diff --git a/18_reinforcement_learning.ipynb b/18_reinforcement_learning.ipynb
index 0b3bbef..0ba4cc9 100644
--- a/18_reinforcement_learning.ipynb
+++ b/18_reinforcement_learning.ipynb
@@ -233,7 +233,7 @@
     "$ xvfb-run -s \"-screen 0 1400x900x24\" jupyter notebook\n",
     "```\n",
     "\n",
-    "Alternatively, you can install the `pyvirtualdisplay` Python library which wraps Xvfb:\n",
+    "Alternatively, you can install the [pyvirtualdisplay](https://github.com/ponty/pyvirtualdisplay) Python library which wraps Xvfb:\n",
     "\n",
     "```bash\n",
     "python3 -m pip install -U pyvirtualdisplay\n",
@@ -1319,8 +1319,8 @@
     "    indices = np.random.randint(len(replay_memory), size=batch_size)\n",
     "    batch = [replay_memory[index] for index in indices]\n",
     "    states, actions, rewards, next_states, dones = [\n",
-    "        np.array([experience[index] for experience in batch])\n",
-    "        for index in range(5)]\n",
+    "        np.array([experience[field_index] for experience in batch])\n",
+    "        for field_index in range(5)]\n",
     "    return states, actions, rewards, next_states, dones"
    ]
   },
@@ -1580,7 +1580,7 @@
     "plt.plot(rewards)\n",
     "plt.xlabel(\"Episode\", fontsize=14)\n",
     "plt.ylabel(\"Sum of rewards\", fontsize=14)\n",
-    "save_fig(\"dqn_rewards_plot\")\n",
+    "save_fig(\"double_dqn_rewards_plot\")\n",
     "plt.show()"
    ]
   },
@@ -2057,7 +2057,7 @@
     "    current_frame_delta = np.maximum(obs[..., 3] - obs[..., :3].mean(axis=-1), 0.)\n",
     "    img[..., 0] += current_frame_delta\n",
     "    img[..., 2] += current_frame_delta\n",
-    "    img = (img - img.min()) / (img.max() - img.min())\n",
+    "    img = np.clip(img / 150, 0, 1)\n",
     "    plt.imshow(img)\n",
     "    plt.axis(\"off\")"
    ]
@@ -2459,58 +2459,6 @@
     "train_agent(n_iterations=200) # change this to 10 million or more!"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "num_eval_episodes = 10\n",
-    "eval_metrics = [\n",
-    "    tf_metrics.AverageReturnMetric(buffer_size=num_eval_episodes),\n",
-    "    tf_metrics.AverageEpisodeLengthMetric(buffer_size=num_eval_episodes),\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "eval_tf_env = suite_atari.load(\n",
-    "    environment_name,\n",
-    "    max_episode_steps=max_episode_steps,\n",
-    "    gym_env_wrappers=[AtariPreprocessing, FrameStack4])\n",
-    "\n",
-    "eval_tf_env = TFPyEnvironment(eval_tf_env)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tf_agents.eval import metric_utils\n",
-    "\n",
-    "results = metric_utils.eager_compute(\n",
-    "    eval_metrics,\n",
-    "    eval_tf_env,\n",
-    "    agent.policy,\n",
-    "    num_episodes=num_eval_episodes,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2534,7 +2482,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2548,7 +2496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2557,7 +2505,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2573,7 +2521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2596,7 +2544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2608,7 +2556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2617,7 +2565,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2640,7 +2588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2689,7 +2637,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2700,7 +2648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [],
    "source": [