Reword tutorial to use Random player.

docs/tutorials/further_topics/approximate_moran_processes.rst

@@ -5,22 +5,32 @@ Approximate Moran Process
 
 Due to the high computational cost of a single Moran process, an approximate
 Moran process is implemented that can make use of cached outcomes of games. The
-following code snippet will generate a Moran process in which a `Defector`
-cooperates (gets a high score) against another `Defector`. First the cache is
-built by passing counter objects of outcomes::
+following code snippet will generate a Moran process in which the outcomes of
+the matches played by a :code:`Random: 0.5` are sampled from one possible
+outcome against each opponent (:code:`Defector` and :code:`Random: 0.5`). First
+the cache is built by passing counter objects of outcomes::
 
     >>> import axelrod as axl
     >>> from collections import Counter
     >>> cached_outcomes = {}
-    >>> cached_outcomes[("Cooperator", "Defector")] = axl.Pdf(Counter([(0, 5)]))
-    >>> cached_outcomes[("Cooperator", "Cooperator")] = axl.Pdf(Counter([(3, 3)]))
-    >>> cached_outcomes[("Defector", "Defector")] = axl.Pdf(Counter([(10, 10), (9, 9)]))
+    >>> cached_outcomes[("Random: 0.5", "Defector")] = axl.Pdf(Counter([(1, 1)]))
+    >>> cached_outcomes[("Random: 0.5", "Random: 0.5")] = axl.Pdf(Counter([(3, 3)]))
+    >>> cached_outcomes[("Defector", "Defector")] = axl.Pdf(Counter([(1, 1)]))
 
 Now let us create an Approximate Moran Process::
 
-    >>> axl.seed(0)
-    >>> players = [axl.Cooperator(), axl.Defector(), axl.Defector(), axl.Defector()]
+    >>> axl.seed(2)
+    >>> players = [axl.Defector(), axl.Random(), axl.Random()]
     >>> amp = axl.ApproximateMoranProcess(players, cached_outcomes)
     >>> results = amp.play()
     >>> amp.population_distribution()
-    Counter({'Defector': 4})
+    Counter({'Random: 0.5': 3})
+
+We see that the :code:`Random: 0.5` won this Moran process. This is not what happens in a
+standard Moran process where the `Random: 0.5` player will not win::
+
+    >>> axl.seed(2)
+    >>> amp = axl.MoranProcess(players)
+    >>> results = amp.play()
+    >>> amp.population_distribution()
+    Counter({'Defector': 3})

docs/tutorials/getting_started/moran.rst

@@ -24,7 +24,7 @@ the library, proceed as follows::
     >>> import axelrod as axl
     >>> axl.seed(0)
     >>> players = [axl.Cooperator(), axl.Defector(),
-    ...               axl.TitForTat(), axl.Grudger()]
+    ...            axl.TitForTat(), axl.Grudger()]
     >>> mp = axl.MoranProcess(players)
     >>> populations = mp.play()
     >>> mp.winning_strategy_name