Install gym with gym[...,accept-rom-license] instead of installing and running AutoROM manually

Author: ageron

18_reinforcement_learning.ipynb

@@ -52,7 +52,9 @@
     "\n",
     "if IS_COLAB or IS_KAGGLE:\n",
     "    !apt update && apt install -y libpq-dev libsdl2-dev swig xorg-dev xvfb\n",
-    "    %pip install -q -U tf-agents pyvirtualdisplay gym[box2d,atari] AutoROM\n",
+    "    %pip install -U tf-agents pyvirtualdisplay\n",
+    "    %pip install -U gym>=0.21.0\n",
+    "    %pip install -U gym[box2d,atari,accept-rom-license]\n",
     "\n",
     "# Scikit-Learn ≥0.20 is required\n",
     "import sklearn\n",
@@ -2180,38 +2182,6 @@
     "Let's use TF-Agents to create an agent that will learn to play Breakout. We will use the Deep Q-Learning algorithm, so you can easily compare the components with the previous implementation, but TF-Agents implements many other (and more sophisticated) algorithms!"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, we need to download and install the Atari ROMs. This can be done very easily using the `AutoROM` tool, if you accept the license:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AutoROM will download the Atari 2600 ROMs.\n",
-      "They will be installed to:\n",
-      "\t/Users/ageron/miniconda3/envs/tf2/lib/python3.8/site-packages/AutoROM/roms\n",
-      "\n",
-      "Existing ROMs will be overwritten.\n",
-      "Done!                                                                           \n"
-     ]
-    }
-   ],
-   "source": [
-    "if IS_COLAB:\n",
-    "    !{sys.prefix}/local/bin/AutoROM --quiet --accept-license\n",
-    "else:\n",
-    "    !{sys.prefix}/bin/AutoROM --quiet --accept-license"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},