diff --git a/docs/source/guide/basic_usage.rst b/docs/source/guide/basic_usage.rst
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+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.
+
+Step-Based Environments
+~~~~~~~~~~~~~~~~~~~~~~~
+
+Regular step based environments added by Fancy Gym are added into the
+``fancy/`` namespace.
+
+.. note::
+    Legacy versions of Fancy Gym used ``fancy_gym.make(...)``. This is no longer supported and will raise an Exception on new versions.
+
+.. code:: python
+
+   import gymnasium as gym
+   import fancy_gym
+
+   env = gym.make('fancy/Reacher5d-v0')
+   # or env = gym.make('metaworld/reach-v2') # fancy_gym allows access to all metaworld ML1 tasks via the metaworld/ NS
+   # or env = gym.make('dm_control/ball_in_cup-catch-v0')
+   # or env = gym.make('Reacher-v2')
+   observation = env.reset(seed=1)
+
+   for i in range(1000):
+       action = env.action_space.sample()
+       observation, reward, terminated, truncated, info = env.step(action)
+       if i % 5 == 0:
+           env.render()
+
+       if terminated or truncated:
+           observation, info = env.reset()
+
+Black-box Environments
+~~~~~~~~~~~~~~~~~~~~~~
+
+All environments provide by default the cumulative episode reward, this
+can however be changed if necessary. Optionally, each environment
+returns all collected information from each step as part of the infos.
+This information is, however, mainly meant for debugging as well as
+logging and not for training.
+
++---------------------+--------------------------------------------------------------------------------------------------------------------------------------------+----------+
+| Key                 | Description                                                                                                                                | Type     |
++=====================+============================================================================================================================================+==========+
+| `positions`         | Generated trajectory from MP                                                                                                               | Optional |
++---------------------+--------------------------------------------------------------------------------------------------------------------------------------------+----------+
+| `velocities`        | Generated trajectory from MP                                                                                                               | Optional |
++---------------------+--------------------------------------------------------------------------------------------------------------------------------------------+----------+
+| `step_actions`      | Step-wise executed action based on controller output                                                                                       | Optional |
++---------------------+--------------------------------------------------------------------------------------------------------------------------------------------+----------+
+| `step_observations` | Step-wise intermediate observations                                                                                                        | Optional |
++---------------------+--------------------------------------------------------------------------------------------------------------------------------------------+----------+
+| `step_rewards`      | Step-wise rewards                                                                                                                          | Optional |
++---------------------+--------------------------------------------------------------------------------------------------------------------------------------------+----------+
+| `trajectory_length` | Total number of environment interactions                                                                                                   | Always   |
++---------------------+--------------------------------------------------------------------------------------------------------------------------------------------+----------+
+| `other`             | All other information from the underlying environment are returned as a list with length `trajectory_length` maintaining the original key. | Always   |
+|                     | In case some information are not provided every time step, the missing values are filled with `None`.                                      |          |
++---------------------+--------------------------------------------------------------------------------------------------------------------------------------------+----------+
+
+Existing MP tasks can be created the same way as above. The namespace of
+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
+     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
+     implemented, it is still in beta and requires further testing. Feel
+     free to try it and open an issue with any problems that occur.
+
+.. code:: python
+
+   import gymnasium as gym
+   import fancy_gym
+
+   env = gym.make('fancy_ProMP/Reacher5d-v0')
+   # or env = gym.make('metaworld_ProDMP/reach-v2')
+   # or env = gym.make('dm_control_DMP/ball_in_cup-catch-v0')
+   # or env = gym.make('gym_ProMP/Reacher-v2') # mp versions of envs added directly by gymnasium are in the gym_<MP-type> NS
+
+   # render() can be called once in the beginning with all necessary arguments.
+   # To turn it of again just call render() without any arguments.
+   env.render(mode='human')
+
+   # This returns the context information, not the full state observation
+   observation, info = env.reset(seed=1)
+
+   for i in range(5):
+       action = env.action_space.sample()
+       observation, reward, terminated, truncated, info = env.step(action)
+
+       # terminated or truncated is always True as we are working on the episode level, hence we always reset()
+       observation, info = env.reset()
+
+To show all available environments, we provide some additional
+convenience variables. All of them return a dictionary with the keys
+``DMP``, ``ProMP``, ``ProDMP`` and ``all`` that store a list of
+available environment ids.
+
+.. code:: python
+
+   import fancy_gym
+
+   print("All Black-box tasks:")
+   print(fancy_gym.ALL_MOVEMENT_PRIMITIVE_ENVIRONMENTS)
+
+   print("Fancy Black-box tasks:")
+   print(fancy_gym.ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS)
+
+   print("OpenAI Gym Black-box tasks:")
+   print(fancy_gym.ALL_GYM_MOVEMENT_PRIMITIVE_ENVIRONMENTS)
+
+   print("Deepmind Control Black-box tasks:")
+   print(fancy_gym.ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS)
+
+   print("MetaWorld Black-box tasks:")
+   print(fancy_gym.ALL_METAWORLD_MOVEMENT_PRIMITIVE_ENVIRONMENTS)
+
+   print("If you add custom envs, their mp versions will be found in:")
+   print(fancy_gym.MOVEMENT_PRIMITIVE_ENVIRONMENTS_FOR_NS['<my_custom_namespace>'])
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diff --git a/docs/source/guide/episodic_rl.rst b/docs/source/guide/episodic_rl.rst
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+What is Episodic RL?
+--------------------
+
+.. raw:: html
+
+   <p align="justify">
+
+Movement primitive (MP) environments differ from traditional step-based
+environments. They align more with concepts from stochastic search,
+black-box optimization, and methods commonly found in classical robotics
+and control. Instead of individual steps, MP environments operate on an
+episode basis, executing complete trajectories. These trajectories are
+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
+chosen depends on the environment’s requirements. Currently, we support
+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
+motion primitives to craft the right trajectory. Our framework further
+enhances this by accommodating a contextual setting. At the episode’s
+onset, we present the context space—a subset of the observation space.
+This demands the prediction of a new action or MP parametrization for
+every unique context.
+
+.. raw:: html
+
+   </p>
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diff --git a/docs/source/guide/installation.rst b/docs/source/guide/installation.rst
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+Installation
+------------
+
+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/>`__
+or `Conda <https://docs.conda.io/en/latest/>`__ can also be used.
+
+Installation from PyPI (recommended)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Install ``fancy_gym`` via
+
+.. code:: bash
+
+   pip install fancy_gym
+
+We have a few optional dependencies. If you also want to install those
+use
+
+.. code:: bash
+
+   # to install all optional dependencies
+   pip install 'fancy_gym[all]'
+
+   # or choose only those you want
+   pip install 'fancy_gym[dmc,box2d,mujoco-legacy,jax,testing]'
+
+Pip can not automatically install up-to-date versions of metaworld,
+since they are not avaible on PyPI yet. Install metaworld via
+
+.. code:: bash
+
+   pip install metaworld@git+https://github.com/Farama-Foundation/Metaworld.git@d155d0051630bb365ea6a824e02c66c068947439#egg=metaworld
+
+Installation from master
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+1. Clone the repository
+
+.. code:: bash
+
+   git clone git@github.com:ALRhub/fancy_gym.git
+
+2. Go to the folder
+
+.. code:: bash
+
+   cd fancy_gym
+
+3. Install with
+
+.. code:: bash
+
+   pip install -e .
+
+We have a few optional dependencies. If you also want to install those
+use
+
+.. code:: bash
+
+   # to install all optional dependencies
+   pip install -e '.[all]'
+
+   # or choose only those you want
+   pip install -e '.[dmc,box2d,mujoco-legacy,jax,testing]'
+
+Metaworld has to be installed manually with
+
+.. code:: bash
+
+   pip install metaworld@git+https://github.com/Farama-Foundation/Metaworld.git@d155d0051630bb365ea6a824e02c66c068947439#egg=metaworld
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diff --git a/docs/source/guide/upgrading_envs.rst b/docs/source/guide/upgrading_envs.rst
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+Creating new MP Environments
+----------------------------
+
+In case a required task is not supported yet in the MP framework, it can
+be created relatively easy. For the task at hand, the following
+`interface <https://github.com/ALRhub/fancy_gym/tree/master/fancy_gym/black_box/raw_interface_wrapper.py>`__
+needs to be implemented.
+
+.. code:: python
+
+   from abc import abstractmethod
+   from typing import Union, Tuple
+
+   import gymnasium as gym
+   import numpy as np
+
+
+   class RawInterfaceWrapper(gym.Wrapper):
+       mp_config = {
+           'ProMP': {},
+           'DMP': {},
+           'ProDMP': {},
+       }
+
+       @property
+       def context_mask(self) -> np.ndarray:
+           """
+               Returns boolean mask of the same shape as the observation space.
+               It determines whether the observation is returned for the contextual case or not.
+               This effectively allows to filter unwanted or unnecessary observations from the full step-based case.
+               E.g. Velocities starting at 0 are only changing after the first action. Given we only receive the
+               context/part of the first observation, the velocities are not necessary in the observation for the task.
+               Returns:
+                   bool array representing the indices of the observations
+           """
+           return np.ones(self.env.observation_space.shape[0], dtype=bool)
+
+       @property
+       @abstractmethod
+       def current_pos(self) -> Union[float, int, np.ndarray, Tuple]:
+           """
+               Returns the current position of the action/control dimension.
+               The dimensionality has to match the action/control dimension.
+               This is not required when exclusively using velocity control,
+               it should, however, be implemented regardless.
+               E.g. The joint positions that are directly or indirectly controlled by the action.
+           """
+           raise NotImplementedError()
+
+       @property
+       @abstractmethod
+       def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
+           """
+               Returns the current velocity of the action/control dimension.
+               The dimensionality has to match the action/control dimension.
+               This is not required when exclusively using position control,
+               it should, however, be implemented regardless.
+               E.g. The joint velocities that are directly or indirectly controlled by the action.
+           """
+           raise NotImplementedError()
+
+Default configurations for MPs can be overitten by defining attributes
+in mp_config. Available parameters are documented in the `MP_PyTorch
+Userguide <https://github.com/ALRhub/MP_PyTorch/blob/main/doc/README.md>`__.
+
+.. code:: python
+
+   class RawInterfaceWrapper(gym.Wrapper):
+       mp_config = {
+           'ProMP': {
+               'phase_generator_kwargs': {
+                   'phase_generator_type': 'linear'
+                   # When selecting another generator type, the default configuration will not be merged for the attribute.
+               },
+               'controller_kwargs': {
+                   'p_gains': 0.5 * np.array([1.0, 4.0, 2.0, 4.0, 1.0, 4.0, 1.0]),
+                   'd_gains': 0.5 * np.array([0.1, 0.4, 0.2, 0.4, 0.1, 0.4, 0.1]),
+               },
+               'basis_generator_kwargs': {
+                   'num_basis': 3,
+                   'num_basis_zero_start': 1,
+                   'num_basis_zero_goal': 1,
+               },
+           },
+           'DMP': {},
+           'ProDMP': {}.
+       }
+
+       [...]
+
+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/>`__.
+
+If the step-based is already registered with gym, you can simply do the
+following:
+
+.. code:: python
+
+   fancy_gym.upgrade(
+       id='custom/cool_new_env-v0',
+       mp_wrapper=my_custom_MPWrapper
+   )
+
+If the step-based is not yet registered with gym we can add both the
+step-based and MP-versions via
+
+.. code:: python
+
+   fancy_gym.register(
+       id='custom/cool_new_env-v0',
+       entry_point=my_custom_env,
+       mp_wrapper=my_custom_MPWrapper
+   )
+
+From this point on, you can access MP-version of your environments via
+
+.. code:: python
+
+   env = gym.make('custom_ProDMP/cool_new_env-v0')
+
+   rewards = 0
+   observation, info = env.reset()
+
+   # number of samples/full trajectories (multiple environment steps)
+   for i in range(5):
+       ac = env.action_space.sample()
+       observation, reward, terminated, truncated, info = env.step(ac)
+       rewards += reward
+
+       if terminated or truncated:
+           print(rewards)
+           rewards = 0
+           observation, info = env.reset()
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