\n",
    "        return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Model Name : Pyramid \n",
    "Changes performed:\n",
    "    - Modified dense layers distribution (number_layers: equal; sizes: modified & not_shared)\n",
    "    - Modify Droput ratios per layer\n",
    "'''\n",
    "class PyramidDQNAgent(DQNAgent):\n",
    "    def network(self, weights=None):\n",
    "        model = Sequential()\n",
    "        model.add(Dense(60, activation='relu', input_dim = 11))\n",
    "        model.add(Dropout(0.15))\n",
    "        model.add(Dense(120, activation='relu'))\n",
    "        model.add(Dropout(0.3))\n",
    "        model.add(Dense(240, activation='relu'))\n",
    "        model.add(Dropout(0.4))\n",
    "        model.add(Dense(3, activation='softmax'))\n",
    "        opt = Adam(self.learning_rate)\n",
    "        model.compile(loss='mse', optimizer=opt)\n",
    "\n",
    "        if weights:\n",
    "            model.load_weights(weights)\n",
    "        return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Model Name : Pyramid_Low\n",
    "Changes performed:\n",
    "    - Modified dense layers distribution (number_layers: equal; sizes: modified & not_shared)\n",
    "    - Modify Droput ratios per layer\n",
    "'''\n",
    "class PyramidLowDQNAgent(DQNAgent):\n",
    "    def network(self, weights=None):\n",
    "        model = Sequential()\n",
    "        model.add(Dense(60, activation='relu', input_dim = 11))\n",
    "        model.add(Dropout(0.15))\n",
    "        model.add(Dense(120, activation='relu'))\n",
    "        model.add(Dropout(0.15))\n",
    "        model.add(Dense(240, activation='relu'))\n",
    "        model.add(Dropout(0.15))\n",
    "        model.add(Dense(3, activation='softmax'))\n",
    "        opt = Adam(self.learning_rate)\n",
    "        model.compile(loss='mse', optimizer=opt)\n",
    "\n",
    "        if weights:\n",
    "            model.load_weights(weights)\n",
    "        return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Model Name : LSTM (Long Short-Term Memory)\n",
    "Changes performed:\n",
    "    - Modified net structure\n",
    "'''\n",
    "class LSTMDQNAgent(DQNAgent):\n",
    "    def network(self, weights=None):\n",
    "        model = Sequential()\n",
    "        model.add(Embedding(11, output_dim=256))\n",
    "        model.add(LSTM(128))\n",
    "        model.add(Dropout(0.3))\n",
    "        model.add(Dense(3, activation='softmax'))\n",
    "        opt = Adam(self.learning_rate)\n",
    "        model.compile(loss='mse', optimizer=opt)\n",
    "\n",
    "        if weights:\n",
    "            model.load_weights(weights)\n",
    "        return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Prueba y estudio del código\n",
    "\n",
    "### 4.1 Entrenamiento de la IA\n",
    "A continuación se ejecutaran los modelos creados para su posterior comparación."
   ]
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   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "Game 1 \tScore: 1 Time: 0.2652909755706787 \tRecord: 1\n",
      "Game 2 \tScore: 0 Time: 0.08076810836791992 \tRecord: 1\n",
      "Game 3 \tScore: 1 Time: 0.08377599716186523 \tRecord: 1\n",
      "Game 4 \tScore: 1 Time: 0.11170196533203125 \tRecord: 1\n",
      "Game 5 \tScore: 0 Time: 0.27776575088500977 \tRecord: 1\n",
      "Game 6 \tScore: 0 Time: 0.08676767349243164 \tRecord: 1\n",
      "Game 7 \tScore: 1 Time: 0.16655540466308594 \tRecord: 1\n",
      "Game 8 \tScore: 2 Time: 0.5383546352386475 \tRecord: 2\n",
      "Game 9 \tScore: 2 Time: 0.12662220001220703 \tRecord: 2\n",
      "Game 10 \tScore: 1 Time: 0.05086183547973633 \tRecord: 2\n",
      "Game 11 \tScore: 2 Time: 0.0997314453125 \tRecord: 2\n",
      "Game 12 \tScore: 1 Time: 0.0468747615814209 \tRecord: 2\n",
      "Game 13 \tScore: 2 Time: 0.06183481216430664 \tRecord: 2\n",
      "Game 14 \tScore: 1 Time: 0.05968952178955078 \tRecord: 2\n",
      "Game 15 \tScore: 1 Time: 0.1406230926513672 \tRecord: 2\n",
      "Game 16 \tScore: 2 Time: 0.11668777465820312 \tRecord: 2\n",
      "Game 17 \tScore: 1 Time: 0.04288506507873535 \tRecord: 2\n",
      "Game 18 \tScore: 1 Time: 0.04686093330383301 \tRecord: 2\n",
      "Game 19 \tScore: 4 Time: 0.26429247856140137 \tRecord: 4\n",
      "Game 20 \tScore: 1 Time: 0.12167501449584961 \tRecord: 4\n",
      "Game 21 \tScore: 1 Time: 0.07981753349304199 \tRecord: 4\n",
      "Game 22 \tScore: 2 Time: 0.05784463882446289 \tRecord: 4\n",
      "Game 23 \tScore: 1 Time: 0.13965582847595215 \tRecord: 4\n",
      "Game 24 \tScore: 3 Time: 0.0937187671661377 \tRecord: 4\n",
      "Game 25 \tScore: 1 Time: 0.04978466033935547 \tRecord: 4\n",
      "Game 26 \tScore: 1 Time: 0.06683135032653809 \tRecord: 4\n",
      "Game 27 \tScore: 0 Time: 0.04886937141418457 \tRecord: 4\n",
      "Game 28 \tScore: 1 Time: 0.09574198722839355 \tRecord: 4\n",
      "Game 29 \tScore: 2 Time: 0.05485391616821289 \tRecord: 4\n",
      "Game 30 \tScore: 1 Time: 0.13862895965576172 \tRecord: 4\n",
      "Game 31 \tScore: 1 Time: 0.034598588943481445 \tRecord: 4\n",
      "Game 32 \tScore: 2 Time: 0.20825719833374023 \tRecord: 4\n",
      "Game 33 \tScore: 6 Time: 0.26939845085144043 \tRecord: 6\n",
      "Game 34 \tScore: 1 Time: 0.03665900230407715 \tRecord: 6\n",
      "Game 35 \tScore: 1 Time: 0.11569023132324219 \tRecord: 6\n",
      "Game 36 \tScore: 5 Time: 0.22751331329345703 \tRecord: 6\n",
      "Game 37 \tScore: 4 Time: 1.118058681488037 \tRecord: 6\n",
      "Game 38 \tScore: 5 Time: 0.4262721538543701 \tRecord: 6\n",
      "Game 39 \tScore: 7 Time: 0.6642248630523682 \tRecord: 7\n",
      "Game 40 \tScore: 3 Time: 0.22061991691589355 \tRecord: 7\n",
      "Game 41 \tScore: 3 Time: 0.09154939651489258 \tRecord: 7\n",
      "Game 42 \tScore: 1 Time: 0.05086469650268555 \tRecord: 7\n",
      "Game 43 \tScore: 2 Time: 0.12865567207336426 \tRecord: 7\n",
      "Game 44 \tScore: 4 Time: 0.3895571231842041 \tRecord: 7\n",
      "Game 45 \tScore: 2 Time: 0.09474635124206543 \tRecord: 7\n",
      "Game 46 \tScore: 3 Time: 0.07081151008605957 \tRecord: 7\n",
      "Game 47 \tScore: 1 Time: 0.07579946517944336 \tRecord: 7\n",
      "Game 48 \tScore: 3 Time: 0.06781840324401855 \tRecord: 7\n",
      "Game 49 \tScore: 4 Time: 0.3548696041107178 \tRecord: 7\n",
      "Game 50 \tScore: 3 Time: 0.8339259624481201 \tRecord: 7\n",
      "Game 51 \tScore: 6 Time: 0.27463245391845703 \tRecord: 7\n",
      "Game 52 \tScore: 6 Time: 0.36396265029907227 \tRecord: 7\n",
      "Game 53 \tScore: 1 Time: 0.14179348945617676 \tRecord: 7\n",
      "Game 54 \tScore: 9 Time: 0.3873107433319092 \tRecord: 9\n",
      "Game 55 \tScore: 1 Time: 0.06699967384338379 \tRecord: 9\n",
      "Game 56 \tScore: 5 Time: 0.6496732234954834 \tRecord: 9\n",
      "Game 57 \tScore: 5 Time: 0.4498250484466553 \tRecord: 9\n",
      "Game 58 \tScore: 3 Time: 0.42885732650756836 \tRecord: 9\n",
      "Game 59 \tScore: 6 Time: 0.3759927749633789 \tRecord: 9\n",
      "Game 60 \tScore: 2 Time: 0.15957403182983398 \tRecord: 9\n",
      "Game 61 \tScore: 4 Time: 0.20138072967529297 \tRecord: 9\n",
      "Game 62 \tScore: 4 Time: 0.09474849700927734 \tRecord: 9\n",
      "Game 63 \tScore: 2 Time: 0.15877509117126465 \tRecord: 9\n",
      "Game 64 \tScore: 1 Time: 0.08975934982299805 \tRecord: 9\n",
      "Game 65 \tScore: 5 Time: 0.4109210968017578 \tRecord: 9\n",
      "Game 66 \tScore: 9 Time: 0.7510244846343994 \tRecord: 9\n",
      "Game 67 \tScore: 1 Time: 0.18849968910217285 \tRecord: 9\n",
      "Game 68 \tScore: 4 Time: 0.4438285827636719 \tRecord: 9\n",
      "Game 69 \tScore: 1 Time: 0.0857698917388916 \tRecord: 9\n",
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      "Game 869 \tScore: 3 Time: 0.07084250450134277 \tRecord: 42\n",
      "Game 870 \tScore: 9 Time: 0.9250121116638184 \tRecord: 42\n",
      "Game 871 \tScore: 2 Time: 0.18946218490600586 \tRecord: 42\n",
      "Game 872 \tScore: 11 Time: 1.1085336208343506 \tRecord: 42\n",
      "Game 873 \tScore: 12 Time: 0.612135648727417 \tRecord: 42\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Game 874 \tScore: 12 Time: 0.7167112827301025 \tRecord: 42\n",
      "Game 875 \tScore: 11 Time: 0.7061104774475098 \tRecord: 42\n",
      "Game 876 \tScore: 24 Time: 1.8978264331817627 \tRecord: 42\n",
      "Game 877 \tScore: 8 Time: 0.23296284675598145 \tRecord: 42\n",
      "Game 878 \tScore: 4 Time: 0.24380898475646973 \tRecord: 42\n",
      "Game 879 \tScore: 11 Time: 1.30485200881958 \tRecord: 42\n",
      "Game 880 \tScore: 15 Time: 0.6907694339752197 \tRecord: 42\n",
      "Game 881 \tScore: 3 Time: 0.21741747856140137 \tRecord: 42\n",
      "Game 882 \tScore: 18 Time: 1.4484279155731201 \tRecord: 42\n",
      "Game 883 \tScore: 25 Time: 1.2059004306793213 \tRecord: 42\n",
      "Game 884 \tScore: 3 Time: 0.17752528190612793 \tRecord: 42\n",
      "Game 885 \tScore: 27 Time: 1.4310016632080078 \tRecord: 42\n",
      "Game 886 \tScore: 23 Time: 1.086097002029419 \tRecord: 42\n",
      "Game 887 \tScore: 10 Time: 0.7179334163665771 \tRecord: 42\n",
      "Game 888 \tScore: 7 Time: 0.3370985984802246 \tRecord: 42\n",
      "Game 889 \tScore: 15 Time: 0.677342414855957 \tRecord: 42\n",
      "Game 890 \tScore: 19 Time: 1.1069376468658447 \tRecord: 42\n",
      "Game 891 \tScore: 5 Time: 0.2543187141418457 \tRecord: 42\n",
      "Game 892 \tScore: 3 Time: 0.08078503608703613 \tRecord: 42\n",
      "Game 893 \tScore: 24 Time: 1.1863048076629639 \tRecord: 42\n",
      "Game 894 \tScore: 17 Time: 0.8058724403381348 \tRecord: 42\n",
      "Game 895 \tScore: 11 Time: 1.5750319957733154 \tRecord: 42\n",
      "Game 896 \tScore: 12 Time: 0.589287281036377 \tRecord: 42\n",
      "Game 897 \tScore: 9 Time: 0.8383526802062988 \tRecord: 42\n",
      "Game 898 \tScore: 5 Time: 0.5375590324401855 \tRecord: 42\n",
      "Game 899 \tScore: 7 Time: 0.31417059898376465 \tRecord: 42\n",
      "Game 900 \tScore: 4 Time: 0.17852067947387695 \tRecord: 42\n",
      "Game 901 \tScore: 9 Time: 0.4587421417236328 \tRecord: 42\n",
      "Game 902 \tScore: 25 Time: 1.183495283126831 \tRecord: 42\n",
      "Game 903 \tScore: 23 Time: 1.559159517288208 \tRecord: 42\n",
      "Game 904 \tScore: 5 Time: 0.3761889934539795 \tRecord: 42\n",
      "Game 905 \tScore: 15 Time: 1.3970749378204346 \tRecord: 42\n",
      "Game 906 \tScore: 13 Time: 0.7049627304077148 \tRecord: 42\n",
      "Game 907 \tScore: 2 Time: 0.12922906875610352 \tRecord: 42\n",
      "Game 908 \tScore: 14 Time: 0.6661267280578613 \tRecord: 42\n",
      "Game 909 \tScore: 12 Time: 0.5474987030029297 \tRecord: 42\n",
      "Game 910 \tScore: 5 Time: 0.42190074920654297 \tRecord: 42\n",
      "Game 911 \tScore: 13 Time: 0.795921802520752 \tRecord: 42\n",
      "Game 912 \tScore: 8 Time: 0.573216438293457 \tRecord: 42\n",
      "Game 913 \tScore: 18 Time: 1.015681266784668 \tRecord: 42\n",
      "Game 914 \tScore: 11 Time: 0.5565290451049805 \tRecord: 42\n",
      "Game 915 \tScore: 10 Time: 0.24035429954528809 \tRecord: 42\n",
      "Game 916 \tScore: 13 Time: 1.1899027824401855 \tRecord: 42\n",
      "Game 917 \tScore: 24 Time: 1.649620532989502 \tRecord: 42\n",
      "Game 918 \tScore: 27 Time: 1.4567084312438965 \tRecord: 42\n",
      "Game 919 \tScore: 7 Time: 0.37755870819091797 \tRecord: 42\n",
      "Game 920 \tScore: 10 Time: 0.324451208114624 \tRecord: 42\n",
      "Game 921 \tScore: 13 Time: 0.7121295928955078 \tRecord: 42\n",
      "Game 922 \tScore: 5 Time: 0.3129255771636963 \tRecord: 42\n",
      "Game 923 \tScore: 12 Time: 0.5989034175872803 \tRecord: 42\n",
      "Game 924 \tScore: 15 Time: 0.5916876792907715 \tRecord: 42\n",
      "Game 925 \tScore: 8 Time: 0.6743075847625732 \tRecord: 42\n",
      "Game 926 \tScore: 22 Time: 1.0977489948272705 \tRecord: 42\n",
      "Game 927 \tScore: 7 Time: 0.21350836753845215 \tRecord: 42\n",
      "Game 928 \tScore: 13 Time: 0.6761736869812012 \tRecord: 42\n",
      "Game 929 \tScore: 17 Time: 1.1130776405334473 \tRecord: 42\n",
      "Game 930 \tScore: 15 Time: 1.2894959449768066 \tRecord: 42\n",
      "Game 931 \tScore: 5 Time: 0.18450689315795898 \tRecord: 42\n",
      "Game 932 \tScore: 4 Time: 0.14638519287109375 \tRecord: 42\n",
      "Game 933 \tScore: 1 Time: 0.5333347320556641 \tRecord: 42\n",
      "Game 934 \tScore: 10 Time: 0.6302847862243652 \tRecord: 42\n",
      "Game 935 \tScore: 5 Time: 0.2324080467224121 \tRecord: 42\n",
      "Game 936 \tScore: 4 Time: 0.13251471519470215 \tRecord: 42\n",
      "Game 937 \tScore: 13 Time: 1.0611927509307861 \tRecord: 42\n",
      "Game 938 \tScore: 5 Time: 0.30126190185546875 \tRecord: 42\n",
      "Game 939 \tScore: 5 Time: 0.8967022895812988 \tRecord: 42\n",
      "Game 940 \tScore: 23 Time: 1.2888309955596924 \tRecord: 42\n",
      "Game 941 \tScore: 32 Time: 2.383742332458496 \tRecord: 42\n",
      "Game 942 \tScore: 5 Time: 0.1296525001525879 \tRecord: 42\n",
      "Game 943 \tScore: 16 Time: 2.429281234741211 \tRecord: 42\n",
      "Game 944 \tScore: 3 Time: 0.1685478687286377 \tRecord: 42\n",
      "Game 945 \tScore: 13 Time: 0.603381872177124 \tRecord: 42\n",
      "Game 946 \tScore: 19 Time: 0.6680431365966797 \tRecord: 42\n",
      "Game 947 \tScore: 11 Time: 0.3374662399291992 \tRecord: 42\n",
      "Game 948 \tScore: 22 Time: 1.289696216583252 \tRecord: 42\n",
      "Game 949 \tScore: 11 Time: 0.5707366466522217 \tRecord: 42\n",
      "Game 950 \tScore: 12 Time: 0.45136356353759766 \tRecord: 42\n",
      "Game 951 \tScore: 19 Time: 0.7957231998443604 \tRecord: 42\n",
      "Game 952 \tScore: 7 Time: 0.22362494468688965 \tRecord: 42\n",
      "Game 953 \tScore: 2 Time: 0.15531468391418457 \tRecord: 42\n",
      "Game 954 \tScore: 10 Time: 0.6654760837554932 \tRecord: 42\n",
      "Game 955 \tScore: 12 Time: 0.5874185562133789 \tRecord: 42\n",
      "Game 956 \tScore: 4 Time: 0.3420886993408203 \tRecord: 42\n",
      "Game 957 \tScore: 6 Time: 0.2605900764465332 \tRecord: 42\n",
      "Game 958 \tScore: 13 Time: 0.6584153175354004 \tRecord: 42\n",
      "Game 959 \tScore: 4 Time: 0.20694756507873535 \tRecord: 42\n",
      "Game 960 \tScore: 13 Time: 0.576418399810791 \tRecord: 42\n",
      "Game 961 \tScore: 12 Time: 0.6153223514556885 \tRecord: 42\n",
      "Game 962 \tScore: 6 Time: 0.22739076614379883 \tRecord: 42\n",
      "Game 963 \tScore: 14 Time: 0.9155268669128418 \tRecord: 42\n",
      "Game 964 \tScore: 8 Time: 0.4118971824645996 \tRecord: 42\n",
      "Game 965 \tScore: 3 Time: 0.3145103454589844 \tRecord: 42\n",
      "Game 966 \tScore: 13 Time: 1.5068023204803467 \tRecord: 42\n",
      "Game 967 \tScore: 15 Time: 0.8337695598602295 \tRecord: 42\n",
      "Game 968 \tScore: 17 Time: 1.128817081451416 \tRecord: 42\n",
      "Game 969 \tScore: 5 Time: 0.16156625747680664 \tRecord: 42\n",
      "Game 970 \tScore: 1 Time: 0.060884714126586914 \tRecord: 42\n",
      "Game 971 \tScore: 12 Time: 0.6967079639434814 \tRecord: 42\n",
      "Game 972 \tScore: 11 Time: 0.7290008068084717 \tRecord: 42\n",
      "Game 973 \tScore: 8 Time: 0.2712736129760742 \tRecord: 42\n",
      "Game 974 \tScore: 2 Time: 0.4866969585418701 \tRecord: 42\n",
      "Game 975 \tScore: 3 Time: 0.16755175590515137 \tRecord: 42\n",
      "Game 976 \tScore: 23 Time: 1.1672022342681885 \tRecord: 42\n",
      "Game 977 \tScore: 9 Time: 0.5977027416229248 \tRecord: 42\n",
      "Game 978 \tScore: 21 Time: 1.361377477645874 \tRecord: 42\n",
      "Game 979 \tScore: 18 Time: 0.7336814403533936 \tRecord: 42\n",
      "Game 980 \tScore: 18 Time: 0.8905277252197266 \tRecord: 42\n",
      "Game 981 \tScore: 19 Time: 1.0504863262176514 \tRecord: 42\n",
      "Game 982 \tScore: 9 Time: 0.323136568069458 \tRecord: 42\n",
      "Game 983 \tScore: 16 Time: 0.616396427154541 \tRecord: 42\n",
      "Game 984 \tScore: 13 Time: 0.594703197479248 \tRecord: 42\n",
      "Game 985 \tScore: 3 Time: 0.09648752212524414 \tRecord: 42\n",
      "Game 986 \tScore: 11 Time: 0.3450636863708496 \tRecord: 42\n",
      "Game 987 \tScore: 24 Time: 1.3540771007537842 \tRecord: 42\n",
      "Game 988 \tScore: 15 Time: 1.1823821067810059 \tRecord: 42\n",
      "Game 989 \tScore: 24 Time: 1.3797988891601562 \tRecord: 42\n",
      "Game 990 \tScore: 3 Time: 0.41188526153564453 \tRecord: 42\n",
      "Game 991 \tScore: 6 Time: 0.3884005546569824 \tRecord: 42\n",
      "Game 992 \tScore: 17 Time: 1.507068395614624 \tRecord: 42\n",
      "Game 993 \tScore: 13 Time: 1.6099975109100342 \tRecord: 42\n",
      "Game 994 \tScore: 13 Time: 0.8412425518035889 \tRecord: 42\n",
      "Game 995 \tScore: 12 Time: 0.8239729404449463 \tRecord: 42\n",
      "Game 996 \tScore: 7 Time: 0.7949111461639404 \tRecord: 42\n",
      "Game 997 \tScore: 12 Time: 0.7918806076049805 \tRecord: 42\n",
      "Game 998 \tScore: 17 Time: 1.4836854934692383 \tRecord: 42\n",
      "Game 999 \tScore: 3 Time: 0.09671878814697266 \tRecord: 42\n",
      "Game 1000 \tScore: 6 Time: 0.25473546981811523 \tRecord: 42\n"
     ]
    }
   ],
   "source": [
    "# Ejecución del código original\n",
    "original_scores, original_times = run(games = 1000, weights_file = \"original_weights.hdf5\",verbose = True)\n",
    "\n",
    "# Store/Load\n",
    "np.save(\"original_scores\", original_scores)\n",
    "np.save(\"original_times\", original_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Game 1 \tScore: 0 Time: 0.06482577323913574 \tRecord: 0\n",
      "Game 2 \tScore: 0 Time: 0.12218022346496582 \tRecord: 0\n",
      "Game 3 \tScore: 0 Time: 0.2483360767364502 \tRecord: 0\n",
      "Game 4 \tScore: 0 Time: 0.08377552032470703 \tRecord: 0\n",
      "Game 5 \tScore: 0 Time: 0.04856157302856445 \tRecord: 0\n",
      "Game 6 \tScore: 0 Time: 0.03291177749633789 \tRecord: 0\n",
      "Game 7 \tScore: 0 Time: 0.029915332794189453 \tRecord: 0\n",
      "Game 8 \tScore: 0 Time: 0.16155123710632324 \tRecord: 0\n",
      "Game 9 \tScore: 0 Time: 0.25729942321777344 \tRecord: 0\n",
      "Game 10 \tScore: 0 Time: 0.11962509155273438 \tRecord: 0\n",
      "Game 11 \tScore: 1 Time: 0.07679486274719238 \tRecord: 1\n",
      "Game 12 \tScore: 1 Time: 0.13442230224609375 \tRecord: 1\n",
      "Game 13 \tScore: 0 Time: 0.0967409610748291 \tRecord: 1\n",
      "Game 14 \tScore: 0 Time: 0.23337674140930176 \tRecord: 1\n",
      "Game 15 \tScore: 1 Time: 0.14261817932128906 \tRecord: 1\n",
      "Game 16 \tScore: 3 Time: 0.8314130306243896 \tRecord: 3\n",
      "Game 17 \tScore: 2 Time: 0.15358924865722656 \tRecord: 3\n",
      "Game 18 \tScore: 1 Time: 0.22838830947875977 \tRecord: 3\n",
      "Game 19 \tScore: 1 Time: 0.1077120304107666 \tRecord: 3\n",
      "Game 20 \tScore: 3 Time: 0.19260668754577637 \tRecord: 3\n",
      "Game 21 \tScore: 3 Time: 0.1326446533203125 \tRecord: 3\n",
      "Game 22 \tScore: 2 Time: 0.18042588233947754 \tRecord: 3\n",
      "Game 23 \tScore: 2 Time: 1.1566972732543945 \tRecord: 3\n",
      "Game 24 \tScore: 2 Time: 0.22044110298156738 \tRecord: 3\n",
      "Game 25 \tScore: 2 Time: 0.048842430114746094 \tRecord: 3\n",
      "Game 26 \tScore: 1 Time: 0.1296532154083252 \tRecord: 3\n",
      "Game 27 \tScore: 4 Time: 0.6098802089691162 \tRecord: 4\n",
      "Game 28 \tScore: 1 Time: 0.03789877891540527 \tRecord: 4\n",
      "Game 29 \tScore: 1 Time: 0.19901514053344727 \tRecord: 4\n",
      "Game 30 \tScore: 1 Time: 1.1924266815185547 \tRecord: 4\n",
      "Game 31 \tScore: 2 Time: 0.19081449508666992 \tRecord: 4\n",
      "Game 32 \tScore: 0 Time: 0.07380342483520508 \tRecord: 4\n",
      "Game 33 \tScore: 2 Time: 0.23636698722839355 \tRecord: 4\n",
      "Game 34 \tScore: 1 Time: 0.07682609558105469 \tRecord: 4\n",
      "Game 35 \tScore: 4 Time: 0.14261841773986816 \tRecord: 4\n",
      "Game 36 \tScore: 2 Time: 0.05983996391296387 \tRecord: 4\n",
      "Game 37 \tScore: 0 Time: 0.05185413360595703 \tRecord: 4\n",
      "Game 38 \tScore: 1 Time: 0.09079146385192871 \tRecord: 4\n",
      "Game 39 \tScore: 0 Time: 0.05285835266113281 \tRecord: 4\n",
      "Game 40 \tScore: 5 Time: 0.40294981002807617 \tRecord: 5\n",
      "Game 41 \tScore: 3 Time: 0.36302947998046875 \tRecord: 5\n",
      "Game 42 \tScore: 1 Time: 0.07490110397338867 \tRecord: 5\n",
      "Game 43 \tScore: 5 Time: 0.45179200172424316 \tRecord: 5\n",
      "Game 44 \tScore: 3 Time: 0.1685490608215332 \tRecord: 5\n",
      "Game 45 \tScore: 7 Time: 0.394946813583374 \tRecord: 7\n",
      "Game 46 \tScore: 2 Time: 0.2154250144958496 \tRecord: 7\n",
      "Game 47 \tScore: 1 Time: 0.0688161849975586 \tRecord: 7\n",
      "Game 48 \tScore: 1 Time: 0.09075736999511719 \tRecord: 7\n",
      "Game 49 \tScore: 2 Time: 0.04986715316772461 \tRecord: 7\n",
      "Game 50 \tScore: 5 Time: 0.1623079776763916 \tRecord: 7\n",
      "Game 51 \tScore: 5 Time: 0.18054461479187012 \tRecord: 7\n",
      "Game 52 \tScore: 9 Time: 0.43546366691589355 \tRecord: 9\n",
      "Game 53 \tScore: 13 Time: 0.7898869514465332 \tRecord: 13\n",
      "Game 54 \tScore: 10 Time: 0.442493200302124 \tRecord: 13\n",
      "Game 55 \tScore: 2 Time: 0.07380199432373047 \tRecord: 13\n",
      "Game 56 \tScore: 2 Time: 0.1476430892944336 \tRecord: 13\n",
      "Game 57 \tScore: 5 Time: 0.30818843841552734 \tRecord: 13\n",
      "Game 58 \tScore: 2 Time: 0.09472990036010742 \tRecord: 13\n",
      "Game 59 \tScore: 3 Time: 0.17322492599487305 \tRecord: 13\n",
      "Game 60 \tScore: 2 Time: 0.04986739158630371 \tRecord: 13\n",
      "Game 61 \tScore: 0 Time: 0.04790067672729492 \tRecord: 13\n",
      "Game 62 \tScore: 3 Time: 0.15960311889648438 \tRecord: 13\n",
      "Game 63 \tScore: 4 Time: 0.3399341106414795 \tRecord: 13\n",
      "Game 64 \tScore: 9 Time: 0.500685453414917 \tRecord: 13\n",
      "Game 65 \tScore: 5 Time: 0.20744609832763672 \tRecord: 13\n",
      "Game 66 \tScore: 5 Time: 0.16537070274353027 \tRecord: 13\n",
      "Game 67 \tScore: 9 Time: 0.37399983406066895 \tRecord: 13\n",
      "Game 68 \tScore: 18 Time: 0.7090914249420166 \tRecord: 18\n",
      "Game 69 \tScore: 8 Time: 0.3836681842803955 \tRecord: 18\n",
      "Game 70 \tScore: 9 Time: 0.35006237030029297 \tRecord: 18\n",
      "Game 71 \tScore: 17 Time: 0.854804277420044 \tRecord: 18\n",
      "Game 72 \tScore: 7 Time: 0.33010244369506836 \tRecord: 18\n",
      "Game 73 \tScore: 24 Time: 1.4144339561462402 \tRecord: 24\n",
      "Game 74 \tScore: 8 Time: 0.3420848846435547 \tRecord: 24\n",
      "Game 75 \tScore: 7 Time: 0.3240773677825928 \tRecord: 24\n",
      "Game 76 \tScore: 20 Time: 0.9502706527709961 \tRecord: 24\n",
      "Game 77 \tScore: 11 Time: 0.529282808303833 \tRecord: 24\n",
      "Game 78 \tScore: 21 Time: 1.0252788066864014 \tRecord: 24\n",
      "Game 79 \tScore: 9 Time: 0.33011817932128906 \tRecord: 24\n",
      "Game 80 \tScore: 3 Time: 0.09363508224487305 \tRecord: 24\n",
      "Game 81 \tScore: 8 Time: 0.30422234535217285 \tRecord: 24\n",
      "Game 82 \tScore: 16 Time: 0.6643352508544922 \tRecord: 24\n",
      "Game 83 \tScore: 5 Time: 0.16256499290466309 \tRecord: 24\n",
      "Game 84 \tScore: 10 Time: 0.33509349822998047 \tRecord: 24\n",
      "Game 85 \tScore: 23 Time: 1.186011552810669 \tRecord: 24\n",
      "Game 86 \tScore: 28 Time: 1.9066059589385986 \tRecord: 28\n",
      "Game 87 \tScore: 18 Time: 1.016479730606079 \tRecord: 28\n",
      "Game 88 \tScore: 18 Time: 1.2105212211608887 \tRecord: 28\n",
      "Game 89 \tScore: 12 Time: 0.461592435836792 \tRecord: 28\n",
      "Game 90 \tScore: 5 Time: 0.31116676330566406 \tRecord: 28\n",
      "Game 91 \tScore: 13 Time: 0.7330377101898193 \tRecord: 28\n",
      "Game 92 \tScore: 28 Time: 1.385307788848877 \tRecord: 28\n",
      "Game 93 \tScore: 26 Time: 1.2561414241790771 \tRecord: 28\n",
      "Game 94 \tScore: 14 Time: 0.5610365867614746 \tRecord: 28\n",
      "Game 95 \tScore: 14 Time: 0.9103114604949951 \tRecord: 28\n",
      "Game 96 \tScore: 14 Time: 0.6313116550445557 \tRecord: 28\n",
      "Game 97 \tScore: 10 Time: 0.30217909812927246 \tRecord: 28\n",
      "Game 98 \tScore: 31 Time: 1.664170742034912 \tRecord: 31\n",
      "Game 99 \tScore: 18 Time: 0.8398237228393555 \tRecord: 31\n",
      "Game 100 \tScore: 19 Time: 1.0520753860473633 \tRecord: 31\n",
      "Game 101 \tScore: 23 Time: 1.0252645015716553 \tRecord: 31\n",
      "Game 102 \tScore: 14 Time: 0.6492905616760254 \tRecord: 31\n",
      "Game 103 \tScore: 35 Time: 1.8483200073242188 \tRecord: 35\n",
      "Game 104 \tScore: 2 Time: 0.06736493110656738 \tRecord: 35\n",
      "Game 105 \tScore: 11 Time: 0.38197922706604004 \tRecord: 35\n",
      "Game 106 \tScore: 25 Time: 1.2316982746124268 \tRecord: 35\n",
      "Game 107 \tScore: 15 Time: 0.5584778785705566 \tRecord: 35\n",
      "Game 108 \tScore: 26 Time: 1.348893165588379 \tRecord: 35\n",
      "Game 109 \tScore: 1 Time: 0.05585932731628418 \tRecord: 35\n",
      "Game 110 \tScore: 22 Time: 1.1441140174865723 \tRecord: 35\n",
      "Game 111 \tScore: 29 Time: 1.5913898944854736 \tRecord: 35\n",
      "Game 112 \tScore: 25 Time: 1.4323835372924805 \tRecord: 35\n",
      "Game 113 \tScore: 30 Time: 1.427872657775879 \tRecord: 35\n",
      "Game 114 \tScore: 5 Time: 0.326127290725708 \tRecord: 35\n",
      "Game 115 \tScore: 11 Time: 0.42948269844055176 \tRecord: 35\n",
      "Game 116 \tScore: 11 Time: 0.5106325149536133 \tRecord: 35\n",
      "Game 117 \tScore: 49 Time: 2.730375289916992 \tRecord: 49\n",
      "Game 118 \tScore: 3 Time: 0.08153963088989258 \tRecord: 49\n",
      "Game 119 \tScore: 9 Time: 0.44779300689697266 \tRecord: 49\n",
      "Game 120 \tScore: 19 Time: 0.9986355304718018 \tRecord: 49\n",
      "Game 121 \tScore: 27 Time: 1.503694772720337 \tRecord: 49\n",
      "Game 122 \tScore: 25 Time: 1.1702885627746582 \tRecord: 49\n",
      "Game 123 \tScore: 39 Time: 1.8515925407409668 \tRecord: 49\n",
      "Game 124 \tScore: 27 Time: 1.3135700225830078 \tRecord: 49\n",
      "Game 125 \tScore: 16 Time: 0.6206731796264648 \tRecord: 49\n",
      "Game 126 \tScore: 39 Time: 2.3806066513061523 \tRecord: 49\n",
      "Game 127 \tScore: 6 Time: 0.231278657913208 \tRecord: 49\n",
      "Game 128 \tScore: 21 Time: 0.9189732074737549 \tRecord: 49\n",
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      "Game 928 \tScore: 22 Time: 1.4106245040893555 \tRecord: 58\n",
      "Game 929 \tScore: 47 Time: 2.7443931102752686 \tRecord: 58\n",
      "Game 930 \tScore: 28 Time: 2.0427603721618652 \tRecord: 58\n",
      "Game 931 \tScore: 17 Time: 1.0909907817840576 \tRecord: 58\n",
      "Game 932 \tScore: 34 Time: 2.102025270462036 \tRecord: 58\n",
      "Game 933 \tScore: 38 Time: 2.3939990997314453 \tRecord: 58\n",
      "Game 934 \tScore: 22 Time: 1.5538315773010254 \tRecord: 58\n",
      "Game 935 \tScore: 6 Time: 0.3258373737335205 \tRecord: 58\n",
      "Game 936 \tScore: 6 Time: 0.4756510257720947 \tRecord: 58\n",
      "Game 937 \tScore: 17 Time: 0.8946259021759033 \tRecord: 58\n",
      "Game 938 \tScore: 35 Time: 2.203179121017456 \tRecord: 58\n",
      "Game 939 \tScore: 2 Time: 0.1296546459197998 \tRecord: 58\n",
      "Game 940 \tScore: 16 Time: 1.1092441082000732 \tRecord: 58\n",
      "Game 941 \tScore: 41 Time: 2.7883269786834717 \tRecord: 58\n",
      "Game 942 \tScore: 14 Time: 0.9704020023345947 \tRecord: 58\n",
      "Game 943 \tScore: 30 Time: 1.9494152069091797 \tRecord: 58\n",
      "Game 944 \tScore: 14 Time: 0.9723973274230957 \tRecord: 58\n",
      "Game 945 \tScore: 29 Time: 2.146921396255493 \tRecord: 58\n",
      "Game 946 \tScore: 2 Time: 0.038907527923583984 \tRecord: 58\n",
      "Game 947 \tScore: 34 Time: 2.0946428775787354 \tRecord: 58\n",
      "Game 948 \tScore: 39 Time: 2.7655794620513916 \tRecord: 58\n",
      "Game 949 \tScore: 2 Time: 0.0608363151550293 \tRecord: 58\n",
      "Game 950 \tScore: 29 Time: 1.7023506164550781 \tRecord: 58\n",
      "Game 951 \tScore: 33 Time: 2.147745132446289 \tRecord: 58\n",
      "Game 952 \tScore: 13 Time: 0.6800887584686279 \tRecord: 58\n",
      "Game 953 \tScore: 26 Time: 2.052945852279663 \tRecord: 58\n",
      "Game 954 \tScore: 17 Time: 1.0634634494781494 \tRecord: 58\n",
      "Game 955 \tScore: 3 Time: 0.12366890907287598 \tRecord: 58\n",
      "Game 956 \tScore: 26 Time: 1.811824083328247 \tRecord: 58\n",
      "Game 957 \tScore: 44 Time: 2.6491475105285645 \tRecord: 58\n",
      "Game 958 \tScore: 40 Time: 2.841434955596924 \tRecord: 58\n",
      "Game 959 \tScore: 8 Time: 0.48572421073913574 \tRecord: 58\n",
      "Game 960 \tScore: 23 Time: 1.4947197437286377 \tRecord: 58\n",
      "Game 961 \tScore: 43 Time: 2.3859617710113525 \tRecord: 58\n",
      "Game 962 \tScore: 21 Time: 1.3208527565002441 \tRecord: 58\n",
      "Game 963 \tScore: 9 Time: 0.4763758182525635 \tRecord: 58\n",
      "Game 964 \tScore: 25 Time: 1.671403408050537 \tRecord: 58\n",
      "Game 965 \tScore: 22 Time: 1.2518489360809326 \tRecord: 58\n",
      "Game 966 \tScore: 31 Time: 2.729086399078369 \tRecord: 58\n",
      "Game 967 \tScore: 41 Time: 2.875380516052246 \tRecord: 58\n",
      "Game 968 \tScore: 31 Time: 2.0792315006256104 \tRecord: 58\n",
      "Game 969 \tScore: 24 Time: 1.146944284439087 \tRecord: 58\n",
      "Game 970 \tScore: 23 Time: 1.1649136543273926 \tRecord: 58\n",
      "Game 971 \tScore: 24 Time: 1.5890295505523682 \tRecord: 58\n",
      "Game 972 \tScore: 36 Time: 2.713047504425049 \tRecord: 58\n",
      "Game 973 \tScore: 35 Time: 1.9747912883758545 \tRecord: 58\n",
      "Game 974 \tScore: 36 Time: 1.8845269680023193 \tRecord: 58\n",
      "Game 975 \tScore: 18 Time: 1.2910678386688232 \tRecord: 58\n",
      "Game 976 \tScore: 2 Time: 0.07362794876098633 \tRecord: 58\n",
      "Game 977 \tScore: 35 Time: 2.507511854171753 \tRecord: 58\n",
      "Game 978 \tScore: 19 Time: 1.3462438583374023 \tRecord: 58\n",
      "Game 979 \tScore: 18 Time: 1.5631062984466553 \tRecord: 58\n",
      "Game 980 \tScore: 31 Time: 1.714231252670288 \tRecord: 58\n",
      "Game 981 \tScore: 32 Time: 1.7529783248901367 \tRecord: 58\n",
      "Game 982 \tScore: 30 Time: 2.146268844604492 \tRecord: 58\n",
      "Game 983 \tScore: 10 Time: 0.4247100353240967 \tRecord: 58\n",
      "Game 984 \tScore: 34 Time: 2.180070400238037 \tRecord: 58\n",
      "Game 985 \tScore: 44 Time: 2.884349822998047 \tRecord: 58\n",
      "Game 986 \tScore: 28 Time: 1.971271276473999 \tRecord: 58\n",
      "Game 987 \tScore: 20 Time: 1.181213617324829 \tRecord: 58\n",
      "Game 988 \tScore: 13 Time: 0.6093380451202393 \tRecord: 58\n",
      "Game 989 \tScore: 30 Time: 2.3121471405029297 \tRecord: 58\n",
      "Game 990 \tScore: 6 Time: 0.45378661155700684 \tRecord: 58\n",
      "Game 991 \tScore: 17 Time: 1.0569884777069092 \tRecord: 58\n",
      "Game 992 \tScore: 1 Time: 0.06283164024353027 \tRecord: 58\n",
      "Game 993 \tScore: 3 Time: 0.17710304260253906 \tRecord: 58\n",
      "Game 994 \tScore: 21 Time: 1.0997347831726074 \tRecord: 58\n",
      "Game 995 \tScore: 29 Time: 2.1537694931030273 \tRecord: 58\n",
      "Game 996 \tScore: 26 Time: 1.3455288410186768 \tRecord: 58\n",
      "Game 997 \tScore: 34 Time: 2.283949613571167 \tRecord: 58\n",
      "Game 998 \tScore: 15 Time: 1.0041766166687012 \tRecord: 58\n",
      "Game 999 \tScore: 31 Time: 1.911525011062622 \tRecord: 58\n",
      "Game 1000 \tScore: 6 Time: 0.314237117767334 \tRecord: 58\n"
     ]
    }
   ],
   "source": [
    "# Ejecución del código original modificado en densidad\n",
    "mod_scores, mod_times = run(agent = ModifiedDQNAgent(),games = 1000, weights_file = \"mod_weights.hdf5\",verbose = True)\n",
    "\n",
    "# Store/Load\n",
    "np.save(\"mod_scores\", mod_scores)\n",
    "np.save(\"mod_times\", mod_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Game 1 \tScore: 1 Time: 0.4240610599517822 \tRecord: 1\n",
      "Game 2 \tScore: 0 Time: 0.03789949417114258 \tRecord: 1\n",
      "Game 3 \tScore: 1 Time: 0.44281506538391113 \tRecord: 1\n",
      "Game 4 \tScore: 0 Time: 0.1462116241455078 \tRecord: 1\n",
      "Game 5 \tScore: 1 Time: 0.08676767349243164 \tRecord: 1\n",
      "Game 6 \tScore: 0 Time: 0.04886960983276367 \tRecord: 1\n",
      "Game 7 \tScore: 1 Time: 0.05089449882507324 \tRecord: 1\n",
      "Game 8 \tScore: 1 Time: 0.05086374282836914 \tRecord: 1\n",
      "Game 9 \tScore: 0 Time: 0.02593064308166504 \tRecord: 1\n",
      "Game 10 \tScore: 1 Time: 0.05189037322998047 \tRecord: 1\n",
      "Game 11 \tScore: 1 Time: 0.18450617790222168 \tRecord: 1\n",
      "Game 12 \tScore: 0 Time: 0.14757657051086426 \tRecord: 1\n",
      "Game 13 \tScore: 1 Time: 0.2912425994873047 \tRecord: 1\n",
      "Game 14 \tScore: 1 Time: 0.059839725494384766 \tRecord: 1\n",
      "Game 15 \tScore: 1 Time: 0.11672401428222656 \tRecord: 1\n",
      "Game 16 \tScore: 1 Time: 0.03889584541320801 \tRecord: 1\n",
      "Game 17 \tScore: 1 Time: 0.12980198860168457 \tRecord: 1\n",
      "Game 18 \tScore: 2 Time: 0.10172891616821289 \tRecord: 2\n",
      "Game 19 \tScore: 1 Time: 0.2712442874908447 \tRecord: 2\n",
      "Game 20 \tScore: 5 Time: 0.17854976654052734 \tRecord: 5\n",
      "Game 21 \tScore: 5 Time: 0.2742781639099121 \tRecord: 5\n",
      "Game 22 \tScore: 0 Time: 0.15880656242370605 \tRecord: 5\n",
      "Game 23 \tScore: 2 Time: 0.13164854049682617 \tRecord: 5\n",
      "Game 24 \tScore: 0 Time: 0.10671067237854004 \tRecord: 5\n",
      "Game 25 \tScore: 2 Time: 0.19348883628845215 \tRecord: 5\n",
      "Game 26 \tScore: 3 Time: 0.13663482666015625 \tRecord: 5\n",
      "Game 27 \tScore: 3 Time: 0.11707472801208496 \tRecord: 5\n",
      "Game 28 \tScore: 0 Time: 0.0747990608215332 \tRecord: 5\n",
      "Game 29 \tScore: 1 Time: 0.0629112720489502 \tRecord: 5\n",
      "Game 30 \tScore: 3 Time: 0.28150177001953125 \tRecord: 5\n",
      "Game 31 \tScore: 3 Time: 0.2344043254852295 \tRecord: 5\n",
      "Game 32 \tScore: 2 Time: 0.13164806365966797 \tRecord: 5\n",
      "Game 33 \tScore: 3 Time: 0.13560771942138672 \tRecord: 5\n",
      "Game 34 \tScore: 1 Time: 0.06184220314025879 \tRecord: 5\n",
      "Game 35 \tScore: 1 Time: 0.04188871383666992 \tRecord: 5\n",
      "Game 36 \tScore: 3 Time: 0.27124905586242676 \tRecord: 5\n",
      "Game 37 \tScore: 2 Time: 0.31911587715148926 \tRecord: 5\n",
      "Game 38 \tScore: 1 Time: 0.07277607917785645 \tRecord: 5\n",
      "Game 39 \tScore: 2 Time: 0.31718015670776367 \tRecord: 5\n",
      "Game 40 \tScore: 7 Time: 0.45874619483947754 \tRecord: 7\n",
      "Game 41 \tScore: 1 Time: 0.05385589599609375 \tRecord: 7\n",
      "Game 42 \tScore: 7 Time: 0.22436976432800293 \tRecord: 7\n",
      "Game 43 \tScore: 1 Time: 0.05681633949279785 \tRecord: 7\n",
      "Game 44 \tScore: 3 Time: 0.10073161125183105 \tRecord: 7\n",
      "Game 45 \tScore: 3 Time: 0.14261794090270996 \tRecord: 7\n",
      "Game 46 \tScore: 1 Time: 0.22939705848693848 \tRecord: 7\n",
      "Game 47 \tScore: 4 Time: 0.15555405616760254 \tRecord: 7\n",
      "Game 48 \tScore: 3 Time: 0.15159368515014648 \tRecord: 7\n",
      "Game 49 \tScore: 9 Time: 0.4512214660644531 \tRecord: 9\n",
      "Game 50 \tScore: 1 Time: 0.04391217231750488 \tRecord: 9\n",
      "Game 51 \tScore: 7 Time: 0.4790463447570801 \tRecord: 9\n",
      "Game 52 \tScore: 4 Time: 0.2932150363922119 \tRecord: 9\n",
      "Game 53 \tScore: 13 Time: 0.8964519500732422 \tRecord: 13\n",
      "Game 54 \tScore: 4 Time: 0.1206817626953125 \tRecord: 13\n",
      "Game 55 \tScore: 1 Time: 0.04731869697570801 \tRecord: 13\n",
      "Game 56 \tScore: 2 Time: 0.14757513999938965 \tRecord: 13\n",
      "Game 57 \tScore: 3 Time: 0.32294225692749023 \tRecord: 13\n",
      "Game 58 \tScore: 3 Time: 0.14362215995788574 \tRecord: 13\n",
      "Game 59 \tScore: 15 Time: 0.7786307334899902 \tRecord: 15\n",
      "Game 60 \tScore: 4 Time: 0.275409460067749 \tRecord: 15\n",
      "Game 61 \tScore: 2 Time: 0.16050386428833008 \tRecord: 15\n",
      "Game 62 \tScore: 1 Time: 0.07679581642150879 \tRecord: 15\n",
      "Game 63 \tScore: 5 Time: 0.20943975448608398 \tRecord: 15\n",
      "Game 64 \tScore: 3 Time: 0.4448256492614746 \tRecord: 15\n",
      "Game 65 \tScore: 1 Time: 0.03992581367492676 \tRecord: 15\n",
      "Game 66 \tScore: 6 Time: 0.35454583168029785 \tRecord: 15\n",
      "Game 67 \tScore: 4 Time: 0.3919403553009033 \tRecord: 15\n",
      "Game 68 \tScore: 4 Time: 0.13959622383117676 \tRecord: 15\n",
      "Game 69 \tScore: 11 Time: 0.6427724361419678 \tRecord: 15\n",
      "Game 70 \tScore: 12 Time: 0.37964749336242676 \tRecord: 15\n",
      "Game 71 \tScore: 15 Time: 0.7594308853149414 \tRecord: 15\n",
      "Game 72 \tScore: 11 Time: 0.6263248920440674 \tRecord: 15\n",
      "Game 73 \tScore: 25 Time: 1.0652220249176025 \tRecord: 25\n",
      "Game 74 \tScore: 13 Time: 0.9475245475769043 \tRecord: 25\n",
      "Game 75 \tScore: 26 Time: 1.548184871673584 \tRecord: 26\n",
      "Game 76 \tScore: 3 Time: 0.2074875831604004 \tRecord: 26\n",
      "Game 77 \tScore: 7 Time: 0.4984714984893799 \tRecord: 26\n",
      "Game 78 \tScore: 35 Time: 2.154573678970337 \tRecord: 35\n",
      "Game 79 \tScore: 7 Time: 0.2154233455657959 \tRecord: 35\n",
      "Game 80 \tScore: 19 Time: 1.0147316455841064 \tRecord: 35\n",
      "Game 81 \tScore: 7 Time: 0.4318428039550781 \tRecord: 35\n",
      "Game 82 \tScore: 20 Time: 1.377915382385254 \tRecord: 35\n",
      "Game 83 \tScore: 18 Time: 1.394578456878662 \tRecord: 35\n",
      "Game 84 \tScore: 4 Time: 0.17852187156677246 \tRecord: 35\n",
      "Game 85 \tScore: 7 Time: 0.26930904388427734 \tRecord: 35\n",
      "Game 86 \tScore: 39 Time: 1.8486731052398682 \tRecord: 39\n",
      "Game 87 \tScore: 2 Time: 0.06282496452331543 \tRecord: 39\n",
      "Game 88 \tScore: 29 Time: 1.2124571800231934 \tRecord: 39\n",
      "Game 89 \tScore: 33 Time: 1.929983139038086 \tRecord: 39\n",
      "Game 90 \tScore: 4 Time: 0.1436455249786377 \tRecord: 39\n",
      "Game 91 \tScore: 6 Time: 0.323519229888916 \tRecord: 39\n",
      "Game 92 \tScore: 3 Time: 0.19052457809448242 \tRecord: 39\n",
      "Game 93 \tScore: 22 Time: 1.224226474761963 \tRecord: 39\n",
      "Game 94 \tScore: 7 Time: 0.13197803497314453 \tRecord: 39\n",
      "Game 95 \tScore: 20 Time: 0.7310142517089844 \tRecord: 39\n",
      "Game 96 \tScore: 12 Time: 0.4246203899383545 \tRecord: 39\n",
      "Game 97 \tScore: 5 Time: 0.32065510749816895 \tRecord: 39\n",
      "Game 98 \tScore: 30 Time: 1.1618423461914062 \tRecord: 39\n",
      "Game 99 \tScore: 22 Time: 1.0216286182403564 \tRecord: 39\n",
      "Game 100 \tScore: 18 Time: 0.9513745307922363 \tRecord: 39\n",
      "Game 101 \tScore: 18 Time: 0.913332462310791 \tRecord: 39\n",
      "Game 102 \tScore: 18 Time: 1.1361782550811768 \tRecord: 39\n",
      "Game 103 \tScore: 3 Time: 0.22041082382202148 \tRecord: 39\n",
      "Game 104 \tScore: 34 Time: 1.768846035003662 \tRecord: 39\n",
      "Game 105 \tScore: 10 Time: 0.4341850280761719 \tRecord: 39\n",
      "Game 106 \tScore: 8 Time: 0.3041861057281494 \tRecord: 39\n",
      "Game 107 \tScore: 35 Time: 1.6502752304077148 \tRecord: 39\n",
      "Game 108 \tScore: 23 Time: 1.121889352798462 \tRecord: 39\n",
      "Game 109 \tScore: 24 Time: 0.9376349449157715 \tRecord: 39\n",
      "Game 110 \tScore: 33 Time: 1.61324143409729 \tRecord: 39\n",
      "Game 111 \tScore: 5 Time: 0.07383084297180176 \tRecord: 39\n",
      "Game 112 \tScore: 21 Time: 1.1742331981658936 \tRecord: 39\n",
      "Game 113 \tScore: 29 Time: 1.6268250942230225 \tRecord: 39\n",
      "Game 114 \tScore: 26 Time: 1.7471435070037842 \tRecord: 39\n",
      "Game 115 \tScore: 30 Time: 1.1672062873840332 \tRecord: 39\n",
      "Game 116 \tScore: 3 Time: 0.1398303508758545 \tRecord: 39\n",
      "Game 117 \tScore: 25 Time: 0.8325951099395752 \tRecord: 39\n",
      "Game 118 \tScore: 35 Time: 1.7824444770812988 \tRecord: 39\n",
      "Game 119 \tScore: 19 Time: 0.9315075874328613 \tRecord: 39\n",
      "Game 120 \tScore: 16 Time: 0.6187293529510498 \tRecord: 39\n",
      "Game 121 \tScore: 21 Time: 0.8068413734436035 \tRecord: 39\n",
      "Game 122 \tScore: 14 Time: 0.5065832138061523 \tRecord: 39\n",
      "Game 123 \tScore: 13 Time: 0.4098985195159912 \tRecord: 39\n",
      "Game 124 \tScore: 38 Time: 1.594900131225586 \tRecord: 39\n",
      "Game 125 \tScore: 24 Time: 1.1198625564575195 \tRecord: 39\n",
      "Game 126 \tScore: 41 Time: 2.1251986026763916 \tRecord: 41\n",
      "Game 127 \tScore: 28 Time: 1.416057825088501 \tRecord: 41\n",
      "Game 128 \tScore: 24 Time: 1.033918857574463 \tRecord: 41\n",
      "Game 129 \tScore: 28 Time: 1.2473266124725342 \tRecord: 41\n",
      "Game 130 \tScore: 9 Time: 0.297135591506958 \tRecord: 41\n",
      "Game 131 \tScore: 38 Time: 1.810420274734497 \tRecord: 41\n",
      "Game 132 \tScore: 13 Time: 0.42489171028137207 \tRecord: 41\n",
      "Game 133 \tScore: 36 Time: 1.7921416759490967 \tRecord: 41\n",
      "Game 134 \tScore: 40 Time: 1.9968476295471191 \tRecord: 41\n",
      "Game 135 \tScore: 29 Time: 1.2419826984405518 \tRecord: 41\n",
      "Game 136 \tScore: 40 Time: 1.753441572189331 \tRecord: 41\n",
      "Game 137 \tScore: 29 Time: 1.2309622764587402 \tRecord: 41\n",
      "Game 138 \tScore: 24 Time: 0.8853845596313477 \tRecord: 41\n",
      "Game 139 \tScore: 38 Time: 1.7750396728515625 \tRecord: 41\n",
      "Game 140 \tScore: 31 Time: 1.1952402591705322 \tRecord: 41\n",
      "Game 141 \tScore: 35 Time: 1.7600297927856445 \tRecord: 41\n",
      "Game 142 \tScore: 31 Time: 1.4438459873199463 \tRecord: 41\n",
      "Game 143 \tScore: 31 Time: 1.1530847549438477 \tRecord: 41\n",
      "Game 144 \tScore: 33 Time: 1.5421862602233887 \tRecord: 41\n",
      "Game 145 \tScore: 13 Time: 0.7676272392272949 \tRecord: 41\n",
      "Game 146 \tScore: 15 Time: 0.6941421031951904 \tRecord: 41\n",
      "Game 147 \tScore: 22 Time: 1.0730171203613281 \tRecord: 41\n",
      "Game 148 \tScore: 19 Time: 0.58894944190979 \tRecord: 41\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Game 149 \tScore: 23 Time: 0.9425773620605469 \tRecord: 41\n",
      "Game 150 \tScore: 50 Time: 2.5794200897216797 \tRecord: 50\n",
      "Game 151 \tScore: 29 Time: 1.1250317096710205 \tRecord: 50\n",
      "Game 152 \tScore: 33 Time: 1.5294885635375977 \tRecord: 50\n",
      "Game 153 \tScore: 49 Time: 2.0174429416656494 \tRecord: 50\n",
      "Game 154 \tScore: 39 Time: 1.8625736236572266 \tRecord: 50\n",
      "Game 155 \tScore: 39 Time: 2.0995490550994873 \tRecord: 50\n",
      "Game 156 \tScore: 36 Time: 1.6954691410064697 \tRecord: 50\n",
      "Game 157 \tScore: 26 Time: 1.1021416187286377 \tRecord: 50\n",
      "Game 158 \tScore: 29 Time: 1.447420358657837 \tRecord: 50\n",
      "Game 159 \tScore: 20 Time: 1.0701589584350586 \tRecord: 50\n",
      "Game 160 \tScore: 14 Time: 0.4834866523742676 \tRecord: 50\n",
      "Game 161 \tScore: 18 Time: 0.6422328948974609 \tRecord: 50\n",
      "Game 162 \tScore: 17 Time: 0.7707779407501221 \tRecord: 50\n",
      "Game 163 \tScore: 25 Time: 1.2350387573242188 \tRecord: 50\n",
      "Game 164 \tScore: 10 Time: 0.4408442974090576 \tRecord: 50\n",
      "Game 165 \tScore: 33 Time: 1.7561984062194824 \tRecord: 50\n",
      "Game 166 \tScore: 45 Time: 1.804743766784668 \tRecord: 50\n",
      "Game 167 \tScore: 30 Time: 1.3012888431549072 \tRecord: 50\n",
      "Game 168 \tScore: 15 Time: 0.711097240447998 \tRecord: 50\n",
      "Game 169 \tScore: 28 Time: 1.4938209056854248 \tRecord: 50\n",
      "Game 170 \tScore: 24 Time: 1.413336992263794 \tRecord: 50\n",
      "Game 171 \tScore: 42 Time: 2.19299054145813 \tRecord: 50\n",
      "Game 172 \tScore: 4 Time: 0.1087028980255127 \tRecord: 50\n",
      "Game 173 \tScore: 31 Time: 1.4077262878417969 \tRecord: 50\n",
      "Game 174 \tScore: 5 Time: 0.18382024765014648 \tRecord: 50\n",
      "Game 175 \tScore: 24 Time: 1.2087650299072266 \tRecord: 50\n",
      "Game 176 \tScore: 25 Time: 0.9717826843261719 \tRecord: 50\n",
      "Game 177 \tScore: 38 Time: 1.5171136856079102 \tRecord: 50\n",
      "Game 178 \tScore: 15 Time: 0.5275881290435791 \tRecord: 50\n",
      "Game 179 \tScore: 32 Time: 1.1998023986816406 \tRecord: 50\n",
      "Game 180 \tScore: 1 Time: 0.030887603759765625 \tRecord: 50\n",
      "Game 181 \tScore: 18 Time: 0.8510053157806396 \tRecord: 50\n",
      "Game 182 \tScore: 41 Time: 1.9535903930664062 \tRecord: 50\n",
      "Game 183 \tScore: 14 Time: 0.41684436798095703 \tRecord: 50\n",
      "Game 184 \tScore: 26 Time: 0.9744975566864014 \tRecord: 50\n",
      "Game 185 \tScore: 26 Time: 1.095473289489746 \tRecord: 50\n",
      "Game 186 \tScore: 18 Time: 0.7095763683319092 \tRecord: 50\n",
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      "Game 983 \tScore: 6 Time: 0.2433476448059082 \tRecord: 59\n",
      "Game 984 \tScore: 17 Time: 0.6120896339416504 \tRecord: 59\n",
      "Game 985 \tScore: 1 Time: 0.02892327308654785 \tRecord: 59\n",
      "Game 986 \tScore: 29 Time: 2.26155161857605 \tRecord: 59\n",
      "Game 987 \tScore: 10 Time: 0.4736473560333252 \tRecord: 59\n",
      "Game 988 \tScore: 9 Time: 0.2892742156982422 \tRecord: 59\n",
      "Game 989 \tScore: 3 Time: 0.08976030349731445 \tRecord: 59\n",
      "Game 990 \tScore: 14 Time: 0.6283178329467773 \tRecord: 59\n",
      "Game 991 \tScore: 17 Time: 0.8118336200714111 \tRecord: 59\n",
      "Game 992 \tScore: 13 Time: 0.5645809173583984 \tRecord: 59\n",
      "Game 993 \tScore: 4 Time: 0.14561128616333008 \tRecord: 59\n",
      "Game 994 \tScore: 3 Time: 0.07782363891601562 \tRecord: 59\n",
      "Game 995 \tScore: 13 Time: 0.4448094367980957 \tRecord: 59\n",
      "Game 996 \tScore: 6 Time: 0.2543201446533203 \tRecord: 59\n",
      "Game 997 \tScore: 17 Time: 0.7518250942230225 \tRecord: 59\n",
      "Game 998 \tScore: 4 Time: 0.13164806365966797 \tRecord: 59\n",
      "Game 999 \tScore: 1 Time: 0.029919147491455078 \tRecord: 59\n",
      "Game 1000 \tScore: 18 Time: 0.9290075302124023 \tRecord: 59\n"
     ]
    }
   ],
   "source": [
    "# Ejecución del código estructura de piramide\n",
    "pyr_scores, pyr_times = run(agent = PyramidDQNAgent(),games = 1000, weights_file = \"pyr_weights.hdf5\",verbose = True)\n",
    "\n",
    "# Store/Load\n",
    "np.save(\"pyr_scores\", pyr_scores)\n",
    "np.save(\"pyr_times\", pyr_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Game 1 \tScore: 1 Time: 0.07978630065917969 \tRecord: 1\n",
      "Game 2 \tScore: 0 Time: 0.07755661010742188 \tRecord: 1\n",
      "Game 3 \tScore: 0 Time: 0.06996536254882812 \tRecord: 1\n",
      "Game 4 \tScore: 0 Time: 0.11272168159484863 \tRecord: 1\n",
      "Game 5 \tScore: 0 Time: 0.09717011451721191 \tRecord: 1\n",
      "Game 6 \tScore: 0 Time: 0.04886651039123535 \tRecord: 1\n",
      "Game 7 \tScore: 0 Time: 0.1685488224029541 \tRecord: 1\n",
      "Game 8 \tScore: 0 Time: 0.03289222717285156 \tRecord: 1\n",
      "Game 9 \tScore: 0 Time: 0.029920339584350586 \tRecord: 1\n",
      "Game 10 \tScore: 0 Time: 0.03191423416137695 \tRecord: 1\n",
      "Game 11 \tScore: 0 Time: 0.07579612731933594 \tRecord: 1\n",
      "Game 12 \tScore: 0 Time: 0.08873295783996582 \tRecord: 1\n",
      "Game 13 \tScore: 0 Time: 0.34320950508117676 \tRecord: 1\n",
      "Game 14 \tScore: 0 Time: 0.09078812599182129 \tRecord: 1\n",
      "Game 15 \tScore: 0 Time: 0.05285787582397461 \tRecord: 1\n",
      "Game 16 \tScore: 0 Time: 0.13663363456726074 \tRecord: 1\n",
      "Game 17 \tScore: 1 Time: 0.04587674140930176 \tRecord: 1\n",
      "Game 18 \tScore: 0 Time: 0.20046424865722656 \tRecord: 1\n",
      "Game 19 \tScore: 1 Time: 0.2270519733428955 \tRecord: 1\n",
      "Game 20 \tScore: 1 Time: 0.34898900985717773 \tRecord: 1\n",
      "Game 21 \tScore: 1 Time: 0.16752052307128906 \tRecord: 1\n",
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      "Game 23 \tScore: 1 Time: 0.1077115535736084 \tRecord: 1\n",
      "Game 24 \tScore: 2 Time: 0.10571670532226562 \tRecord: 2\n",
      "Game 25 \tScore: 1 Time: 0.041896820068359375 \tRecord: 2\n",
      "Game 26 \tScore: 1 Time: 0.11759161949157715 \tRecord: 2\n",
      "Game 27 \tScore: 2 Time: 0.05884265899658203 \tRecord: 2\n",
      "Game 28 \tScore: 1 Time: 0.11369490623474121 \tRecord: 2\n",
      "Game 29 \tScore: 1 Time: 0.0967416763305664 \tRecord: 2\n",
      "Game 30 \tScore: 1 Time: 0.10870909690856934 \tRecord: 2\n",
      "Game 31 \tScore: 1 Time: 0.05984067916870117 \tRecord: 2\n",
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      "Game 33 \tScore: 3 Time: 0.06183981895446777 \tRecord: 3\n",
      "Game 34 \tScore: 1 Time: 0.06881451606750488 \tRecord: 3\n",
      "Game 35 \tScore: 1 Time: 0.08228492736816406 \tRecord: 3\n",
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      "Game 37 \tScore: 0 Time: 0.04487967491149902 \tRecord: 3\n",
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      "Game 39 \tScore: 2 Time: 0.12344098091125488 \tRecord: 3\n",
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      "Game 88 \tScore: 34 Time: 1.9224798679351807 \tRecord: 40\n",
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      "Game 106 \tScore: 13 Time: 0.9643707275390625 \tRecord: 40\n",
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      "Game 126 \tScore: 11 Time: 0.6747682094573975 \tRecord: 40\n",
      "Game 127 \tScore: 29 Time: 1.7839415073394775 \tRecord: 40\n",
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      "Game 148 \tScore: 9 Time: 1.440173625946045 \tRecord: 40\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Game 149 \tScore: 26 Time: 1.2980196475982666 \tRecord: 40\n",
      "Game 150 \tScore: 12 Time: 0.779247522354126 \tRecord: 40\n",
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      "Game 228 \tScore: 26 Time: 1.5701379776000977 \tRecord: 40\n",
      "Game 229 \tScore: 3 Time: 0.45976924896240234 \tRecord: 40\n",
      "Game 230 \tScore: 7 Time: 1.0606918334960938 \tRecord: 40\n",
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      "Game 237 \tScore: 8 Time: 1.047290325164795 \tRecord: 46\n",
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      "Game 239 \tScore: 32 Time: 1.851661205291748 \tRecord: 46\n",
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     ]
    }
   ],
   "source": [
    "# Ejecución del código estructura de piramide (low dropout)\n",
    "pyrl_scores, pyrl_times = run(agent = PyramidLowDQNAgent(),games = 1000, weights_file = \"pyrl_weights.hdf5\",verbose = True)\n",
    "\n",
    "# Store/Load\n",
    "np.save(\"pyrl_scores\", pyrl_scores)\n",
    "np.save(\"pyrl_times\", pyrl_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0917 11:08:54.610852  9424 deprecation.py:323] From C:\\Users\\FMJabato\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Game 834 \tScore: 8 Time: 1.0994415283203125 \tRecord: 45\n",
      "Game 835 \tScore: 32 Time: 4.144623517990112 \tRecord: 45\n",
      "Game 836 \tScore: 19 Time: 2.7333099842071533 \tRecord: 45\n",
      "Game 837 \tScore: 17 Time: 2.2867071628570557 \tRecord: 45\n",
      "Game 838 \tScore: 31 Time: 4.5191428661346436 \tRecord: 45\n",
      "Game 839 \tScore: 22 Time: 2.8606107234954834 \tRecord: 45\n",
      "Game 840 \tScore: 16 Time: 1.860914945602417 \tRecord: 45\n",
      "Game 841 \tScore: 9 Time: 0.7900247573852539 \tRecord: 45\n",
      "Game 842 \tScore: 16 Time: 1.655547857284546 \tRecord: 45\n",
      "Game 843 \tScore: 6 Time: 0.8506827354431152 \tRecord: 45\n",
      "Game 844 \tScore: 20 Time: 2.438065767288208 \tRecord: 45\n",
      "Game 845 \tScore: 29 Time: 4.9452362060546875 \tRecord: 45\n",
      "Game 846 \tScore: 22 Time: 3.0743489265441895 \tRecord: 45\n",
      "Game 847 \tScore: 30 Time: 4.608685731887817 \tRecord: 45\n",
      "Game 848 \tScore: 27 Time: 3.8882994651794434 \tRecord: 45\n",
      "Game 849 \tScore: 24 Time: 3.918379545211792 \tRecord: 45\n",
      "Game 850 \tScore: 19 Time: 2.1653285026550293 \tRecord: 45\n",
      "Game 851 \tScore: 9 Time: 1.5684146881103516 \tRecord: 45\n",
      "Game 852 \tScore: 25 Time: 4.465779542922974 \tRecord: 45\n",
      "Game 853 \tScore: 14 Time: 2.1269686222076416 \tRecord: 45\n",
      "Game 854 \tScore: 8 Time: 1.1592974662780762 \tRecord: 45\n",
      "Game 855 \tScore: 13 Time: 1.4974877834320068 \tRecord: 45\n",
      "Game 856 \tScore: 26 Time: 4.128469467163086 \tRecord: 45\n",
      "Game 857 \tScore: 18 Time: 1.8991034030914307 \tRecord: 45\n",
      "Game 858 \tScore: 26 Time: 3.7656235694885254 \tRecord: 45\n",
      "Game 859 \tScore: 18 Time: 2.3587992191314697 \tRecord: 45\n",
      "Game 860 \tScore: 7 Time: 1.11509108543396 \tRecord: 45\n",
      "Game 861 \tScore: 15 Time: 1.8010802268981934 \tRecord: 45\n",
      "Game 862 \tScore: 34 Time: 4.731063604354858 \tRecord: 45\n",
      "Game 863 \tScore: 17 Time: 1.723175287246704 \tRecord: 45\n",
      "Game 864 \tScore: 17 Time: 2.152625799179077 \tRecord: 45\n",
      "Game 865 \tScore: 20 Time: 2.7375736236572266 \tRecord: 45\n",
      "Game 866 \tScore: 12 Time: 1.4718294143676758 \tRecord: 45\n",
      "Game 867 \tScore: 37 Time: 5.394178628921509 \tRecord: 45\n",
      "Game 868 \tScore: 26 Time: 2.6345067024230957 \tRecord: 45\n",
      "Game 869 \tScore: 25 Time: 3.1986489295959473 \tRecord: 45\n",
      "Game 870 \tScore: 24 Time: 3.0346620082855225 \tRecord: 45\n",
      "Game 871 \tScore: 22 Time: 3.950789451599121 \tRecord: 45\n",
      "Game 872 \tScore: 25 Time: 3.866781711578369 \tRecord: 45\n",
      "Game 873 \tScore: 23 Time: 2.7527825832366943 \tRecord: 45\n",
      "Game 874 \tScore: 20 Time: 2.375074863433838 \tRecord: 45\n",
      "Game 875 \tScore: 17 Time: 2.0042643547058105 \tRecord: 45\n",
      "Game 876 \tScore: 21 Time: 2.670309543609619 \tRecord: 45\n",
      "Game 877 \tScore: 19 Time: 2.9271605014801025 \tRecord: 45\n",
      "Game 878 \tScore: 20 Time: 3.2529923915863037 \tRecord: 45\n",
      "Game 879 \tScore: 16 Time: 2.685523271560669 \tRecord: 45\n",
      "Game 880 \tScore: 16 Time: 2.222041368484497 \tRecord: 45\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Game 881 \tScore: 18 Time: 2.7092878818511963 \tRecord: 45\n",
      "Game 882 \tScore: 26 Time: 3.6270222663879395 \tRecord: 45\n",
      "Game 883 \tScore: 31 Time: 4.102670669555664 \tRecord: 45\n",
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      "Game 885 \tScore: 25 Time: 3.692551612854004 \tRecord: 45\n",
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      "Game 888 \tScore: 21 Time: 3.883084774017334 \tRecord: 45\n",
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      "Game 890 \tScore: 22 Time: 3.2333855628967285 \tRecord: 45\n",
      "Game 891 \tScore: 12 Time: 3.359248638153076 \tRecord: 45\n",
      "Game 892 \tScore: 26 Time: 3.630970001220703 \tRecord: 45\n",
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      "Game 898 \tScore: 49 Time: 8.063076734542847 \tRecord: 49\n",
      "Game 899 \tScore: 32 Time: 4.734453916549683 \tRecord: 49\n",
      "Game 900 \tScore: 13 Time: 1.8432865142822266 \tRecord: 49\n",
      "Game 901 \tScore: 18 Time: 2.130136251449585 \tRecord: 49\n",
      "Game 902 \tScore: 20 Time: 2.354912519454956 \tRecord: 49\n",
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      "Game 914 \tScore: 18 Time: 2.344414710998535 \tRecord: 49\n",
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      "Game 916 \tScore: 38 Time: 5.29295539855957 \tRecord: 49\n",
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      "Game 921 \tScore: 30 Time: 4.048698902130127 \tRecord: 49\n",
      "Game 922 \tScore: 30 Time: 4.154110431671143 \tRecord: 49\n",
      "Game 923 \tScore: 6 Time: 0.8387715816497803 \tRecord: 49\n",
      "Game 924 \tScore: 21 Time: 3.3499608039855957 \tRecord: 49\n",
      "Game 925 \tScore: 34 Time: 4.4950950145721436 \tRecord: 49\n",
      "Game 926 \tScore: 21 Time: 2.903609275817871 \tRecord: 49\n",
      "Game 927 \tScore: 39 Time: 5.562153339385986 \tRecord: 49\n",
      "Game 928 \tScore: 31 Time: 3.909862518310547 \tRecord: 49\n",
      "Game 929 \tScore: 34 Time: 4.14324426651001 \tRecord: 49\n",
      "Game 930 \tScore: 26 Time: 4.472653865814209 \tRecord: 49\n",
      "Game 931 \tScore: 30 Time: 4.532534599304199 \tRecord: 49\n",
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      "Game 933 \tScore: 28 Time: 3.54080867767334 \tRecord: 49\n",
      "Game 934 \tScore: 23 Time: 2.75020432472229 \tRecord: 49\n",
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      "Game 940 \tScore: 14 Time: 1.8727412223815918 \tRecord: 49\n",
      "Game 941 \tScore: 15 Time: 1.7688353061676025 \tRecord: 49\n",
      "Game 942 \tScore: 2 Time: 0.21442556381225586 \tRecord: 49\n",
      "Game 943 \tScore: 14 Time: 1.7616214752197266 \tRecord: 49\n",
      "Game 944 \tScore: 7 Time: 0.561497688293457 \tRecord: 49\n",
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      "Game 947 \tScore: 29 Time: 3.8614237308502197 \tRecord: 49\n",
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      "Game 950 \tScore: 11 Time: 1.2916274070739746 \tRecord: 49\n",
      "Game 951 \tScore: 31 Time: 5.386049747467041 \tRecord: 49\n",
      "Game 952 \tScore: 17 Time: 2.884812116622925 \tRecord: 49\n",
      "Game 953 \tScore: 7 Time: 0.8512654304504395 \tRecord: 49\n",
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      "Game 956 \tScore: 28 Time: 4.087635040283203 \tRecord: 49\n",
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      "Game 958 \tScore: 11 Time: 3.2013015747070312 \tRecord: 49\n",
      "Game 959 \tScore: 35 Time: 5.641520977020264 \tRecord: 49\n",
      "Game 960 \tScore: 14 Time: 2.3333418369293213 \tRecord: 49\n",
      "Game 961 \tScore: 32 Time: 4.370951175689697 \tRecord: 49\n",
      "Game 962 \tScore: 5 Time: 0.8108303546905518 \tRecord: 49\n",
      "Game 963 \tScore: 23 Time: 3.2066457271575928 \tRecord: 49\n",
      "Game 964 \tScore: 21 Time: 3.0030715465545654 \tRecord: 49\n",
      "Game 965 \tScore: 20 Time: 3.1183817386627197 \tRecord: 49\n",
      "Game 966 \tScore: 17 Time: 1.6227591037750244 \tRecord: 49\n",
      "Game 967 \tScore: 31 Time: 4.15378737449646 \tRecord: 49\n",
      "Game 968 \tScore: 18 Time: 2.1102161407470703 \tRecord: 49\n",
      "Game 969 \tScore: 17 Time: 3.198002338409424 \tRecord: 49\n",
      "Game 970 \tScore: 32 Time: 3.9351892471313477 \tRecord: 49\n",
      "Game 971 \tScore: 10 Time: 1.6234934329986572 \tRecord: 49\n",
      "Game 972 \tScore: 6 Time: 1.0133168697357178 \tRecord: 49\n",
      "Game 973 \tScore: 29 Time: 4.623263597488403 \tRecord: 49\n",
      "Game 974 \tScore: 20 Time: 2.9063010215759277 \tRecord: 49\n",
      "Game 975 \tScore: 18 Time: 2.9008352756500244 \tRecord: 49\n",
      "Game 976 \tScore: 32 Time: 5.022394895553589 \tRecord: 49\n",
      "Game 977 \tScore: 46 Time: 7.206371307373047 \tRecord: 49\n",
      "Game 978 \tScore: 27 Time: 4.1040239334106445 \tRecord: 49\n",
      "Game 979 \tScore: 17 Time: 2.218233108520508 \tRecord: 49\n",
      "Game 980 \tScore: 13 Time: 1.3809452056884766 \tRecord: 49\n",
      "Game 981 \tScore: 26 Time: 3.8963358402252197 \tRecord: 49\n",
      "Game 982 \tScore: 13 Time: 1.4663333892822266 \tRecord: 49\n",
      "Game 983 \tScore: 19 Time: 2.6625237464904785 \tRecord: 49\n",
      "Game 984 \tScore: 23 Time: 3.141728401184082 \tRecord: 49\n",
      "Game 985 \tScore: 20 Time: 2.560420513153076 \tRecord: 49\n",
      "Game 986 \tScore: 10 Time: 1.1865346431732178 \tRecord: 49\n",
      "Game 987 \tScore: 13 Time: 1.3655080795288086 \tRecord: 49\n",
      "Game 988 \tScore: 18 Time: 2.9998908042907715 \tRecord: 49\n",
      "Game 989 \tScore: 13 Time: 2.005539655685425 \tRecord: 49\n",
      "Game 990 \tScore: 42 Time: 5.4910569190979 \tRecord: 49\n",
      "Game 991 \tScore: 42 Time: 5.756154298782349 \tRecord: 49\n",
      "Game 992 \tScore: 27 Time: 3.0055007934570312 \tRecord: 49\n",
      "Game 993 \tScore: 8 Time: 0.8694050312042236 \tRecord: 49\n",
      "Game 994 \tScore: 22 Time: 2.898780345916748 \tRecord: 49\n",
      "Game 995 \tScore: 13 Time: 2.448371171951294 \tRecord: 49\n",
      "Game 996 \tScore: 20 Time: 2.5296616554260254 \tRecord: 49\n",
      "Game 997 \tScore: 18 Time: 2.5061683654785156 \tRecord: 49\n",
      "Game 998 \tScore: 42 Time: 7.55898380279541 \tRecord: 49\n",
      "Game 999 \tScore: 24 Time: 3.42016863822937 \tRecord: 49\n",
      "Game 1000 \tScore: 20 Time: 1.9886324405670166 \tRecord: 49\n"
     ]
    }
   ],
   "source": [
    "# Ejecución del código estructura de piramide (low dropout)\n",
    "lstm_scores, lstm_times = run(agent = LSTMDQNAgent(),games = 1000, weights_file = \"lstm_weights.hdf5\",verbose = True)\n",
    "\n",
    "# Store/Load\n",
    "np.save(\"lstm_scores\", lstm_scores)\n",
    "np.save(\"lstm_times\", lstm_times)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Preparación de los resultados\n",
    "Con las ejecuciones ya acabadas, procedemos a preparar los datos de tiempo y puntuación obtenidos para poder ser comparados. Para ello vamos a agrupar los datos en tripletas `{eval_id,exec_time,score}`e incluir un identificador de modelo así como un identificador de grupo si dividiésemos las simulaciones en grupos de 50 (20 bins). Para ello vamos a crear una función que agrupe los datos en el formato deseado y luego los concatenaremos en un único dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Method to unify results arrays ands generate a Pandas dataframe with given data and specified identifiers\n",
    "    Inputs:\n",
    "        - times : array of execution times per evaluation\n",
    "        - scores : array of scores obtained per evaluation\n",
    "        - set_id : model identifier\n",
    "        - bin_size: size to divide data in bins\n",
    "    Outputs: return a dataframe with the following format: <Exec, Exec_Time, Score, bin, Set>\n",
    "'''\n",
    "def results_handler(times,scores,set_id, bin_size = 50):\n",
    "    # Prepare calculated vectors\n",
    "    execs = [i for i in range(1,len(times)+1)]\n",
    "    bins = [[i]*bin_size for i in range(1,round(len(times)/bin_size)+1)]\n",
    "    bins = [item for sublist in bins for item in sublist]\n",
    "    ids = [set_id] * len(times)\n",
    "    # Generate data frame\n",
    "    info = {'Exec' : execs, 'Exec_Time' : times, 'Score': scores, 'bin' : bins, 'Set':ids}\n",
    "    df = pd.DataFrame(info)\n",
    "    # return\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Exec</th>\n",
       "      <th>Exec_Time</th>\n",
       "      <th>Score</th>\n",
       "      <th>bin</th>\n",
       "      <th>Set</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0.265291</td>\n",
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       "      <td>Original</td>\n",
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       "      <td>0.083776</td>\n",
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       "      <td>4</td>\n",
       "      <td>0.111702</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Original</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0.277766</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Original</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Original</td>\n",
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       "      <td>0.538355</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   Exec  Exec_Time  Score  bin       Set\n",
       "0     1   0.265291      1    1  Original\n",
       "1     2   0.080768      0    1  Original\n",
       "2     3   0.083776      1    1  Original\n",
       "3     4   0.111702      1    1  Original\n",
       "4     5   0.277766      0    1  Original\n",
       "5     6   0.086768      0    1  Original\n",
       "6     7   0.166555      1    1  Original\n",
       "7     8   0.538355      2    1  Original\n",
       "8     9   0.126622      2    1  Original\n",
       "9    10   0.050862      1    1  Original"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transform\n",
    "ori_df = results_handler(original_times,original_scores,'Original',bin_size = 50)\n",
    "mod_df = results_handler(mod_times,mod_scores,'Modified',bin_size = 50)\n",
    "pyr_df = results_handler(pyr_times,pyr_scores,'Pyramidal',bin_size = 50)\n",
    "pyrl_df = results_handler(pyrl_times,pyrl_scores,'Pyramidal_Low',bin_size = 50)\n",
    "lstm_df = results_handler(lstm_times,lstm_scores,'LSTM',bin_size = 50)\n",
    "# Unify\n",
    "results_df = [ori_df,mod_df,pyr_df,pyrl_df,lstm_df]\n",
    "results_df = pd.concat(results_df)\n",
    "# Show\n",
    "results_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Estudio de los resultados\n",
    "Si graficamos la evolución de la puntuación obtenida por cada modelo a lo largo de las ejecuciones de entrenamiento obtenemos lo siguiente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16d492aa668>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "plt.figure(figsize=(16, 15))\n",
    "plt.subplot(3, 1, 1)\n",
    "sns.lineplot(x='Exec', y='Score', hue='Set', data=results_df)\n",
    "plt.subplot(3, 1, 2)\n",
    "sns.boxplot(x='bin', y='Score', hue='Set', data=results_df)\n",
    "plt.subplot(3, 1, 3)\n",
    "sns.boxplot(x='bin', y='Exec_Time', hue='Set', data=results_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Set\n",
       "LSTM             2336.848762\n",
       "Modified         1288.135543\n",
       "Original          674.314771\n",
       "Pyramidal         645.041266\n",
       "Pyramidal_Low     735.720342\n",
       "Name: Exec_Time, dtype: float64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total execution times\n",
    "results_df.groupby('Set')['Exec_Time'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 Observaciones y conclusiones\n",
    "\n",
    "**Observaciones:**\n",
    "1. Modelo `Original`:\n",
    "    * Aprende adecuadamente a jugar en poco tiempo.\n",
    "    * Mantiene su media de puntuación a lo largo de las ejecuciones tardías en torno a los 15 puntos.\n",
    "2. Modelo `Modified`:\n",
    "    * Aprende adecuadamente a jugar en poco tiempo.\n",
    "    * Alcanza las mejores puntuaciones de media aunque no consiga el record de los modelos.\n",
    "    * Mantiene su capacidad de jugar adecuadamente a lo largo de todas las ejecuciones.\n",
    "3. Modelo `Pyramid`:\n",
    "    * Aprende rápidamente a jugar bien.\n",
    "    * Alcanza el record de los modelos antes de las 500 ejecuciones.\n",
    "    * Rápidamente se sobre-entrena y baja sus puntuaciones a la peor.\n",
    "4. Modelo `Pyramid_Low`:\n",
    "    * Aprende lentamente a jugar bien.\n",
    "    * El ajuste de `dropout`consigue que el modelo no pierda su capacidad de juego pero evita que alcance las puntuaciones altas que consigue el modelo `Pyramid`.\n",
    "    * Está entre las mejores puntuaciones una vez entrenado.\n",
    "    * Requiere menos tiempo de ejecución que el modelo `Modified`.\n",
    "5. Modelo `LSTM`:\n",
    "    * Aprende lentamente a jugar.\n",
    "    * Alcanza buenas medias en las etapas finales.\n",
    "    * Necesita demasiado tiempo para ser ejecutado.\n",
    "\n",
    "De estas observaciones podemos obtener las siguientes **conclusiones:**\n",
    "\n",
    "* Los mejores modelos para este juego son el modelo `Modified`y el `Pyramid_Low`.\n",
    "    * El modelo `Modified` aprende más rápido.\n",
    "    * El modelo `Pyramid_Low` necesita más tiempo de entrenamiento pero menos tiempo de ejecución.\n",
    "* El modelo `Pyramid`es un gran candidato pero habría que detectar el momento en el que se produce el sobre-entrenamiento para mantenerlo en unas cotas de acierto adecuadas.\n",
    "* El modelo LSTM demuestra aprender adecuadamente a jugar aunque necesita demasiado tiempo de ejecución y entrenamiento.\n",
    "\n",
    "## 5. Trabajos futuros\n",
    "\n",
    "Observados estos resultados, los trabajos futuros podrían ser:\n",
    "* Re-ejecutar los modelos `Pyramid_Low`y `Modified`con un grid parámetros centrados en los `dropout`y el ratio de aprendizaje.\n",
    "* Re-ejecutar todos los modelos modificando las reglas del juego para que la serpiente pueda atravesar las paredes."
   ]
  }
 ],
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