IBM AI Fundamentals: Reinforcement Learning AI Systems Using Trial and Error

Learn about reinforcement learning, a powerful AI technique where systems learn through trial and error to maximize rewards. Prepare for the IBM AI Fundamentals exam.

Question

Which method of learning does an AI system use when it uses the process of trial and error?

A. Reinforcement learning
B. Supervised learning
C. Classical conditioning
D. Unsupervised learning

Answer

A. Reinforcement learning

Explanation

Reinforcement learning uses trial and error to allow systems to learn. You’ve probably had a similar experience when you win or lose while you learn to play a game.

Reinforcement learning is a type of machine learning where an AI agent learns by interacting with its environment through trial and error. The agent takes actions and receives feedback in the form of rewards or penalties. Over many iterations, it learns to maximize its cumulative reward by discovering optimal behaviors through this process of trial and error and reinforcement.

The key aspects of reinforcement learning are:

1. The agent explores its environment by taking various actions.
2. The environment provides feedback signals (rewards or penalties) in response to those actions.
3. The agent adjusts its decision-making policy based on the feedback to maximize its total rewards over time.
4. Through repeated interactions and trial and error, the agent gradually learns and converges on an optimal policy.

Reinforcement learning is well-suited for sequential decision-making problems like game-playing, robotics, and autonomous systems. The other learning paradigms mentioned (supervised, unsupervised, classical conditioning) do not inherently involve learning by trial and error through interaction with an environment.

So in summary, when an AI system learns through a process of taking actions, receiving feedback, and iteratively improving its behavior to maximize rewards, it is using the reinforcement learning paradigm. The repeated trial and error allows it to discover good policies.

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