AWS Certified AI Practitioner

Explanation

This scenario describes a classic reinforcement learning problem because:

1. Reward/Penalty System: The AI receives rewards for faster routes and penalties for delays, which is the core mechanism of reinforcement learning where an agent learns through trial-and-error interactions with an environment.

2. Learning from Feedback: The system improves decisions based on feedback (rewards/penalties) from previous deliveries, which aligns with the reinforcement learning paradigm of learning optimal policies through environmental feedback.

3. Sequential Decision Making: Route optimization involves making a sequence of decisions (which turns to take, which roads to use) to reach an optimal outcome, which is characteristic of reinforcement learning problems.

Why other options are incorrect:

• A) Unsupervised learning: This involves finding patterns in data without explicit labels or feedback, not learning from rewards/penalties.
• B) Self-supervised learning: This uses the data itself to create supervisory signals, not external rewards/penalties.
• D) Transfer learning: This involves applying knowledge from one domain to another, not learning through reward-based feedback.

Reinforcement learning is particularly well-suited for optimization problems where an agent must learn to make sequential decisions to maximize cumulative rewards over time, exactly as described in this delivery-route optimization scenario.