Which design aims to optimize an AI’s behavior based on feedback from the environment?

• A. Reinforcement Learning
• B. Unsupervised Learning
• C. Markov Chains
• D. Expert Systems

Answer: (A)

Reinforcement learning focuses on an agent that learns to optimize its behavior by receiving feedback from the environment. At each step, the agent chooses an action, the environment responds with a new state and a reward, and the agent uses that feedback to improve its decision-making over time. The aim is to maximize the cumulative reward, so the agent learns which actions lead to the best long-term outcomes, even when rewards are delayed. This feedback loop—actions, environment response, and reward guidance—drives the learning process and policy improvement. In contrast, unsupervised learning looks for structure in data without any reward signal, Markov chains model state transitions without an optimization focus on behavior, and expert systems rely on fixed, human-defined rules rather than learning from environmental feedback.