Split q_learning.py into algorithm, environment glue, and a training script

q_learning.py mixed a bespoke BabySnake environment wrapper, the two
functions students implement, a training loop, and a terminal watch
routine in one file, with no tests and no single command to run
training.

- q_learning.py / q_learning_solution.py now hold only choose_action
  and update_q, with actions as an explicit parameter instead of a
  module-global, so the file has no babysnake/retro/retro_gamer
  imports at all.
- train_babysnake.py builds GameEnvironment directly from babysnake's
  pyproject metadata, trains, and watches the trained agent in one
  command: `python train_babysnake.py`. It also caps steps per
  episode, since a lucky random walk that keeps finding food can
  otherwise make an episode run unboundedly long.
- test_q_learning.py adds unittest coverage for both functions with no
  game dependency.
- questions.md adds a checkpoint instructing students to get the tests
  passing before training, and points the post-training step at
  train_babysnake.py.

questions.md

@@ -0,0 +1,51 @@
+# Questions
+
+## BabySnake
+
+## Checkpoint 1: Before training
+
+1. How do you decide where to move in BabySnake? Explain how to choose moves
+   in enough detail that someone else could follow your instructions.
+
+2. How many distinct states are there for BabySnake? If we assume that all four 
+   arrow keys are valid actions in every state, how many rows would the full Q-table contain?
+
+3. The discount factor γ (gamma) can range from 0 to 1. What would be the effect of setting
+   γ to 0? What about 1?
+
+4. The learning rate α (alpha) can also range from 0 to 1. What would be the effect of setting
+   α to 0? What about 1?
+
+5. Calculate the new Q-value for the situation described. Explain your answer.
+
+6. Implement `choose_action` and `update_q` in `q_learning.py`, then run
+
+   ```
+   python test_q_learning.py
+   ```
+
+   Get every test passing before moving on — errors are much easier to track
+   down here than during training.
+
+
+---
+
+## Checkpoint 2: After training
+
+Train your Q-learning agent to consistently score 3 or more food items per
+episode, then watch it play:
+
+```
+python train_babysnake.py
+```
+
+**At what episode did the agent start reliably finding food?**
+
+
+**Print `q_table` after training. Can you read the policy?** For a state you
+pick, does the highest Q-value point toward the food?
+
+
+**How does the trained agent's behavior compare to the reasoning you wrote
+down in Checkpoint 1?**
+