Better guide

docs/source/guide/basic_usage.rst

@@ -1,9 +1,7 @@
 Basic Usage
 -----------
 
-We will only show the basics here and prepared `multiple
-examples <https://github.com/ALRhub/fancy_gym/tree/master/fancy_gym/examples/>`__
-for a more detailed look.
+We will only show the basics here and prepared :ref:`multiple examples <example-general>` for a more detailed look.
 
 Step-Based Environments
 ~~~~~~~~~~~~~~~~~~~~~~~
@@ -34,7 +32,7 @@ Regular step based environments added by Fancy Gym are added into the
        if terminated or truncated:
            observation, info = env.reset()
 
-Black-box Environments
+Black-Box Environments
 ~~~~~~~~~~~~~~~~~~~~~~
 
 All environments provide by default the cumulative episode reward, this
@@ -67,8 +65,8 @@ a MP-variant of an environment is given by
 ``<original namespace>_<MP name>/``. Just keep in mind, calling
 ``step()`` executes a full trajectory.
 
-   | **Note:**
-   | Currently, we are also in the process of enabling replanning as
+.. note::
+    Currently, we are also in the process of enabling replanning as
     well as learning of sub-trajectories. This allows to split the
     episode into multiple trajectories and is a hybrid setting between
     step-based and black-box leaning. While this is already

docs/source/guide/episodic_rl.rst

@@ -3,7 +3,7 @@ What is Episodic RL?
 
 .. raw:: html
 
-   <p align="justify">
+    <div class="justify">
 
 Movement primitive (MP) environments differ from traditional step-based
 environments. They align more with concepts from stochastic search,
@@ -14,13 +14,6 @@ produced by trajectory generators like Dynamic Movement Primitives
 (DMP), Probabilistic Movement Primitives (ProMP) or Probabilistic
 Dynamic Movement Primitives (ProDMP).
 
-.. raw:: html
-
-   </p>
-
-.. raw:: html
-
-   <p align="justify">
 
 Once generated, these trajectories are converted into step-by-step
 actions using a trajectory tracking controller. The specific controller
@@ -29,13 +22,6 @@ position, velocity, and PD-Controllers tailored for position, velocity,
 and torque control. Additionally, we have a specialized controller
 designed for the MetaWorld control suite.
 
-.. raw:: html
-
-   </p>
-
-.. raw:: html
-
-   <p align="justify">
 
 While the overarching objective of MP environments remains the learning
 of an optimal policy, the actions here represent the parametrization of
@@ -47,4 +33,4 @@ every unique context.
 
 .. raw:: html
 
-   </p>
+    </div>

docs/source/guide/installation.rst

@@ -1,6 +1,7 @@
 Installation
 ------------
 
+.. note::
    We recommend installing ``fancy_gym`` into a virtual environment as
    provided by `venv <https://docs.python.org/3/library/venv.html>`__. 3rd
    party alternatives to venv like `Poetry <https://python-poetry.org/>`__

docs/source/guide/upgrading_envs.rst

@@ -92,7 +92,7 @@ If you created a new task wrapper, feel free to open a PR, so we can
 integrate it for others to use as well. Without the integration the task
 can still be used. A rough outline can be shown here, for more details
 we recommend having a look at the
-`examples <https://github.com/ALRhub/fancy_gym/tree/master/fancy_gym/examples/>`__.
+:ref:`multiple examples <example-mp>`.
 
 If the step-based is already registered with gym, you can simply do the
 following: