Predictive Analytics with TensorFlow 11.2: Developing a Multiarmed Bandit's Predictive Model

Predictive Analytics with TensorFlow 11.2: Developing a Multiarmed Bandit's Predictive Model

Assessment

Interactive Video

Information Technology (IT), Architecture

University

Practice Problem

Hard

Created by

Wayground Content

FREE Resource

The video tutorial explores reinforcement learning, focusing on multi-armed bandits. It begins with a stateless agent approach, highlighting its limitations, and progresses to developing contextual bandits, which incorporate state information for better decision-making. The tutorial includes designing, training, and evaluating a contextual bandit agent using TensorFlow, demonstrating improved prediction accuracy and reward maximization.

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10 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the primary goal when dealing with multi-armed bandits in reinforcement learning?

To ensure all machines have the same payout probability

To formalize outputs on every state

To maximize the profit by choosing the best payout machine

To minimize the number of slot machines

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How are the initial weights set in a stateless bandit agent?

They are set to one

They are set to zero

They are set based on previous rewards

They are set randomly

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is a key limitation of a stateless bandit agent?

It cannot learn from environmental states

It can only handle one bandit at a time

It always chooses the same action

It requires a complex neural network

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the main advantage of using contextual bandits over stateless bandits?

They are easier to implement

They do not need any training

They can utilize environmental states for better decision-making

They require less computational power

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

In the development of contextual bandits, what is the role of the 'get bandit' function?

To generate a random number from a normal distribution

To initialize the bandit weights

To reset the training graph

To compute the loss function

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the purpose of the 'Contextual Bandit' class in the development process?

To initialize the training parameters

To define the neural network architecture

To list all possible bandits and their states

To compute the reward probabilities

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the primary objective during the training of a contextual bandit agent?

To maximize the number of actions

To ensure all predictions are incorrect

To minimize the number of bandits

To compute the mean reward for each bandit

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