Cleared notebook output.

python/Basics.ipynb

   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:unityagents:\n",
-      "'Ball3DAcademy' started successfully!\n",
-      "Unity Academy name: Ball3DAcademy\n",
-      "        Number of Brains: 1\n",
-      "        Number of External Brains : 1\n",
-      "        Lesson number : 0\n",
-      "        Reset Parameters :\n",
-      "\t\t\n",
-      "Unity brain name: Ball3DBrain\n",
-      "        Number of Visual Observations (per agent): 0\n",
-      "        Vector Observation space type: continuous\n",
-      "        Vector Observation space size (per agent): 8\n",
-      "        Number of stacked Vector Observation: 3\n",
-      "        Vector Action space type: continuous\n",
-      "        Vector Action space size (per agent): 2\n",
-      "        Vector Action descriptions: , \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Unity Academy name: Ball3DAcademy\n",
-      "        Number of Brains: 1\n",
-      "        Number of External Brains : 1\n",
-      "        Lesson number : 0\n",
-      "        Reset Parameters :\n",
-      "\t\t\n",
-      "Unity brain name: Ball3DBrain\n",
-      "        Number of Visual Observations (per agent): 0\n",
-      "        Vector Observation space type: continuous\n",
-      "        Vector Observation space size (per agent): 8\n",
-      "        Number of stacked Vector Observation: 3\n",
-      "        Vector Action space type: continuous\n",
-      "        Vector Action space size (per agent): 2\n",
-      "        Vector Action descriptions: , \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "env = UnityEnvironment(file_name=env_name)\n",
     "\n",
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Agent state looks like: \n",
-      "[ 0.          0.          0.          0.          0.          0.          0.\n",
-      "  0.          0.          0.          0.          0.          0.          0.\n",
-      "  0.          0.         -0.01467304 -0.01468306 -0.52082086  4.\n",
-      " -0.79952097  0.          0.          0.        ]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Reset the environment\n",
     "env_info = env.reset(train_mode=train_mode)[default_brain]\n",
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total reward this episode: 0.700000025331974\n",
-      "Total reward this episode: 1.500000037252903\n",
-      "Total reward this episode: 0.6000000238418579\n",
-      "Total reward this episode: 1.0000000298023224\n",
-      "Total reward this episode: 0.40000002086162567\n",
-      "Total reward this episode: 0.5000000223517418\n",
-      "Total reward this episode: 0.700000025331974\n",
-      "Total reward this episode: 1.1000000312924385\n",
-      "Total reward this episode: 0.9000000283122063\n",
-      "Total reward this episode: 1.1000000312924385\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for episode in range(10):\n",
     "    env_info = env.reset(train_mode=train_mode)[default_brain]\n",
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {