GENERATIVE-AI-LEADER · Question #88
A company is developing an AI character for a video game. The AI character needs to learn how to navigate a complex environment and make decisions to achieve certain objectives within the game. When…
The correct answer is A. Reinforcement learning. Reinforcement learning (RL) is the correct paradigm here. In RL, an agent learns by interacting with an environment, receiving positive rewards for desirable actions and negative penalties for undesirable ones, and iteratively improves its policy through trial and error. The…
Question
A company is developing an AI character for a video game. The AI character needs to learn how to navigate a complex environment and make decisions to achieve certain objectives within the game. When the AI takes actions that lead to positive outcomes, like finding a reward or overcoming an obstacle, it receives a positive score. When it takes actions that lead to negative outcomes, like hitting a wall or losing progress, it receives a negative score. Through this process of trial and error, the AI gradually improves the character's ability to play the game effectively. What machine learning should the company use?
Options
- AReinforcement learning
- BUnsupervised learning
- CSupervised learning
- DDeep learning
How the community answered
(63 responses)- A92% (58)
- B2% (1)
- C2% (1)
- D5% (3)
Explanation
Reinforcement learning (RL) is the correct paradigm here. In RL, an agent learns by interacting with an environment, receiving positive rewards for desirable actions and negative penalties for undesirable ones, and iteratively improves its policy through trial and error. The scenario describes exactly this: the AI character earns positive scores for good outcomes and negative scores for bad ones. Supervised learning (C) requires labeled input-output pairs. Unsupervised learning (B) finds patterns in unlabeled data. Deep learning (D) is an architectural approach, not a training paradigm on its own.
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