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.
BabySnake's tabular Q-learning exercise needs a small discrete state,
not the flattened board encoding retro_gamer normally produces. Define
get_state(game) in babysnake_env.py, returning a plain
(agent_x, agent_y, food_x, food_y) tuple, and point babysnake's
observation_function at it instead of declaring a character_set.
Disable the on-screen state overlay so it doesn't clutter the
terminal watch view.
Previously the game ran indefinitely and relied entirely on
max_turns_per_episode to end an episode. Add start/food energy values
so an episode ends on its own when energy runs out, and mention
retro-gamer plot in the lab's training log instructions.
