Discover how reinforcement learning is used to train AI systems in games like Go. Gain a comprehensive understanding of the different types of AI learning techniques and their applications in this in-depth exploration.
Table of Contents
Question
Henri is training an AI system. He has loaded the rules of the Chinese game of Go into the system. As the AI system plays its first few games, winning some and losing some, it gradually improves.
What type of learning is Henri using to train the AI system?
A. Unsupervised learning
B. Reinforcement learning
C. Supervised learning
Answer
B. Reinforcement learning
Explanation
Reinforcement learning was used to train the AI system in this scenario. In reinforcement learning, the machine learns through trial and error, receiving rewards for correct actions and penalties for incorrect actions.
Henri is using reinforcement learning
In reinforcement learning, an AI system learns by interacting with an environment and receiving feedback (rewards or penalties) based on its actions. In Henri’s case, the AI system plays the game of Go, and its performance (winning or losing) serves as feedback. Over time, the system adapts its strategy to maximize rewards (wins) and minimize penalties (losses), leading to gradual improvement. Reinforcement learning is commonly used for training agents in games, robotics, and other dynamic environments.
The scenario described in the question, where Henri is training an AI system to play the game of Go, is an example of reinforcement learning. In reinforcement learning, an AI agent learns to make decisions and take actions in an environment to maximize a reward signal.
In this case, the AI system starts with the basic rules of Go and begins playing games. As it plays, it receives feedback in the form of rewards (winning) or punishments (losing). Based on this feedback, the AI system gradually improves its performance by adjusting its decision-making process. It learns from its experiences and adapts its strategy to maximize its chances of winning future games.
Reinforcement learning is different from other types of learning:
- Supervised learning: In supervised learning, the AI system is trained using labeled data, where the correct output is provided for each input. This is not the case in the Go game scenario, as the AI system is not given the correct moves to make.
- Unsupervised learning: Unsupervised learning involves finding patterns and structures in unlabeled data. In the Go game scenario, the AI system is not simply discovering patterns but actively learning to make decisions based on rewards and punishments.
Reinforcement learning is commonly used in training AI systems for game-playing, robotics, and other applications where an agent needs to learn to make decisions in an environment to achieve a goal. By iteratively playing the game and receiving feedback, the AI system can improve its performance over time, making reinforcement learning the most suitable approach for Henri’s training of the Go-playing AI system.
IBM Artificial Intelligence Fundamentals certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Artificial Intelligence Fundamentals graded quizzes and final assessments, earn IBM Artificial Intelligence Fundamentals digital credential and badge.