t-agent.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting gym\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1d/85/a7a462d7796f097027d60f9a62b4e17a0a94dcf12ac2a9f9a913333b11a6/gym-0.15.4.tar.gz (1.6MB)\n",
      "\u001b[K     |████████████████████████████████| 1.6MB 7.7MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: scipy in /opt/conda/envs/Python36/lib/python3.6/site-packages (from gym) (1.2.0)\n",
      "Requirement already satisfied: numpy>=1.10.4 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from gym) (1.15.4)\n",
      "Requirement already satisfied: six in /opt/conda/envs/Python36/lib/python3.6/site-packages (from gym) (1.12.0)\n",
      "Collecting pyglet<=1.3.2,>=1.2.0 (from gym)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1c/fc/dad5eaaab68f0c21e2f906a94ddb98175662cc5a654eee404d59554ce0fa/pyglet-1.3.2-py2.py3-none-any.whl (1.0MB)\n",
      "\u001b[K     |████████████████████████████████| 1.0MB 42.5MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting cloudpickle~=1.2.0 (from gym)\n",
      "  Downloading https://files.pythonhosted.org/packages/c1/49/334e279caa3231255725c8e860fa93e72083567625573421db8875846c14/cloudpickle-1.2.2-py2.py3-none-any.whl\n",
      "Collecting opencv-python (from gym)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c0/a9/9828dfaf93f40e190ebfb292141df6b7ea1a2d57b46263e757f52be8589f/opencv_python-4.1.2.30-cp36-cp36m-manylinux1_x86_64.whl (28.3MB)\n",
      "\u001b[K     |████████████████████████████████| 28.3MB 25.4MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: future in /opt/conda/envs/Python36/lib/python3.6/site-packages (from pyglet<=1.3.2,>=1.2.0->gym) (0.17.1)\n",
      "Building wheels for collected packages: gym\n",
      "  Building wheel for gym (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/dsxuser/.cache/pip/wheels/e9/26/9b/8a1a6599a91077a938ac4348cc3d3ac84bfab0dbfddeb4c6e7\n",
      "Successfully built gym\n",
      "\u001b[31mERROR: spyder 3.3.3 requires pyqt5<=5.12; python_version >= \"3\", which is not installed.\u001b[0m\n",
      "Installing collected packages: pyglet, cloudpickle, opencv-python, gym\n",
      "  Found existing installation: cloudpickle 0.7.0\n",
      "    Uninstalling cloudpickle-0.7.0:\n",
      "      Successfully uninstalled cloudpickle-0.7.0\n",
      "Successfully installed cloudpickle-1.2.2 gym-0.15.4 opencv-python-4.1.2.30 pyglet-1.3.2\n"
     ]
    }
   ],
   "source": [
    "! pip install gym"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import gym\n",
    "import random\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from keras.models     import Sequential\n",
    "from keras.layers     import Dense\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's create the Cart Pole OpenAI Gym game environment and define some constants\n",
    "env = gym.make('CartPole-v1')\n",
    "env.reset()\n",
    "goal_steps = 500\n",
    "score_requirement = 60\n",
    "intial_games = 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(128, input_shape=(4,), activation='tanh', name='in'))\n",
    "model.add(Dense(52, activation='tanh'))\n",
    "model.add(Dense(2, activation='softmax', name='out'))\n",
    "model.compile(loss='mse', optimizer=Adam())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.4637847],\n",
       "       [0.5362153]], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = np.array([0.03075273,  0.22894843,  0.00086323, -0.30798407])\n",
    "test.shape = (1,4)\n",
    "\n",
    "np.argmax(model.predict(test), axis=1)[0]\n",
    "result = model.predict(test)\n",
    "result.shape = (2,1)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     ]
    }
   ],
   "source": [
    "epsilon = 0.5\n",
    "epsilons = []\n",
    "scores = []\n",
    "for game_index in range(intial_games):\n",
    "    score = 0\n",
    "    game_memory = []\n",
    "    observation, reward, done, info = env.step(random.randrange(0, 2))\n",
    "    epsilon = epsilon * 0.9995\n",
    "    epsilons.append(epsilon)\n",
    "    \n",
    "    for step_index in range(goal_steps):\n",
    "        observation.shape = (1,4)\n",
    "        policy = model.predict(observation)\n",
    "        policy.shape = (2,1)\n",
    "        if random.random() < epsilon: #epsilon greedy exploration\n",
    "            policy = np.flip(policy)\n",
    "        action = np.argmax(policy, axis=0)[0]\n",
    "        action = random.randrange(0,2)\n",
    "        previous_observation = observation\n",
    "        observation, reward, done, info = env.step(action)\n",
    "        game_memory.append([previous_observation, action])\n",
    "\n",
    "        score += reward\n",
    "        if done:\n",
    "            break\n",
    "    #print(score)\n",
    "    scores.append(score)\n",
    "    if score >= score_requirement:\n",
    "        for data in game_memory:\n",
    "            if data[1] == 1:\n",
    "                output = np.array([0, 1])\n",
    "            elif data[1] == 0:\n",
    "                output = np.array([1, 0])\n",
    "            print('training model')\n",
    "            #print(output)\n",
    "            output.shape = (1,2)\n",
    "            model.fit(data[0], output, epochs=10, verbose=0)\n",
    "\n",
    "    env.reset()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff37c1a3588>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff2d8280550>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(epsilons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('t-agent.h5')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-agent.h5\r\n"
     ]
    }
   ],
   "source": [
    "!ls "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import types\n",
    "import pandas as pd\n",
    "from botocore.client import Config\n",
    "import ibm_boto3\n",
    "\n",
    "def __iter__(self): return 0\n",
    "\n",
    "# @hidden_cell\n",
    "# The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials.\n",
    "# You might want to remove those credentials before you share the notebook.\n",
    "client_f627bebb44974cc5a73efe23105891f4 = ibm_boto3.client(service_name='s3',\n",
    "    ibm_api_key_id='kx8KnsivcP0GZuH9Rqb4SQCgkNu9jSwbgPZuOEndlnSp',\n",
    "    ibm_auth_endpoint=\"https://iam.ng.bluemix.net/oidc/token\",\n",
    "    config=Config(signature_version='oauth'),\n",
    "    endpoint_url='https://s3-api.us-geo.objectstorage.service.networklayer.com')\n",
    "\n",
    "\n",
    "credentials = {\n",
    "    'IAM_SERVICE_ID': 'iam-ServiceId-fe383a6e-53a3-4447-94ef-579652b22734',\n",
    "    'IBM_API_KEY_ID': 'kx8KnsivcP0GZuH9Rqb4SQCgkNu9jSwbgPZuOEndlnSp',\n",
    "    'ENDPOINT': 'https://s3-api.us-geo.objectstorage.service.networklayer.com',\n",
    "    'IBM_AUTH_ENDPOINT': 'https://iam.ng.bluemix.net/oidc/token',\n",
    "    'BUCKET': 'courserabddeg-donotdelete-pr-x3n6zvw0htb1sf',\n",
    "    'FILE': 'y_arma_data.csv'\n",
    "}\n",
    "client_f627bebb44974cc5a73efe23105891f4.upload_file('t-agent.h5', credentials['BUCKET'],'t-agent.h5')\n",
    "# @hidden_cell\n",
    "# The following code contains the credentials for a file in your IBM Cloud Object Storage.\n",
    "# You might want to remove those credentials before you share your notebook.\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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