Virtual Labs

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Aim

Theory

Procedure

Pretest

Demo

Practice

Posttest

References

Contributors

Feedback

Q-Learning

Choose difficulty:

Beginner

Intermediate

Advanced

What is Q-learning?

a: A type of reinforcement learning algorithm Explanation

Explanation

b: A type of supervised learning algorithm Explanation

Explanation

c: A type of unsupervised learning algorithm Explanation

Explanation

d: A type of deep learning algorithm Explanation

Explanation

What is the purpose of the Q-value in Q-learning

a: To measure the uncertainty of the agent's belief about the state of the environment Explanation

Explanation

b: To estimate the expected reward of taking a particular action in a particular state Explanation

Explanation

c: To represent the probability of observing a particular state given a set of actions Explanation

Explanation

What is the purpose of the epsilon-greedy policy in Q-learning?

a: To converge the Q values quickly Explanation

Explanation

b: To ensure that the agent always takes the action with the highest Q-value Explanation

Explanation

c: To avoid getting stuck in local optima in the Q-value function Explanation

Explanation

d: To balance exploration and exploitation of the state-action space Explanation

Explanation

Is Q-Learning an off policy method?

a: Yes Explanation

Explanation

b: No Explanation

Explanation

What is the ideal value of the discount factor in Q-learning to prioritize long-term rewards?

a: A high value, such as 0.99 Explanation

Explanation

b: A low value, such as 0.1 Explanation

Explanation

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