pt-drl

21. How does MAML facilitate fast adaptation in reinforcement learning tasks?

1

שאלה #171137

MAML optimizes model parameters to require minimal updates when exposed to new tasks, allowing models to generalize across tasks efficiently with few-shot learning.

מיין לפי

22. Provide an example where curiosity-driven exploration outperforms epsilon-greedy.

1

In sparse reward environments like Montezuma’s Revenge, curiosity-driven exploration encourages discovering novel states by assigning intrinsic rewards to unpredictable outcomes, while epsilon-greedy struggles due to the lack of immediate feedback.

מיין לפי

Q: What is Reinforcement Learning?

1

שאלה #171139

RL is a machine learning paradigm where an agent learns by interacting with an environment, receiving rewards to maximize cumulative reward.

מיין לפי

What are the key components of an RL system?

1

שאלה #171140

		• Agent – The decision-maker
		• Environment – The external system the agent interacts with.
		• State (s) – The agent's current situation.
		• Action (a) – The decision the agent makes.
		• Reward (r) – Feedback from the environment.
		• Policy (π) – Defines the agent’s behavior.
		• Value Function (V, Q) – Estimates the expected return from states/actions.

מיין לפי

Q: What are the types of RL?

1

שאלה #171141

		Model-free RL (e.g., Q-learning, Policy Gradient) – No knowledge of environment dynamics.
		Model-based RL – Uses a learned model of the environment for planning.
		read

מיין לפי

Q: What is the difference between value-based and policy-based methods?

1

שאלה #171142

		• Value-based: Learns Q-values (e.g., Q-learning, DQN).
		• Policy-based: Directly learns the policy (e.g., REINFORCE, PPO).
		read

מיין לפי

Q: What are the limitations of Q-learning, and how does DQN improve it?

1

שאלה #171143

		Struggles with high-dimensional state spaces.
		Overestimates Q-values.

מיין לפי

what are DQN improvements?

1

שאלה #171144

		Uses a deep neural network for function approximation.
		Experience replay to break correlations in data.
		Target network to stabilize learning.

מיין לפי

What are policy gradient methods?

1

שאלה #171145

		Instead of learning value functions, they optimize the policy directly.
		Examples: REINFORCE, PPO, TRPO.
		Work well for continuous action spaces.
		read

מיין לפי

What is Imitation Learning?

1

שאלה #171146

Learning a policy by mimicking expert demonstrations instead of using reward signals.

מיין לפי

Discuss, Learn and be Happy דיון בשאלות

21. How does MAML facilitate fast adaptation in reinforcement learning tasks?

שאלה #171137

22. Provide an example where curiosity-driven exploration outperforms epsilon-greedy.

שאלה #171138

Q: What is Reinforcement Learning?

שאלה #171139

What are the key components of an RL system?

שאלה #171140

Q: What are the types of RL?

שאלה #171141

Q: What is the difference between value-based and policy-based methods?

שאלה #171142

Q: What are the limitations of Q-learning, and how does DQN improve it?

שאלה #171143

what are DQN improvements?

שאלה #171144

What are policy gradient methods?

שאלה #171145

What is Imitation Learning?

שאלה #171146

pt-drl

Discuss, Learn and be Happy דיון בשאלות

21. How does MAML facilitate fast adaptation in reinforcement learning tasks?

22. Provide an example where curiosity-driven exploration outperforms epsilon-greedy.

Q: What is Reinforcement Learning?

What are the key components of an RL system?

Q: What are the types of RL?

Q: What is the difference between value-based and policy-based methods?

Q: What are the limitations of Q-learning, and how does DQN improve it?

what are DQN improvements?

What are policy gradient methods?

What is Imitation Learning?

בקשה לשינוי התשובה / תיקון שאלה gavel

בקשה למחיקת השאלהmood_bad

בקשה לשינוי התשובה / תיקון שאלה

בקשה למחיקת השאלה