Refactor CLI/interface: init command, factory field, remove extras_size

- Rename `retro-gamer create` to `retro-gamer init` with positional
  args (GAME OUTPUT) instead of --game/--output flags
- Add [tool.retro-gamer].factory = "module:attr" to declare the game
  factory function from pyproject.toml instead of relying on a
  hard-coded create_game attribute
- Remove extras_size from user-facing config; it is now measured
  automatically from a sample observation and never declared
- Update docs throughout: create→init, runs/→training/, add factory
  field documentation, clarify [model] vs [training] hyperparameter
  sections, remove stale version-history explanations
- Bump version to 0.3.0; require retro-games>=2.5.0

docs/introduction.rst

@@ -58,6 +58,7 @@ A typical workflow looks like this. First, describe your game in the
 .. code-block:: toml
 
    [tool.retro-gamer]
+   factory = "snake:create_game"
    actions = ["KEY_RIGHT", "KEY_UP", "KEY_LEFT", "KEY_DOWN"]
    reward = "score"
    character_set = ["@", "*", ">", "<", "^", "v"]
@@ -66,13 +67,13 @@ Then create a training run, train, and watch the result:
 
 .. code-block:: console
 
-   % retro-gamer create --game my_game --output runs/snake/
+   % retro-gamer init games/snake training/snake
 
-   % retro-gamer train runs/snake/
+   % retro-gamer train training/snake
 
-   % retro-gamer play runs/snake/ --checkpoint ep_0500
+   % retro-gamer play training/snake --checkpoint ep_0500
 
-The ``create`` command sets up the training run directory; ``train``
+The ``init`` command sets up the training run directory; ``train``
 runs the learning algorithm; ``play`` loads a checkpoint and lets you
 watch the trained agent live in the terminal.