AWS Certified AI Practitioner AIF-C01 Practice Question

Reinforcement learning trains an agent by allowing it to interact with an environment, observe the resulting state, and receive a reward signal. The agent learns a policy that maximizes cumulative reward through trial and error. Supervised learning relies on labeled examples with known target outputs, unsupervised learning searches for hidden structure in unlabeled data, and transfer learning starts from a pre-trained model rather than learning solely from reward feedback in an environment.