diff --git a/examples/rllib.ipynb b/examples/rllib.ipynb
index 1913b7bb0..b512b78f8 100644
--- a/examples/rllib.ipynb
+++ b/examples/rllib.ipynb
@@ -182,17 +182,17 @@
     "from itertools import islice\n",
     "\n",
     "with make_env() as env:\n",
-    "  # The two datasets we will be using:\n",
-    "  npb = env.datasets[\"npb-v0\"]\n",
-    "  chstone = env.datasets[\"chstone-v0\"]\n",
+    "    # The two datasets we will be using:\n",
+    "    npb = env.datasets[\"npb-v0\"]\n",
+    "    chstone = env.datasets[\"chstone-v0\"]\n",
     "\n",
-    "  # Each dataset has a `benchmarks()` method that returns an iterator over the\n",
-    "  # benchmarks within the dataset. Here we will use iterator sliceing to grab a \n",
-    "  # handful of benchmarks for training and validation.\n",
-    "  train_benchmarks = list(islice(npb.benchmarks(), 55))\n",
-    "  train_benchmarks, val_benchmarks = train_benchmarks[:50], train_benchmarks[50:]\n",
-    "  # We will use the entire chstone-v0 dataset for testing.\n",
-    "  test_benchmarks = list(chstone.benchmarks())\n",
+    "    # Each dataset has a `benchmarks()` method that returns an iterator over the\n",
+    "    # benchmarks within the dataset. Here we will use iterator sliceing to grab a \n",
+    "    # handful of benchmarks for training and validation.\n",
+    "    train_benchmarks = list(islice(npb.benchmarks(), 55))\n",
+    "    train_benchmarks, val_benchmarks = train_benchmarks[:50], train_benchmarks[50:]\n",
+    "    # We will use the entire chstone-v0 dataset for testing.\n",
+    "    test_benchmarks = list(chstone.benchmarks())\n",
     "\n",
     "print(\"Number of benchmarks for training:\", len(train_benchmarks))\n",
     "print(\"Number of benchmarks for validation:\", len(val_benchmarks))\n",
@@ -221,11 +221,11 @@
     "from compiler_gym.wrappers import CycleOverBenchmarks\n",
     "\n",
     "def make_training_env(*args) -> compiler_gym.envs.CompilerEnv:\n",
-    "  \"\"\"Make a reinforcement learning environment that cycles over the\n",
-    "  set of training benchmarks in use.\n",
-    "  \"\"\"\n",
-    "  del args  # Unused env_config argument passed by ray\n",
-    "  return CycleOverBenchmarks(make_env(), train_benchmarks)\n",
+    "    \"\"\"Make a reinforcement learning environment that cycles over the\n",
+    "    set of training benchmarks in use.\n",
+    "    \"\"\"\n",
+    "    del args  # Unused env_config argument passed by ray\n",
+    "    return CycleOverBenchmarks(make_env(), train_benchmarks)\n",
     "\n",
     "tune.register_env(\"compiler_gym\", make_training_env)"
    ]
@@ -245,12 +245,12 @@
     "# Lets cycle through a few calls to reset() to demonstrate that this environment\n",
     "# selects a new benchmark for each episode.\n",
     "with make_training_env() as env:\n",
-    "  env.reset()\n",
-    "  print(env.benchmark)\n",
-    "  env.reset()\n",
-    "  print(env.benchmark)\n",
-    "  env.reset()\n",
-    "  print(env.benchmark)"
+    "    env.reset()\n",
+    "    print(env.benchmark)\n",
+    "    env.reset()\n",
+    "    print(env.benchmark)\n",
+    "    env.reset()\n",
+    "    print(env.benchmark)"
    ]
   },
   {
@@ -282,7 +282,7 @@
     "\n",
     "# (Re)Start the ray runtime.\n",
     "if ray.is_initialized():\n",
-    "  ray.shutdown()\n",
+    "    ray.shutdown()\n",
     "ray.init(include_dashboard=False, ignore_reinit_error=True)\n",
     "\n",
     "tune.register_env(\"compiler_gym\", make_training_env)\n",
@@ -370,18 +370,18 @@
     "# performance on a set of benchmarks.\n",
     "\n",
     "def run_agent_on_benchmarks(benchmarks):\n",
-    "  \"\"\"Run agent on a list of benchmarks and return a list of cumulative rewards.\"\"\"\n",
-    "  with make_env() as env:\n",
+    "    \"\"\"Run agent on a list of benchmarks and return a list of cumulative rewards.\"\"\"\n",
     "    rewards = []\n",
-    "    for i, benchmark in enumerate(benchmarks, start=1):\n",
-    "        observation, done = env.reset(benchmark=benchmark), False\n",
-    "        while not done:\n",
-    "            action = agent.compute_action(observation)\n",
-    "            observation, _, done, _ = env.step(action)\n",
-    "        rewards.append(env.episode_reward)\n",
-    "        print(f\"[{i}/{len(benchmarks)}] {env.state}\")\n",
+    "    with make_env() as env:\n",
+    "        for i, benchmark in enumerate(benchmarks, start=1):\n",
+    "            observation, done = env.reset(benchmark=benchmark), False\n",
+    "            while not done:\n",
+    "                action = agent.compute_action(observation)\n",
+    "                observation, _, done, _ = env.step(action)\n",
+    "            rewards.append(env.episode_reward)\n",
+    "            print(f\"[{i}/{len(benchmarks)}] {env.state}\")\n",
     "\n",
-    "  return rewards\n",
+    "    return rewards\n",
     "\n",
     "# Evaluate agent performance on the validation set.\n",
     "val_rewards = run_agent_on_benchmarks(val_benchmarks)"
@@ -417,14 +417,15 @@
    "outputs": [],
    "source": [
     "# Finally lets plot our results to see how we did!\n",
+    "%matplotlib inline\n",
     "from matplotlib import pyplot as plt\n",
     "\n",
     "def plot_results(x, y, name, ax):\n",
-    "  plt.sca(ax)\n",
-    "  plt.bar(range(len(y)), y)\n",
-    "  plt.ylabel(\"Reward (higher is better)\")\n",
-    "  plt.xticks(range(len(x)), x, rotation = 90)\n",
-    "  plt.title(f\"Performance on {name} set\")\n",
+    "    plt.sca(ax)\n",
+    "    plt.bar(range(len(y)), y)\n",
+    "    plt.ylabel(\"Reward (higher is better)\")\n",
+    "    plt.xticks(range(len(x)), x, rotation = 90)\n",
+    "    plt.title(f\"Performance on {name} set\")\n",
     "\n",
     "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
     "fig.set_size_inches(13, 3)\n",