Fix typos and grammar mistakes (#2140)

Author: omahs

README.md

@@ -116,7 +116,7 @@ Install the Stable Baselines3 package:
 pip install 'stable-baselines3[extra]'
 ```
 
-This includes an optional dependencies like Tensorboard, OpenCV or `ale-py` to train on atari games. If you do not need those, you can use:
+This includes optional dependencies like Tensorboard, OpenCV or `ale-py` to train on atari games. If you do not need those, you can use:
 ```sh
 pip install stable-baselines3
 ```

docs/guide/algos.rst

@@ -78,7 +78,7 @@ Credit: part of the *Reproducibility* section comes from `PyTorch Documentation
 Training exceeds ``total_timesteps``
 ------------------------------------
 
-When you train an agent using SB3, you pass a ``total_timesteps`` parameter to the ``learn()`` method which defines the training budget for the agent (how many interaction with the environment are allowed).
+When you train an agent using SB3, you pass a ``total_timesteps`` parameter to the ``learn()`` method which defines the training budget for the agent (how many interactions with the environment are allowed).
 For example:
 
 .. code-block:: python

docs/guide/callbacks.rst

@@ -412,7 +412,7 @@ It must be used with the :ref:`EvalCallback` and use the event triggered after e
 
     model = SAC("MlpPolicy", "Pendulum-v1", learning_rate=1e-3, verbose=1)
     # Almost infinite number of timesteps, but the training will stop early
-    # as soon as the the number of consecutive evaluations without model
+    # as soon as the number of consecutive evaluations without model
     # improvement is greater than 3
     model.learn(int(1e10), callback=eval_callback)
 

docs/guide/checking_nan.rst

@@ -145,12 +145,12 @@ It will monitor the actions, observations, and rewards, indicating what action o
 RL Model hyperparameters
 ------------------------
 
-Depending on your hyperparameters, NaN can occurs much more often.
+Depending on your hyperparameters, NaN can occur much more often.
 A great example of this: https://github.com/hill-a/stable-baselines/issues/340
 
 Be aware, the hyperparameters given by default seem to work in most cases,
 however your environment might not play nice with them.
-If this is the case, try to read up on the effect each hyperparameters has on the model,
+If this is the case, try to read up on the effect each hyperparameter has on the model,
 so that you can try and tune them to get a stable model. Alternatively, you can try automatic hyperparameter tuning (included in the rl zoo).
 
 Missing values from datasets

docs/guide/custom_env.rst

@@ -102,7 +102,7 @@ To check that your environment follows the Gym interface that SB3 supports, plea
 
 Gymnasium also have its own `env checker <https://gymnasium.farama.org/api/utils/#gymnasium.utils.env_checker.check_env>`_ but it checks a superset of what SB3 supports (SB3 does not support all Gym features).
 
-We have created a `colab notebook <https://colab.research.google.com/github/araffin/rl-tutorial-jnrr19/blob/sb3/5_custom_gym_env.ipynb>`_ for a concrete example on creating a custom environment along with an example of using it with Stable-Baselines3 interface.
+We have created a `colab notebook <https://colab.research.google.com/github/araffin/rl-tutorial-jnrr19/blob/sb3/5_custom_gym_env.ipynb>`_ for a concrete example of creating a custom environment along with an example of using it with Stable-Baselines3 interface.
 
 Alternatively, you may look at Gymnasium `built-in environments <https://gymnasium.farama.org>`_.
 

docs/guide/custom_policy.rst

@@ -16,7 +16,7 @@ other type of input features (MlpPolicies) and multiple different inputs (MultiI
 SB3 Policy
 ^^^^^^^^^^
 
-SB3 networks are separated into two mains parts (see figure below):
+SB3 networks are separated into two main parts (see figure below):
 
 - A features extractor (usually shared between actor and critic when applicable, to save computation)
   whose role is to extract features (i.e. convert to a feature vector) from high-dimensional observations, for instance, a CNN that extracts features from images.

docs/guide/examples.rst

@@ -247,7 +247,7 @@ If your callback returns False, training is aborted early.
 
       :param check_freq:
       :param log_dir: Path to the folder where the model will be saved.
-        It must contains the file created by the ``Monitor`` wrapper.
+        It must contain the file created by the ``Monitor`` wrapper.
       :param verbose: Verbosity level: 0 for no output, 1 for info messages, 2 for debug messages
       """
       def __init__(self, check_freq: int, log_dir: str, verbose: int = 1):
@@ -702,7 +702,7 @@ A2C policy gradient updates on the model.
       if ("policy" in key or "shared_net" in key or "action" in key)
   )
 
-  # population size of 50 invdiduals
+  # population size of 50 individuals
   pop_size = 50
   # Keep top 10%
   n_elite = pop_size // 10

docs/guide/export.rst

@@ -167,7 +167,7 @@ For more discussion around the topic, please refer to `GH#383 <https://github.co
 Trace/Export to C++
 -------------------
 
-You can use PyTorch JIT to trace and save a trained model that can be re-used in other applications
+You can use PyTorch JIT to trace and save a trained model that can be reused in other applications
 (for instance inference code written in C++).
 
 There is a draft PR in the RL Zoo about C++ export: https://github.com/DLR-RM/rl-baselines3-zoo/pull/228

docs/guide/install.rst

@@ -18,7 +18,7 @@ For a quick start you can move straight to installing Stable-Baselines3 in the n
 
 .. note::
 
-	Trying to create Atari environments may result to vague errors related to missing DLL files and modules. This is an
+	Trying to create Atari environments may result in vague errors related to missing DLL files and modules. This is an
 	issue with atari-py package. `See this discussion for more information <https://github.com/openai/atari-py/issues/65>`_.
 
 
@@ -34,7 +34,7 @@ To install Stable Baselines3 with pip, execute:
         Some shells such as Zsh require quotation marks around brackets, i.e. ``pip install 'stable-baselines3[extra]'`` `More information <https://stackoverflow.com/a/30539963>`_.
 
 
-This includes an optional dependencies like Tensorboard, OpenCV or ``ale-py`` to train on Atari games. If you do not need those, you can use:
+This includes optional dependencies like Tensorboard, OpenCV or ``ale-py`` to train on Atari games. If you do not need those, you can use:
 
 .. code-block:: bash
 
@@ -151,7 +151,7 @@ Explanation of the docker command:
    run it interactively (so ctrl+c will work)
 -  ``--rm`` option means to remove the container once it exits/stops
    (otherwise, you will have to use ``docker rm``)
--  ``--network host`` don't use network isolation, this allow to use
+-  ``--network host`` don't use network isolation, this allows to use
    tensorboard/visdom on host machine
 -  ``--ipc=host`` Use the host system’s IPC namespace. IPC (POSIX/SysV IPC) namespace provides
    separation of named shared memory segments, semaphores and message

docs/guide/integrations.rst

@@ -175,7 +175,7 @@ With ``package_to_hub()``
   # Train the agent
   model.learn(total_timesteps=int(5000))
 
-  # This method save, evaluate, generate a model card and record a replay video of your agent before pushing the repo to the hub
+  # This method saves, evaluates, generates a model card and records a replay video of your agent before pushing the repo to the hub
   package_to_hub(model=model,
                model_name="ppo-CartPole-v1",
                model_architecture="PPO",
@@ -219,7 +219,7 @@ With ``push_to_hub()``
   model.save("ppo-CartPole-v1")
 
   # Push this saved model .zip file to the hf repo
-  # If this repo does not exists it will be created
+  # If this repo does not exist it will be created
   ## repo_id = id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name})
   ## filename: the name of the file == "name" inside model.save("ppo-CartPole-v1")
   push_to_hub(