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+****************
+Exploring Models
+****************
+
+Background
+=====================
+
+Often, stormpy is used as a testbed for new algorithms.
+An essential step is to transfer the (low-level) descriptions of an MDP or other state-based model into
+an own algorithm. In this section, we discuss some of the functionality.
+
+Reading MDPs
+=====================
+
+.. seealso:: `01-exploration.py <https://github.com/moves-rwth/stormpy/blob/master/examples/exploration/01-exploration.py>`_
+
+In :doc:`../getting_started`, we briefly iterated over a DTMC. In this section, we explore an MDP::
+
+    >>> import doctest
+    >>> doctest.ELLIPSIS_MARKER = '-etc-' # doctest:+ELLIPSIS
+    >>> import stormpy
+    >>> import stormpy.examples
+    >>> import stormpy.examples.files
+    >>> program = stormpy.parse_prism_program(stormpy.examples.files.prism_mdp_maze)
+    >>> prop = "R=? [F \"goal\"]"
+
+    >>> properties = stormpy.parse_properties_for_prism_program(prop, program, None)
+    >>> model = stormpy.build_model(program, properties)
+
+The iteration over the model is as before, but now, for every action, we can have several transitions::
+
+    >>> for state in model.states:
+    ...    if state.id in model.initial_states:
+    ...        print("State {} is initial".format(state.id))
+    ...    for action in state.actions:
+    ...        for transition in action.transitions:
+    ...            print("From state {} by action {}, with probability {}, go to state {}".format(state, action, transition.value(), transition.column))
+    -etc-
+
+The output (omitted for brievety) contains sentences like:
+
+    From state 1 by action 0, with probability 1.0, go to state 2
+    From state 1 by action 1, with probability 1.0, go to state 1
+
+
+
+Internally, storm can hold hints to the origin of the actions, which may be helpful to give meaning and for debugging.
+As the availability and the encoding of this data depends on the input model, we discuss these features in :doc:`highlevel_models`.
+
+
+Storm currently supports deterministic rewards on states or actions. More information can be found in that :doc:`reward_models`.
+
+
+Reading POMDPs
+======================
+.. seealso:: `02-exploration.py <https://github.com/moves-rwth/stormpy/blob/master/examples/exploration/01-exploration.py>`_
+
+
+Internally, POMDPs extend MDPs. Thus, iterating over the MDP is done as before.
+
+    >>> import stormpy
+    >>> import stormpy.examples
+    >>> import stormpy.examples.files
+    >>> program = stormpy.parse_prism_program(stormpy.examples.files.prism_pomdp_maze)
+    >>> prop = "R=? [F \"goal\"]"
+
+    >>> properties = stormpy.parse_properties_for_prism_program(prop, program, None)
+    >>> model = stormpy.build_model(program, properties)
+
+Indeed, all that changed in the code above is the example we use.
+And, that the model type now is a POMDP::
+
+    >>> print(model.model_type)
+    ModelType.POMDP
+
+Additionally, POMDPs have a set of observations, which are internally just numbered by an integer from 0 to the number of observations -1 ::
+
+    >>> print(model.nr_observations)
+    8
+    >>> for state in model.states:
+    ...     print("State {} has observation id {}".format(state.id, model.observations[state.id]))
+    State 0 has observation id 6
+    State 1 has observation id 1
+    State 2 has observation id 4
+    State 3 has observation id 7
+    State 4 has observation id 4
+    State 5 has observation id 3
+    State 6 has observation id 0
+    State 7 has observation id 0
+    State 8 has observation id 0
+    State 9 has observation id 0
+    State 10 has observation id 0
+    State 11 has observation id 0
+    State 12 has observation id 2
+    State 13 has observation id 2
+    State 14 has observation id 4
+    State 15 has observation id 5
+
+
+
+
+
+Reading MAs
+======================
+
+To be continued...
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