You work as a machine learning specialist for a robotics product manufacturer. Your company is trying to use machine learning to help its automatic vacuuming robot determine the most efficient path across the floor of a room. You need to build a machine learning model to accomplish this problem.
Which modeling approach best fits your problem?

Answer options:

A.Multi-Class Classification
B.Simulation-based Reinforcement Learning
C.Binary Classification
D.Heuristic Approach

Answer: B
Option A is incorrect. Multi-Class Classification is used when your model needs to has many class outcomes from which to choose, as in a car model classification image recognition problem. In this strategy determination problem, we need to learn a strategy that optimizes an objective. A Multi-Class Classification approach wouldn’t give you this result.
Option B is correct. Simulation-Based Reinforcement Learning is used in problems where your model needs to learn through trial and error. This is how a robot would best learn the optimal path through a given environment.
Option C is incorrect. Binary Classification is the approach you use when you are trying to predict a binary outcome. This strategy determination problem would not fit a binary classification model.
Option D is incorrect. The Heuristic Approach is used when a machine learning approach is not necessary. An example is the rate of acceleration of a particle through space. There are well known formulas for speed, inertia, and friction that can solve a problem such as this.
Reference:
Please see the Amazon SageMaker developer guide titled Linear Learner Algorithm, the Amazon SageMaker developer guide titled Reinforcement Learningwith Amazon SageMaker RL,the Amazon Machine Learning developer guide titled Multiclass Classification, and the article titled What is the difference between a machine learning algorithm and a heuristic, and when to use each?