Quiz Space

Reinforcement Learning · Quiz 2 · 4 Aug 2024 · May 2024 term

Question 16: In reinforcement learning, what distinguishes the semi-g…

Question 16

+3 marksOne correct option

In reinforcement learning, what distinguishes the semi-gradient method from the full gradient method?

  1. A

    The semi-gradient method uses only a subset of the available data to compute parameter updates, while the full gradient method uses all available data.

  2. B

    The semi-gradient method updates the parameters of the value function based on a mixture of TD error and the gradient of the value function, while the full gradient method updates parameters using only the gradient.

  3. C

    The semi-gradient method is more computationally efficient than the full gradient method, requiring fewer iterations to converge to the optimal solution.

  4. D

    The semi-gradient method guarantees convergence to the optimal policy for any choice of learning rate, while the full gradient method may diverge with certain learning rates.

Show answer

Correct answer

  • B

    The semi-gradient method updates the parameters of the value function based on a mixture of TD error and the gradient of the value function, while the full gradient method updates parameters using only the gradient.

See the whole paperTake it as a mock test

Question 16 of 16 in the IIT Madras BS Reinforcement Learning (Reinforcement Learning) Quiz 2 paper sat on 4 Aug 2024, in the May 2024 term (IIT M DEGREE AN EXAM QDB2 4 Aug 2024). It carries 3 marks.

More questions from this paper

  1. Q1Based on the above data, answer the given subquestions.
  2. Q2Based on the above data, answer the given subquestions.
  3. Q3Based on the above data, answer the given subquestions.
  4. Q4Based on the above data, answer the given subquestions.
  5. Q5Figure question
  6. Q6Figure question
  7. Q7Figure question
  8. Q8Figure question
  9. Q9What is a key advantage of using n-step TD prediction over one-step TD prediction?
  10. Q10Consider a reinforcement learning agent navigating a complex environment with sparse rewards. Which of the following st…
  11. Q11What is a key advantage of using a target network in the Deep Q-Network (DQN) algorithm?
  12. Q12In the context of Deep Q-Networks (DQN), how does experience replay contribute to improving learning efficiency?
  13. Q13How does the choice of function approximator impact the performance of policy gradient methods?
  14. Q14How does the inclusion of a baseline in the REINFORCE algorithm impact its learning dynamics and performance?
  15. Q15In Dueling DQN, what is the primary advantage of decoupling the value function into state values and advantage values?