vscode-henry/proyectos/lunar-lander/Lunar_Lander.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# LUNAR LANDER\n",
        "\n",
        "El objetivo del juego es simple (¡pero aterrizar no lo es!): ¡aterrizar la nave espacial sana y salva en la plataforma designada! ¡Prepárate para un aterrizaje suave y heroico! 🚀🌕\n"
      ],
      "metadata": {
        "id": "hmWbGiyvNNME"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Reglas y Punteo\n",
        "En cada momento del juego, ganas o pierdes puntos (recompensa) dependiendo de cómo te vaya:\n",
        "\n",
        "**Aterrizaje y velocidad**: Ganas puntos si te acercas a la zona de aterrizaje y vas despacio. Pierdes puntos si te alejas o vas muy rápido.\n",
        "\n",
        "**Inclinación**: Pierdes puntos si la nave está muy inclinada. ¡Tienes que mantenerla lo más horizontal posible!\n",
        "\n",
        "**Patas en el suelo**: Ganas **10** puntos por cada pata que toca el suelo en la zona de aterrizaje.\n",
        "\n",
        "**Motores**: Pierdes puntos por usar los motores: un poquito por los motores laterales y más por el motor principal. ¡Hay que usarlos con cuidado!\n",
        "\n",
        "**Final del juego**: Si te estrellas, pierdes **100** puntos. Si aterrizas suavemente en la plataforma, ¡ganas **100** puntos extra!\n",
        "\n",
        "Para considerar que has tenido éxito en un intento (episodio), ¡necesitas conseguir al menos **200** puntos en total!"
      ],
      "metadata": {
        "id": "OvS3IEo_Pmv8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Instalacion de librerias"
      ],
      "metadata": {
        "id": "GiL_39bYC6dT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
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      "source": [
        "# Permite conectar codigo en C, C++ con Python\n",
        "# Requerido por box2d\n",
        "!pip install -q swig\n",
        "\n",
        "# Gymnasium provee entornos de simulacion, controles y califica resultado\n",
        "!pip install -q \"gymnasium[classic-control]\"\n",
        "!pip install -q gymnasium[box2d]\n",
        "\n",
        "# Para grabar y reproducir video\n",
        "# !pip install moviepy\n",
        "!pip install -q pyvirtualdisplay\n",
        "\n",
        "# Agente DQN (Deep Q-learning), al que entrenaremos\n",
        "!pip install -q stable-baselines3"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Variables globales"
      ],
      "metadata": {
        "id": "3l9R8gV2C_ZJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ENV_NAME = \"LunarLander-v3\" # Nombre del entorno\n",
        "VIDEO_FOLDER = \"./video_prueba_de_vuelo\" # En esta carpeta se guardaran los videos del test de vuelo\n",
        "EPISODES = 1 # Numero de episodios a grabar en la prueba de vuelo, se tratara de seleccionar el mejor\n",
        "LOG_DIR = \"./tmp/dqn_lunar\" # Carpeta donde se guardarán los registros de entrenamiento (logs)"
      ],
      "metadata": {
        "id": "502iTO5rCz_P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Entrenando el modelo"
      ],
      "metadata": {
        "id": "Ywk9QTWzEOtc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ==============================================================================\n",
        "# ENTRENAMIENTO DE UN AGENTE DQN (Stable-Baselines3)\n",
        "# ==============================================================================\n",
        "\n",
        "# Gymnasium provee el entorno, controles y evalua el resultado\n",
        "import gymnasium as gym\n",
        "from gymnasium.wrappers import RecordVideo\n",
        "import os\n",
        "# import moviepy.editor as mp # Importamos MoviePy\n",
        "\n",
        "\n",
        "# Agente DQN, al que entrenaremos\n",
        "from stable_baselines3 import DQN\n",
        "from stable_baselines3.common.env_util import make_vec_env\n",
        "from stable_baselines3.common.monitor import Monitor\n",
        "\n",
        "\n",
        "# --- Preparación para el entrenamiento ---\n",
        "# La grabación de video solo debe hacerse después del entrenamiento o en un ambiente separado.\n",
        "# Para entrenar, usaremos una versión simple del ambiente sin el wrapper de video.\n",
        "\n",
        "os.makedirs(LOG_DIR, exist_ok=True)\n",
        "\n",
        "# Crear el ambiente para el entrenamiento (usando Monitor para guardar logs)\n",
        "env_train = gym.make(\n",
        "    ENV_NAME,\n",
        "    continuous=False,\n",
        "    gravity=-10,\n",
        "    enable_wind=False,\n",
        "    wind_power=15.0,\n",
        "    turbulence_power=1.5\n",
        ")\n",
        "env_train = Monitor(env_train, LOG_DIR)\n",
        "\n",
        "# Stable-Baselines3 funciona mejor con entornos vectorizados\n",
        "env_train_vec = make_vec_env(lambda: env_train, n_envs=1)\n",
        "\n",
        "\n",
        "# --- Creación del Modelo DQN ---\n",
        "# DQN es un algoritmo de Q-Learning profundo, ideal para ambientes discretos (como LunarLander-v3)\n",
        "model = DQN(\n",
        "    \"MlpPolicy\",         # Tipo de red neuronal (Multi-layer perceptron)\n",
        "    env_train_vec,       # El ambiente de entrenamiento\n",
        "    learning_rate=0.0001,  # Tasa de aprendizaje (0.00001 y 0.001)\n",
        "    buffer_size=10000,   # (10000 - 50000)\n",
        "    learning_starts=5000,# (1000 - 10000)\n",
        "    batch_size=64,       # Puede ser [32, 64, 128]\n",
        "    gamma=0.99,          # Factor de descuento (0.90 - 0.99) menor=quiero recompensas rapido, mayor=espera recompensas mayores siendo mas cuidadoso\n",
        "    verbose=1,           # Mostrar el progreso del entrenamiento\n",
        "    tensorboard_log=LOG_DIR\n",
        ")\n",
        "\n",
        "# --- Bucle de Aprendizaje ---\n",
        "# El método .learn() es el núcleo del entrenamiento de RL.\n",
        "# Entrenaremos por (50,000 - 200,000) pasos (timesteps). Esto tomará unos minutos en Colab.\n",
        "TIMESTEPS = 100_000\n",
        "print(f\"\\n--- INICIANDO ENTRENAMIENTO DQN por {TIMESTEPS} pasos ---\")\n",
        "\n",
        "# Entrenar!!\n",
        "model.learn(\n",
        "    total_timesteps=TIMESTEPS,\n",
        "    log_interval=100\n",
        ")\n",
        "\n",
        "print(\"\\n--- ENTRENAMIENTO FINALIZADO. Modelo entrenado guardado. ---\")\n",
        "model.save(\"modelo_nave_entrenada\") # Guarda el modelo entrenado\n",
        "env_train.close()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "OtROtkf7gzka",
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          "text": [
            "Gym has been unmaintained since 2022 and does not support NumPy 2.0 amongst other critical functionality.\n",
            "Please upgrade to Gymnasium, the maintained drop-in replacement of Gym, or contact the authors of your software and request that they upgrade.\n",
            "See the migration guide at https://gymnasium.farama.org/introduction/migration_guide/ for additional information.\n",
            "<frozen importlib._bootstrap>:488: DeprecationWarning: builtin type SwigPyPacked has no __module__ attribute\n",
            "<frozen importlib._bootstrap>:488: DeprecationWarning: builtin type SwigPyObject has no __module__ attribute\n",
            "<frozen importlib._bootstrap>:488: DeprecationWarning: builtin type swigvarlink has no __module__ attribute\n",
            "/usr/local/lib/python3.12/dist-packages/pygame/pkgdata.py:25: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n",
            "  from pkg_resources import resource_stream, resource_exists\n",
            "/usr/local/lib/python3.12/dist-packages/pkg_resources/__init__.py:3154: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google')`.\n",
            "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
            "  declare_namespace(pkg)\n",
            "/usr/local/lib/python3.12/dist-packages/pkg_resources/__init__.py:3154: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google.cloud')`.\n",
            "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
            "  declare_namespace(pkg)\n",
            "/usr/local/lib/python3.12/dist-packages/pkg_resources/__init__.py:3154: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('sphinxcontrib')`.\n",
            "Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages\n",
            "  declare_namespace(pkg)\n",
            "/usr/local/lib/python3.12/dist-packages/jupyter_client/session.py:203: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).\n",
            "  return datetime.utcnow().replace(tzinfo=utc)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using cpu device\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/jupyter_client/session.py:203: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).\n",
            "  return datetime.utcnow().replace(tzinfo=utc)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- INICIANDO ENTRENAMIENTO DQN por 100000 pasos ---\n",
            "Logging to ./tmp/dqn_lunar/DQN_4\n",
            "----------------------------------\n",
            "| rollout/            |          |\n",
            "|    ep_len_mean      | 213      |\n",
            "|    ep_rew_mean      | -241     |\n",
            "|    exploration_rate | 0.05     |\n",
            "| time/               |          |\n",
            "|    episodes         | 100      |\n",
            "|    fps              | 990      |\n",
            "|    time_elapsed     | 21       |\n",
            "|    total_timesteps  | 21324    |\n",
            "| train/              |          |\n",
            "|    learning_rate    | 0.0001   |\n",
            "|    loss             | 1.19     |\n",
            "|    n_updates        | 4080     |\n",
            "----------------------------------\n",
            "----------------------------------\n",
            "| rollout/            |          |\n",
            "|    ep_len_mean      | 318      |\n",
            "|    ep_rew_mean      | -111     |\n",
            "|    exploration_rate | 0.05     |\n",
            "| time/               |          |\n",
            "|    episodes         | 200      |\n",
            "|    fps              | 858      |\n",
            "|    time_elapsed     | 61       |\n",
            "|    total_timesteps  | 53093    |\n",
            "| train/              |          |\n",
            "|    learning_rate    | 0.0001   |\n",
            "|    loss             | 0.729    |\n",
            "|    n_updates        | 12023    |\n",
            "----------------------------------\n",
            "----------------------------------\n",
            "| rollout/            |          |\n",
            "|    ep_len_mean      | 315      |\n",
            "|    ep_rew_mean      | 8.29     |\n",
            "|    exploration_rate | 0.05     |\n",
            "| time/               |          |\n",
            "|    episodes         | 300      |\n",
            "|    fps              | 832      |\n",
            "|    time_elapsed     | 101      |\n",
            "|    total_timesteps  | 84629    |\n",
            "| train/              |          |\n",
            "|    learning_rate    | 0.0001   |\n",
            "|    loss             | 1.27     |\n",
            "|    n_updates        | 19907    |\n",
            "----------------------------------\n",
            "\n",
            "--- ENTRENAMIENTO FINALIZADO. Modelo entrenado guardado. ---\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prueba de Vuelo"
      ],
      "metadata": {
        "id": "nq6619RPJBFb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ==============================================================================\n",
        "# 4. PRUEBA DE VUELO Y GRABAR EL VIDEO\n",
        "# ==============================================================================\n",
        "from IPython.display import HTML\n",
        "from base64 import b64encode\n",
        "import glob\n",
        "import io\n",
        "from pyvirtualdisplay import Display\n",
        "\n",
        "# Google collab tiene dependencias core deprecadas\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# 1. Configurar la Pantalla Virtual (Necesario para Colab/Jupyter sin GUI)\n",
        "print(\"\\n--- Configurando Pantalla Virtual ---\")\n",
        "try:\n",
        "    display = Display(visible=0, size=(640, 480))\n",
        "    display.start()\n",
        "    print(\"Pantalla virtual iniciada.\")\n",
        "except Exception as e:\n",
        "    print(f\"Advertencia al iniciar pyvirtualdisplay: {e}. Continuaremos.\")\n",
        "\n",
        "# 2. Crear un nuevo ambiente con el wrapper RecordVideo\n",
        "# Creamos la carpeta de video si no existe\n",
        "os.makedirs(VIDEO_FOLDER, exist_ok=True)\n",
        "print(f\"Grabando {EPISODES} episodio(s) en la carpeta: {VIDEO_FOLDER}\")\n",
        "\n",
        "# Creamos el ambiente de test con el wrapper de video\n",
        "env_test = gym.make(\n",
        "    ENV_NAME,\n",
        "    continuous=False,\n",
        "    gravity=-10,\n",
        "    enable_wind=False,\n",
        "    wind_power=15.0,\n",
        "    turbulence_power=0.1,\n",
        "    render_mode=\"rgb_array\"\n",
        ")\n",
        "# El wrapper de RecordVideo debe ser el que envuelve al ambiente base\n",
        "env_test_video = RecordVideo(\n",
        "    env_test,\n",
        "    video_folder=VIDEO_FOLDER,\n",
        "    episode_trigger=lambda x: x == 0, # Graba solo el primer episodio\n",
        "    name_prefix=\"prueba_de_vuelo\"\n",
        ")\n",
        "\n",
        "# 3. Cargar el modelo entrenado y ejecutar un episodio\n",
        "# Cargamos el modelo que acabamos de entrenar y guardar\n",
        "model = DQN.load(\"modelo_nave_entrenada\", env=env_test_video)\n",
        "\n",
        "obs, info = env_test_video.reset()\n",
        "done = False\n",
        "truncated = False\n",
        "while not (done or truncated):\n",
        "    # El modelo determina la acción\n",
        "    action, _ = model.predict(obs, deterministic=True)\n",
        "    # Ejecutamos la acción\n",
        "    obs, reward, done, truncated, info = env_test_video.step(action)\n",
        "\n",
        "env_test_video.close()\n",
        "print(\"\\n--- Grabación del video finalizada. ---\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jL7FmdKqhZaA",
        "outputId": "faeee27a-cb52-472a-cde7-47828f328b1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- Configurando Pantalla Virtual ---\n",
            "Pantalla virtual iniciada.\n",
            "Grabando 1 episodio(s) en la carpeta: ./video_prueba_de_vuelo\n",
            "Wrapping the env with a `Monitor` wrapper\n",
            "Wrapping the env in a DummyVecEnv.\n",
            "\n",
            "--- Grabación del video finalizada. ---\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Reproducir Video de la prueba"
      ],
      "metadata": {
        "id": "ZYI_XlxDJJU4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ==============================================================================\n",
        "# 5. CARGAR Y REPRODUCIR EL VIDEO DE LA PRUEBA DE VUELO\n",
        "# ==============================================================================\n",
        "import os\n",
        "import glob\n",
        "import io\n",
        "from IPython.display import HTML, display\n",
        "from base64 import b64encode\n",
        "\n",
        "# 1. Función para codificar y mostrar un video usando Base64\n",
        "def display_encoded_video(video_path):\n",
        "    \"\"\"Codifica un video a Base64 y lo muestra en un Jupyter/Colab notebook.\"\"\"\n",
        "    print(f\"Mostrando: {video_path}\")\n",
        "\n",
        "    try:\n",
        "        # Abrir y codificar el archivo\n",
        "        with io.open(video_path, 'rb') as f:\n",
        "            video_bytes = f.read()\n",
        "        video_encoded = b64encode(video_bytes).decode()\n",
        "\n",
        "        # Crear y mostrar el tag de video HTML\n",
        "        html_tag = f\"\"\"\n",
        "        <video width=\"600\" controls autoplay>\n",
        "            <source src=\"data:video/mp4;base64,{video_encoded}\" type=\"video/mp4\">\n",
        "            Tu navegador no soporta el tag de video.\n",
        "        </video>\n",
        "        <p>--------------------------------------------------</p>\n",
        "        \"\"\"\n",
        "        display(HTML(html_tag))\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"❌ ERROR al procesar o mostrar el video {video_path}: {e}\")\n",
        "        print(\"Esto podría ser por un archivo muy grande.\")\n",
        "\n",
        "\n",
        "# 2. Buscar todos los archivos .mp4 en la carpeta\n",
        "# Ordenamos por fecha de creación para verlos en orden de grabación\n",
        "list_of_files = sorted(\n",
        "    glob.glob(os.path.join(VIDEO_FOLDER, \"*.mp4\")),\n",
        "    key=os.path.getctime\n",
        ")\n",
        "\n",
        "# 3. Iterar y mostrar cada video\n",
        "if list_of_files:\n",
        "    print(f\"✅ Se encontraron {len(list_of_files)} videos para reproducir.\")\n",
        "    for video_file in list_of_files:\n",
        "        display_encoded_video(video_file)\n",
        "else:\n",
        "    print(f\"❌ No se encontró ningún archivo de video MP4 en {VIDEO_FOLDER}.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "VAqKRsjN2gb-",
        "outputId": "7b7f43cc-af29-4f29-edcc-34345da691db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Se encontraron 1 videos para reproducir.\n",
            "Mostrando: ./video_prueba_de_vuelo/prueba_de_vuelo-episode-0.mp4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "        <video width=\"600\" controls autoplay>\n",
              "            <source 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type=\"video/mp4\">\n",
              "            Tu navegador no soporta el tag de video.\n",
              "        </video>\n",
              "        <p>--------------------------------------------------</p>\n",
              "        "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Puntaje de la prueba"
      ],
      "metadata": {
        "id": "za-H_n-3JRBd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ----------------------------------------------------------------------\n",
        "# CALIFICACION DEL ENTRENAMIENTO\n",
        "# ----------------------------------------------------------------------\n",
        "\n",
        "# Asume que estas variables ya han sido actualizadas por env_test_video.step()\n",
        "# reward, done, truncated, info\n",
        "\n",
        "\n",
        "# Imprimir cada variable en una línea separada\n",
        "print(f\"Reward (Recompensa): {reward:.2f}\")\n",
        "print(f\"Done (Logro Completar?): {done}\")\n",
        "print(f\"Truncated (Tuvo que interrumpirse?): {truncated}\")\n",
        "print(f\"Info (Información): {info}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "id": "uabPapxG2_nO",
        "outputId": "bc9ac42e-9bf3-4653-c4e9-bb6c063c6d2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'reward' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/tmp/ipython-input-984683751.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Imprimir cada variable en una línea separada\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Reward (Recompensa): {reward:.2f}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Done (Logro Completar?): {done}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Truncated (Tuvo que interrumpirse?): {truncated}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'reward' is not defined"
          ]
        }
      ]
    }
  ]
}