Generative AI Certificate Q&A: What type of learning when machine learning algorithms are coming up with interesting new ways to play the game?

Table of Contents

Question
Answer
Explanation
Reference

Question

You work for a company that produces video games. One of the challenges is creating non-player characters (NPCs) that are controlled by the game, but still make strategic decisions. Your team decides to use machine learning, and each time an NPC does better than a player it gets a small reward. Now the machine learning algorithms are coming up with interesting new ways to play the game. What type of learning is this?

A. self-supervised machine learning
B. reinforcement learning
C. unsupervised machine learning
D. generative AI

Answer

B. reinforcement learning

Explanation

The type of learning that is used in this scenario is B. Reinforcement learning.

Reinforcement learning is a branch of machine learning that enables machines to learn from their own actions and feedback. Reinforcement learning is inspired by the way humans and animals learn from trial and error, rewards and penalties, or goals and outcomes.

Reinforcement learning involves four main components, which are:

An agent, which is the machine or system that performs the actions and learns from the feedback.
An environment, which is the setting or context where the agent operates and interacts.
An action, which is the choice or decision that the agent makes at each step or state.
A reward, which is the feedback or outcome that the agent receives after each action.

Reinforcement learning works by:

Observing the state of the environment and choosing an action based on a policy or strategy.
Executing the action and receiving a reward or penalty from the environment.
Updating the policy or strategy based on the reward or penalty and the value function or estimation of future rewards.
Repeating the process until the agent reaches a terminal state or achieves a goal.

Reinforcement learning can perform various tasks that involve sequential decision making, exploration and exploitation, or optimization and control. Some examples are game playing, robotics, self-driving cars, or recommendation systems.

In this scenario, you work for a company that produces video games. One of the challenges is creating non-player characters (NPCs) that are controlled by the game, but still make strategic decisions. Your team decides to use machine learning, and each time an NPC does better than a player it gets a small reward. Now the machine learning algorithms are coming up with interesting new ways to play the game.

You are using reinforcement learning for your NPCs. This means that:

Your NPCs are the agents that perform actions and learn from feedback in the game environment.
Your game environment is the setting or context where your NPCs operate and interact with other players or elements.
Your NPC actions are the choices or decisions that your NPCs make at each step or state in the game, such as moving, attacking, defending, etc.
Your NPC rewards are the feedback or outcomes that your NPCs receive after each action in the game, such as winning, losing, scoring, etc.

Your NPCs learn by:

Observing the state of the game environment and choosing an action based on a policy or strategy that maximizes their rewards.
Executing the action and receiving a reward or penalty from the game environment based on their performance compared to other players.
Updating their policy or strategy based on their reward or penalty and their value function or estimation of future rewards.
Repeating the process until they reach a terminal state or achieve a goal in the game.

Therefore, you are using reinforcement learning for your NPCs.

Reference

Generative AI Exam Question and Answer

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