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minesh1291/fitter-interface-cartpole.ipynb

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Created May 14, 2023 12:13
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Fitter interface cartpole.ipynb
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fitter-interface-cartpole.ipynb
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},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/jamartinh/f4ca4fa53e8bd256f91012aa7bedf625/fitter-interface-cartpole.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "i-BJuDdnKHem"
},
"source": [
"Setup rendering dependencies for Google Colaboratory."
]
},
{
"cell_type": "code",
"metadata": {
"id": "4cfXdvcFKHep"
},
"source": [
"!pip install gym pyvirtualdisplay > /dev/null 2>&1\n",
"!apt-get install -y xvfb python-opengl ffmpeg > /dev/null 2>&1"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "YsDS4wucKHer"
},
"source": [
"\n",
"\n",
"```\n",
"# Tiene formato de código\n",
"```\n",
"\n",
"Install d3rlpy!"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BI6fbV68MvMf",
"outputId": "152903fe-39e8-4d92-da44-b30d1b565fa5"
},
"source": [
"!pip install d3rlpy"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting d3rlpy\n",
" Downloading d3rlpy-0.91-cp37-cp37m-manylinux1_x86_64.whl (1.0 MB)\n",
"\u001b[K |████████████████████████████████| 1.0 MB 8.1 MB/s \n",
"\u001b[?25hRequirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (from d3rlpy) (3.1.0)\n",
"Collecting colorama\n",
" Downloading colorama-0.4.4-py2.py3-none-any.whl (16 kB)\n",
"Requirement already satisfied: cloudpickle in /usr/local/lib/python3.7/dist-packages (from d3rlpy) (1.3.0)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from d3rlpy) (4.62.3)\n",
"Requirement already satisfied: gym in /usr/local/lib/python3.7/dist-packages (from d3rlpy) (0.17.3)\n",
"Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from d3rlpy) (7.1.2)\n",
"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from d3rlpy) (0.22.2.post1)\n",
"Collecting GPUtil\n",
" Downloading GPUtil-1.4.0.tar.gz (5.5 kB)\n",
"Collecting structlog\n",
" Downloading structlog-21.1.0-py2.py3-none-any.whl (49 kB)\n",
"\u001b[K |████████████████████████████████| 49 kB 5.5 MB/s \n",
"\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from d3rlpy) (1.4.1)\n",
"Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from d3rlpy) (1.9.0+cu102)\n",
"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from d3rlpy) (3.7.4.3)\n",
"Collecting tensorboardX\n",
" Downloading tensorboardX-2.4-py2.py3-none-any.whl (124 kB)\n",
"\u001b[K |████████████████████████████████| 124 kB 44.1 MB/s \n",
"\u001b[?25hRequirement already satisfied: numpy>=1.10.4 in /usr/local/lib/python3.7/dist-packages (from gym->d3rlpy) (1.19.5)\n",
"Requirement already satisfied: pyglet<=1.5.0,>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from gym->d3rlpy) (1.5.0)\n",
"Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from pyglet<=1.5.0,>=1.4.0->gym->d3rlpy) (0.16.0)\n",
"Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py->d3rlpy) (1.5.2)\n",
"Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->d3rlpy) (1.0.1)\n",
"Requirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.7/dist-packages (from tensorboardX->d3rlpy) (3.17.3)\n",
"Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.7/dist-packages (from protobuf>=3.8.0->tensorboardX->d3rlpy) (1.15.0)\n",
"Building wheels for collected packages: GPUtil\n",
" Building wheel for GPUtil (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for GPUtil: filename=GPUtil-1.4.0-py3-none-any.whl size=7411 sha256=a3a890159cb9ead8bae273a05d8d3b6c50aa34f30baf2b3e074bf95032f00d5b\n",
" Stored in directory: /root/.cache/pip/wheels/6e/f8/83/534c52482d6da64622ddbf72cd93c35d2ef2881b78fd08ff0c\n",
"Successfully built GPUtil\n",
"Installing collected packages: tensorboardX, structlog, GPUtil, colorama, d3rlpy\n",
"Successfully installed GPUtil-1.4.0 colorama-0.4.4 d3rlpy-0.91 structlog-21.1.0 tensorboardX-2.4\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lYlxWocKKHer"
},
"source": [
"Setup cartpole dataset."
]
},
{
"cell_type": "code",
"metadata": {
"id": "hmPlILJ8KHes",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "06bc5c3e-be85-4659-c2cf-5079a8b0cc7d"
},
"source": [
"from d3rlpy.datasets import get_cartpole\n",
"\n",
"# get CartPole dataset\n",
"dataset, env = get_cartpole()"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Donwloading cartpole.pkl into d3rlpy_data/cartpole_replay.h5...\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "o2gc9PSrKHes"
},
"source": [
"Setup data-driven deep reinforcement learning algorithm."
]
},
{
"cell_type": "code",
"metadata": {
"id": "s7GGJqYkKHet"
},
"source": [
"from d3rlpy.algos import DiscreteCQL\n",
"from d3rlpy.metrics.scorer import discounted_sum_of_advantage_scorer\n",
"from d3rlpy.metrics.scorer import evaluate_on_environment\n",
"from d3rlpy.metrics.scorer import td_error_scorer\n",
"from d3rlpy.metrics.scorer import average_value_estimation_scorer\n",
"from sklearn.model_selection import train_test_split\n"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "zR80ShP0LNGi"
},
"source": [
"\n",
"# setup CQL algorithm\n",
"algo = DiscreteCQL(use_gpu=False)"
],
"execution_count": 20,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "-Mv2dLyLLKXW"
},
"source": [
"# split train and test episodes\n",
"train_episodes, test_episodes = train_test_split(dataset, test_size=0.2)"
],
"execution_count": 21,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "6A-fuhI0Leor"
},
"source": [
"fitter = algo.fitter(\n",
" dataset=train_episodes,\n",
" eval_episodes=test_episodes,\n",
" n_epochs=10,\n",
" verbose=False,\n",
" tensorboard_dir=None,\n",
" save_metrics=False,\n",
" shuffle=True,\n",
" scorers={\n",
" \"td_error\": td_error_scorer,\n",
" },\n",
")"
],
"execution_count": 27,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ChX2k29zL4AI"
},
"source": [
"df_metrics = None"
],
"execution_count": 28,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Y85ZOFnIM_jv"
},
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from IPython.display import display, clear_output"
],
"execution_count": 31,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 628,
"referenced_widgets": [
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},
"id": "7bwIFeH8MEmt",
"outputId": "5d99b34e-dab7-4ba8-80c5-b7d745571aa4"
},
"source": [
"fig, ax = plt.subplots(figsize=(30, 10))\n",
"for epoch, metrics in fitter: \n",
" try:\n",
"\n",
" if df_metrics is None:\n",
" df_metrics = pd.DataFrame.from_records([metrics])\n",
" else:\n",
" df_metrics = df_metrics.append([metrics]).reset_index(drop=True)\n",
"\n",
" if epoch % 1 == 0: \n",
" plt.close(fig)\n",
" clear_output(wait=True)\n",
" fig, ax = plt.subplots(figsize=(30, 10))\n",
" df_metrics[(col for col in df_metrics.columns if \"time\" not in col and \"temp\" not in col and \"alpha\" not in col)].rolling(100, min_periods=1).mean().plot(ax=ax, subplots=True)\n",
" plt.show()\n",
" plt.pause(0.05)\n",
"\n",
" if epoch % 100 == 0:\n",
" df_metrics = pd.DataFrame(df_metrics.to_numpy(), columns=df_metrics.columns)\n",
" gc.collect() \n",
"\n",
" except KeyboardInterrupt:\n",
" break"
],
"execution_count": 32,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/pandas/plotting/_matplotlib/__init__.py:61: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n",
" plot_obj.generate()\n"
]
},
{
"output_type": "display_data",
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\n",
"text/plain": [
"<Figure size 2160x720 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "k_kstKZZKHeu"
},
"source": [
"Setup rendering utilities for Google Colaboratory."
]
},
{
"cell_type": "code",
"metadata": {
"id": "Gm0FlHbJKHeu"
},
"source": [
"import glob\n",
"import io\n",
"import base64\n",
"\n",
"from gym.wrappers import Monitor\n",
"from IPython.display import HTML\n",
"from IPython import display as ipythondisplay\n",
"from pyvirtualdisplay import Display\n",
"\n",
"# start virtual display\n",
"display = Display(visible=0, size=(1400, 900))\n",
"display.start()\n",
"\n",
"# play recorded video\n",
"def show_video():\n",
" mp4list = glob.glob('video/*.mp4')\n",
" if len(mp4list) > 0:\n",
" mp4 = mp4list[0]\n",
" video = io.open(mp4, 'r+b').read()\n",
" encoded = base64.b64encode(video)\n",
" ipythondisplay.display(HTML(data='''\n",
" <video alt=\"test\" autoplay loop controls style=\"height: 400px;\">\n",
" <source src=\"data:video/mp4;base64,{0}\" type=\"video/mp4\" />\n",
" </video>'''.format(encoded.decode('ascii'))))\n",
" else: \n",
" print(\"Could not find video\")"
],
"execution_count": 33,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "FweSLekbKHev"
},
"source": [
"Record video!"
]
},
{
"cell_type": "code",
"metadata": {
"id": "8zkzCrAvKHev",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "02f84d2d-6460-40cc-f4a8-e2a43c6505cc"
},
"source": [
"# wrap Monitor wrapper\n",
"env = Monitor(env, './video', force=True)\n",
"\n",
"# evaluate\n",
"evaluate_on_environment(env)(cql)"
],
"execution_count": 34,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"200.0"
]
},
"metadata": {},
"execution_count": 34
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZMfw9Y5kKHew"
},
"source": [
"Let's see how it works!"
]
},
{
"cell_type": "code",
"metadata": {
"id": "EwogRHwXKHew",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 422
},
"outputId": "4dc9c042-bf59-4f61-ee84-9705178534d7"
},
"source": [
"show_video()"
],
"execution_count": 35,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <video alt=\"test\" autoplay loop controls style=\"height: 400px;\">\n",
" <source 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type=\"video/mp4\" />\n",
" </video>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "-TzApV2kOkvg"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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