Documentation cleanup

Author: AntoinePrv

docs/discussion/seeding.rst

@@ -8,7 +8,7 @@ One such aspect is the solver randomness, which is controlled by its random seed
 
 This means that, by default, Ecole environment will generate different episodes (and in
 particular different initial states) after each new call to
-:py:meth:`~ecole.environment.EnvironmentComposer.reset`.
+:py:meth:`~ecole.environment.Environment.reset`.
 To do so, the environment keeps a :py:class:`~ecole.RandomGenerator` (random state)
 between episodes, and start a new episode by calling
 :py:meth:`~ecole.typing.Dynamics.set_dynamics_random_state` on the underlying

docs/howto/create-environments.rst

@@ -117,13 +117,13 @@ To do so, we will take parameters in the constructor.
        __Dynamics__ = SimpleBranchingDynamics
 
 
-The constructor arguments are forwarded from the :py:meth:`~ecole.environment.EnvironmentComposer.__init__` constructor:
+The constructor arguments are forwarded from the :py:meth:`~ecole.environment.Environment.__init__` constructor:
 
 .. testcode::
    :skipif: pyscipopt is None
 
    env = SimpleBranching(observation_function=None, disable_cuts=False)
 
-Similarily, extra arguments given to the environemnt :py:meth:`~ecole.environment.EnvironmentComposer.reset` and
-:py:meth:`~ecole.environment.EnvironmentComposer.step` are forwarded to the associated
+Similarily, extra arguments given to the environemnt :py:meth:`~ecole.environment.Environment.reset` and
+:py:meth:`~ecole.environment.Environment.step` are forwarded to the associated
 :py:class:`~ecole.typing.Dynamics` methods.

docs/howto/instances.rst

@@ -59,7 +59,7 @@ With an Environment
 -------------------
 The instance objects generated by :py:class:`~ecole.typing.InstanceGenerator`s,
 of type :py:class:`ecole.scip.Model`, can be passed directly to an environment's
-:py:meth:`~ecole.environment.EnvironmentComposer.reset` method.
+:py:meth:`~ecole.environment.Environment.reset` method.
 
 A typical example training over 1000 instances/episodes would look like:
 
@@ -78,7 +78,7 @@ A typical example training over 1000 instances/episodes would look like:
 
 .. note::
    The generated instance objects can be, in principle, modified between their generation and their usage in an environment
-   :py:meth:`~ecole.environment.EnvironmentComposer.reset` method. To keep code clean, however, we recommend that such modifications
+   :py:meth:`~ecole.environment.Environment.reset` method. To keep code clean, however, we recommend that such modifications
    be wrapped in a custom environment class. Details about custom environments :ref:`can be found here<create-new-environment>`.