pt-drl

11. What is the main advantage of Model-Agnostic Meta-Learning (MAML)?

1

12. In meta-learning, what is one way to ensure fast adaptation?

1

13. Double DQNs help reduce the overestimation of Q-values.

1

14. The REINFORCE algorithm is an off-policy algorithm.

1

15. Thompson sampling is more sample efficient than epsilon-greedy exploration.

1

16. MCTS is only useful for deterministic environments.

1

17. Policy gradients are more effective than Q-learning in continuous action spaces.

1

18. How does prioritized experience replay improve DQN performance?

1

It assigns higher sampling probabilities to transitions with large temporal difference (TD) errors, ensuring that more informative experiences are used for training, which speeds up convergence and improves sample efficiency.

מיין לפי

19. One advantage and one limitation of transformers in DRL?

1

שאלה #171135

		Advantage: Transformers capture long-range dependencies in sequential data, improving decision-making in complex environments.
		Limitation: Transformers require large amounts of data and computation, making them challenging to train in reinforcement learning.

מיין לפי

20. Why is imitation learning useful when reward functions are difficult to define?

1

שאלה #171136

Imitation learning allows an agent to learn from expert demonstrations, bypassing the need for explicitly defined rewards, which is helpful in tasks where designing a reward function is complex (e.g., autonomous driving).

מיין לפי

		It fine-tunes models using few examples
		It completely eliminates the need for pre-training
		It uses a memory-based approach for fast adaptation
		It optimizes separate models for each task independently

		Use a fixed learning rate across all tasks
		Apply deep Q-learning instead of policy gradients
		Use only supervised learning techniques
		Train a model on a diverse set of tasks

Discuss, Learn and be Happy דיון בשאלות

11. What is the main advantage of Model-Agnostic Meta-Learning (MAML)?

שאלה #171127

12. In meta-learning, what is one way to ensure fast adaptation?

שאלה #171128

13. Double DQNs help reduce the overestimation of Q-values.

שאלה #171129

14. The REINFORCE algorithm is an off-policy algorithm.

שאלה #171130

15. Thompson sampling is more sample efficient than epsilon-greedy exploration.

שאלה #171131

16. MCTS is only useful for deterministic environments.

שאלה #171132

17. Policy gradients are more effective than Q-learning in continuous action spaces.

שאלה #171133

18. How does prioritized experience replay improve DQN performance?

שאלה #171134

19. One advantage and one limitation of transformers in DRL?

שאלה #171135

20. Why is imitation learning useful when reward functions are difficult to define?

שאלה #171136

		true
		false
		MCTS can handle stochastic environments using simulations.

pt-drl

Discuss, Learn and be Happy דיון בשאלות

11. What is the main advantage of Model-Agnostic Meta-Learning (MAML)?

12. In meta-learning, what is one way to ensure fast adaptation?

13. Double DQNs help reduce the overestimation of Q-values.

14. The REINFORCE algorithm is an off-policy algorithm.

15. Thompson sampling is more sample efficient than epsilon-greedy exploration.

16. MCTS is only useful for deterministic environments.

17. Policy gradients are more effective than Q-learning in continuous action spaces.

18. How does prioritized experience replay improve DQN performance?

19. One advantage and one limitation of transformers in DRL?

20. Why is imitation learning useful when reward functions are difficult to define?

בקשה לשינוי התשובה / תיקון שאלה gavel

בקשה למחיקת השאלהmood_bad

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