Change SQIL SAC to use Pendulum (#800)

docs/tutorials/8a_train_sqil_sac.ipynb

@@ -7,14 +7,14 @@
  "[download this notebook here](https://github.com/HumanCompatibleAI/imitation/blob/master/docs/tutorials/8a_train_sqil_sac.ipynb)\n",
  "# Train an Agent using Soft Q Imitation Learning with SAC\n",
  "\n",
- "In the previous tutorial, we used Soft Q Imitation Learning ([SQIL](https://arxiv.org/abs/1905.11108)) on top of the DQN base algorithm. In fact, SQIL can be combined with any off-policy algorithm from `stable_baselines3`. Here, we train a HalfCheetah agent using SQIL + SAC."
+ "In the previous tutorial, we used Soft Q Imitation Learning ([SQIL](https://arxiv.org/abs/1905.11108)) on top of the DQN base algorithm. In fact, SQIL can be combined with any off-policy algorithm from `stable_baselines3`. Here, we train a Pendulum agent using SQIL + SAC."
  ]
  },
  {
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "First, we need some expert trajectories in our environment (`seals/HalfCheetah-v0`).\n",
+ "First, we need some expert trajectories in our environment (`Pendulum-v1`).\n",
  "Note that you can use other environments, but the action space must be continuous."
  ]
  },
@@ -28,7 +28,7 @@
  "from imitation.data import huggingface_utils\n",
  "\n",
  "# Download some expert trajectories from the HuggingFace Datasets Hub.\n",
- "dataset = datasets.load_dataset(\"HumanCompatibleAI/ppo-seals-HalfCheetah-v0\")\n",
+ "dataset = datasets.load_dataset(\"HumanCompatibleAI/ppo-Pendulum-v1\")\n",
  "\n",
  "# Convert the dataset to a format usable by the imitation library.\n",
  "expert_trajectories = huggingface_utils.TrajectoryDatasetSequence(dataset[\"train\"])"
@@ -75,12 +75,11 @@
  "from imitation.util.util import make_vec_env\n",
  "import numpy as np\n",
  "from stable_baselines3 import sac\n",
- "import seals # noqa: F401 # needed to load \"seals/\" environments\n",
  "\n",
  "SEED = 42\n",
  "\n",
  "venv = make_vec_env(\n",
- " \"seals/HalfCheetah-v1\",\n",
+ " \"Pendulum-v1\",\n",
  " rng=np.random.default_rng(seed=SEED),\n",
  ")\n",
  "\n",