What is the term for training machine learning models to make a sequence of decisions?

What is the term for training machine learning models to make a sequence of decisions?

• A. Supervised learning
• B. Unsupervised learning
• C. Predictive AI models
• D. Reinforcement learning

Answer: (D)

When models are trained to act over time and improve based on the consequences of their actions, the approach is reinforcement learning. In this framework, an agent interacts with an environment across a sequence of steps. At each step, it selects an action, receives feedback in the form of a reward, and the environment transitions to a new state. The goal is to learn a policy—basically a strategy for choosing actions—that maximizes the total reward accumulated over time. This emphasis on long-term decision making and feedback from actions distinguishes it from other learning paradigms. Supervised learning relies on labeled examples to map inputs to outputs in single-shot tasks, without the iterative, reward-driven sequence. Unsupervised learning seeks patterns or structure without any reward signals. Predictive AI models is a vague term that doesn’t inherently capture the sequential decision-making and feedback aspect. So reinforcement learning is the best fit for training models to make a sequence of decisions.

When models are trained to act over time and improve based on the consequences of their actions, the approach is reinforcement learning. In this framework, an agent interacts with an environment across a sequence of steps. At each step, it selects an action, receives feedback in the form of a reward, and the environment transitions to a new state. The goal is to learn a policy—basically a strategy for choosing actions—that maximizes the total reward accumulated over time. This emphasis on long-term decision making and feedback from actions distinguishes it from other learning paradigms. Supervised learning relies on labeled examples to map inputs to outputs in single-shot tasks, without the iterative, reward-driven sequence. Unsupervised learning seeks patterns or structure without any reward signals. Predictive AI models is a vague term that doesn’t inherently capture the sequential decision-making and feedback aspect. So reinforcement learning is the best fit for training models to make a sequence of decisions.