Back
0

Machine Learning Deep Reinforcement Learning

Description: This quiz is designed to assess your understanding of Machine Learning Deep Reinforcement Learning.
Number of Questions: 15
Created by:
Tags: machine learning deep reinforcement learning neural networks artificial intelligence
Start the Quiz
Attempted 0/15 Correct 0 Score 0
‹
›

Which of the following is NOT a type of Deep Reinforcement Learning algorithm?

  1. Q-learning

  2. Policy Gradients

  3. Actor-Critic Methods

  4. Supervised Learning

Show answer
Correct Option: D
Explanation:

Supervised Learning is not a type of Deep Reinforcement Learning algorithm. It is a type of Machine Learning algorithm that learns from labeled data.

What is the goal of a Deep Reinforcement Learning agent?

  1. To maximize its reward

  2. To minimize its loss

  3. To learn the optimal policy

  4. To predict the future

Show answer
Correct Option: A
Explanation:

The goal of a Deep Reinforcement Learning agent is to maximize its reward. This is done by learning the optimal policy, which is the policy that results in the highest expected reward.

Which of the following is NOT a component of a Deep Reinforcement Learning system?

  1. Environment

  2. Agent

  3. Reward Function

  4. Loss Function

Show answer
Correct Option: D
Explanation:

A Loss Function is not a component of a Deep Reinforcement Learning system. It is a component of a Supervised Learning system.

What is the difference between Q-learning and Policy Gradients?

  1. Q-learning learns the optimal policy, while Policy Gradients learns the optimal value function.

  2. Q-learning is off-policy, while Policy Gradients is on-policy.

  3. Q-learning is model-based, while Policy Gradients is model-free.

  4. All of the above.

Show answer
Correct Option: D
Explanation:

Q-learning learns the optimal policy, while Policy Gradients learns the optimal value function. Q-learning is off-policy, while Policy Gradients is on-policy. Q-learning is model-based, while Policy Gradients is model-free.

Which of the following is NOT a Deep Reinforcement Learning application?

  1. Playing Atari games

  2. Training robots to walk

  3. Playing chess

  4. Predicting stock prices

Show answer
Correct Option: D
Explanation:

Predicting stock prices is not a Deep Reinforcement Learning application. It is a Supervised Learning application.

What is the main challenge in Deep Reinforcement Learning?

  1. The high dimensionality of the state space

  2. The large number of actions

  3. The delayed reward

  4. All of the above

Show answer
Correct Option: D
Explanation:

The high dimensionality of the state space, the large number of actions, and the delayed reward are all challenges in Deep Reinforcement Learning.

Which of the following is NOT a technique for addressing the high dimensionality of the state space in Deep Reinforcement Learning?

  1. Function approximation

  2. Deep neural networks

  3. Dimensionality reduction

  4. Monte Carlo tree search

Show answer
Correct Option: D
Explanation:

Monte Carlo tree search is not a technique for addressing the high dimensionality of the state space in Deep Reinforcement Learning. It is a technique for searching for the optimal move in a game.

Which of the following is NOT a technique for addressing the large number of actions in Deep Reinforcement Learning?

  1. Action discretization

  2. Action grouping

  3. Hierarchical reinforcement learning

  4. Policy gradient methods

Show answer
Correct Option: D
Explanation:

Policy gradient methods are not a technique for addressing the large number of actions in Deep Reinforcement Learning. They are a technique for learning the optimal policy.

Which of the following is NOT a technique for addressing the delayed reward in Deep Reinforcement Learning?

  1. Temporal difference learning

  2. Q-learning

  3. SARSA

  4. Actor-critic methods

Show answer
Correct Option: D
Explanation:

Actor-critic methods are not a technique for addressing the delayed reward in Deep Reinforcement Learning. They are a technique for learning the optimal policy.

Which of the following is NOT a Deep Reinforcement Learning algorithm?

  1. Deep Q-learning

  2. Asynchronous Advantage Actor-Critic (A3C)

  3. Proximal Policy Optimization (PPO)

  4. Generative Adversarial Networks (GANs)

Show answer
Correct Option: D
Explanation:

Generative Adversarial Networks (GANs) are not a Deep Reinforcement Learning algorithm. They are a type of Generative Model.

Which of the following is NOT a Deep Reinforcement Learning toolkit?

  1. TensorFlow

  2. PyTorch

  3. Keras

  4. scikit-learn

Show answer
Correct Option: D
Explanation:

scikit-learn is not a Deep Reinforcement Learning toolkit. It is a general-purpose Machine Learning toolkit.

Which of the following is NOT a Deep Reinforcement Learning application?

  1. Playing Atari games

  2. Training robots to walk

  3. Playing chess

  4. Natural language processing

Show answer
Correct Option: D
Explanation:

Natural language processing is not a Deep Reinforcement Learning application. It is a Natural Language Processing application.

Which of the following is NOT a Deep Reinforcement Learning research area?

  1. Multi-agent reinforcement learning

  2. Continuous control

  3. Transfer learning

  4. Quantum reinforcement learning

Show answer
Correct Option: D
Explanation:

Quantum reinforcement learning is not a Deep Reinforcement Learning research area. It is a Quantum Computing research area.

Which of the following is NOT a Deep Reinforcement Learning challenge?

  1. The high dimensionality of the state space

  2. The large number of actions

  3. The delayed reward

  4. The need for large amounts of data

Show answer
Correct Option: D
Explanation:

The need for large amounts of data is not a Deep Reinforcement Learning challenge. It is a general Machine Learning challenge.

Which of the following is NOT a Deep Reinforcement Learning trend?

  1. The use of deep neural networks

  2. The use of off-policy learning

  3. The use of multi-agent reinforcement learning

  4. The use of supervised learning

Show answer
Correct Option: D
Explanation:

The use of supervised learning is not a Deep Reinforcement Learning trend. It is a general Machine Learning trend.

- Hide questions
ADVERTISEMENT

Find more quizzes from top tags

general knowledge
3615 Quizzes
119858 Questions
 technology
307 Quizzes
36705 Questions
 sports
963 Quizzes
19148 Questions
 history
1187 Quizzes
13475 Questions
 physics
598 Quizzes
13441 Questions
 biology
1160 Quizzes
12174 Questions
 science & technology
551 Quizzes
11015 Questions
 chemistry
490 Quizzes
9892 Questions
 maths
518 Quizzes
9324 Questions
 softskills
0 Quizzes
7131 Questions