Virtual Labs

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Aim

Theory

Procedure

Pretest

Demo

Practice

Posttest

References

Contributors

Feedback

Policy Iteration

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Beginner

Intermediate

Advanced

What does MDP stand for?

a: Markov Decision Process Explanation

Explanation

b: Machine Deterministic Process Explanation

Explanation

c: Magnitude Difference Policy Explanation

Explanation

d: Markov Dynamic Position Explanation

Explanation

What is the goal of policy iteration?

a: To find the optimal policy in a Markov Decision Process Explanation

Explanation

b: To estimate the value function for each state Explanation

Explanation

c: To determine the transition probabilities in a Markov Decision Process Explanation

Explanation

d: To calculate the expected rewards in a Markov Decision Process Explanation

Explanation

What are the two main steps of policy iteration?

a: Policy evaluation and policy improvement Explanation

Explanation

b: State estimation and reward maximization Explanation

Explanation

c: Action selection and state transition Explanation

Explanation

d: Exploration and exploitation Explanation

Explanation

What is the purpose of policy evaluation in policy iteration?

a: To determine the value function for a given policy Explanation

Explanation

b: To update the transition probabilities in a Markov Decision Process Explanation

Explanation

c: To find the optimal policy Explanation

Explanation

d: To select the best action in a given state Explanation

Explanation

What is policy improvement in policy iteration?

a: Modifying the policy to select better actions in each state Explanation

Explanation

b: Updating the value function for each state Explanation

Explanation

c: Adjusting the transition probabilities in a Markov Decision Process Explanation

Explanation

d: Evaluating the expected rewards for each action Explanation

Explanation

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