Initialize the tabular value store and basic lookups over states and actions.
Implement exploration vs exploitation using random sampling and epsilon decay.
Compute TD targets, errors, and apply the Q-learning update to table entries.
Combine action selection, environment stepping, and updates into episodes and full training runs.
Derive a greedy policy from the trained Q-table and measure its success rate on FrozenLake.