Minor fixes to docs (#118)

* fix link typo * cli doc typo * minari version dataset docs --------- Co-authored-by: rodrigodelazcano <[email protected]>
Farama-Foundation · Jul 18, 2023 · b702a05 · b702a05
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diff --git a/docs/_scripts/gen_dataset_md.py b/docs/_scripts/gen_dataset_md.py
@@ -57,6 +57,7 @@
         email = dataset_spec["author_email"]
         algo_name = dataset_spec["algorithm_name"]
         code = dataset_spec["code_permalink"]
+        minari_version = dataset_spec["minari_version"]
 
         description = None
         if "description" in dataset_spec:
@@ -112,6 +113,7 @@
 | Author              | `{author}`              |
 | Email               | `{email}`               |
 | Code Permalink      | `{code}`                |
+| Minari Version      | `{minari_version}`      |
 | download            | `minari.download_dataset("{dataset_id}")` |
 
 

diff --git a/docs/content/minari_cli.md b/docs/content/minari_cli.md
@@ -103,7 +103,7 @@ current content of the local dataset.
 ```{eval-rst}
 
 .. note::
-   The download is aborted if the remote dataset is not compatible with your local installed Minari version or through a warning if the dataset already exists locally. To perform a force download add :code:`--force` or :code: `-f` to the download command.
+   The download is aborted if the remote dataset is not compatible with your local installed Minari version or through a warning if the dataset already exists locally. To perform a force download add :code:`--force` or :code:`-f` to the download command.
 
 ```
 

diff --git a/docs/tutorials/using_datasets/behavioral_cloning.py b/docs/tutorials/using_datasets/behavioral_cloning.py
@@ -11,7 +11,7 @@
 # %%
 # Policy training
 # ~~~~~~~~~~~~~~~~~~~
-# To train the expert policy, we use `SB3 <https://github.com/DLR-RM/stable-baselines3>`_'s <rl-zoo3 <https://github.com/DLR-RM/rl-baselines3-zoo>`_ library.
+# To train the expert policy, we use `SB3 <https://github.com/DLR-RM/stable-baselines3>`_'s `rl-zoo3 <https://github.com/DLR-RM/rl-baselines3-zoo>`_ library.
 # After installing the library with ``pip install rl_zoo3``,
 # we train a PPO agent on the environment with the following command:
 #