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Machine Learning Deep Reinforcement Learning

This quiz is designed to assess your understanding of Machine Learning Deep Reinforcement Learning.

15 Questions Published
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Questions

Question 1 Multiple Choice (Single Answer)

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
Question 2 Multiple Choice (Single Answer)

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
Question 3 Multiple Choice (Single Answer)

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

  1. Environment
  2. Agent
  3. Reward Function
  4. Loss Function
Question 4 Multiple Choice (Single Answer)

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.
Question 5 Multiple Choice (Single Answer)

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
Question 6 Multiple Choice (Single Answer)

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
Question 7 Multiple Choice (Single Answer)

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
Question 8 Multiple Choice (Single Answer)

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
Question 9 Multiple Choice (Single Answer)

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
Question 10 Multiple Choice (Single Answer)

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)
Question 11 Multiple Choice (Single Answer)

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

  1. TensorFlow
  2. PyTorch
  3. Keras
  4. scikit-learn
Question 12 Multiple Choice (Single Answer)

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
Question 13 Multiple Choice (Single Answer)

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
Question 14 Multiple Choice (Single Answer)

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
Question 15 Multiple Choice (Single Answer)

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
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