Updates across the board

docs/introduction.rst

@@ -100,12 +100,12 @@ matters.
 
 **Observation design** determines what information is available to the
 agent. If you leave a character out of the ``character_set``, the agent
-will not distinguish it from empty space. If you include a game-state
-variable in ``observe_state``, the agent can see it directly rather than
-having to infer it from the board. The consequences of these choices for
-what the agent can learn are reasonably predictable—and making and
-checking those predictions is exactly the kind of reasoning the tool is
-designed to support.
+will not distinguish it from empty space. If the game module defines a
+``get_state()`` function, the agent also receives those computed values
+as part of its observation. The consequences of these choices for what
+the agent can learn are reasonably predictable — and making and checking
+those predictions is exactly the kind of reasoning the tool is designed
+to support.
 
 **Reward engineering** is the craft of specifying what counts as doing
 well in a way the agent can actually optimize. Using score as the reward