Practice Markov Decision Process (MDP) - 2 | Reinforcement Learning and Decision Making | Artificial Intelligence Advance
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Knowledge

2 - Markov Decision Process (MDP)

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does MDP stand for?

💡 Hint: Think of a term used in decision-making frameworks.

Question 2

Easy

Name one component of an MDP.

💡 Hint: These components help define decision-making frameworks.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What does the discount factor (γ) indicate in an MDP?

  • Only the immediate rewards
  • Entirely future rewards
  • Importance of future rewards versus immediate rewards

💡 Hint: It's a number between 0 and 1.

Question 2

True or False: The states in an MDP represent only the current conditions of the agent at any moment.

  • True
  • False

💡 Hint: Think about different possible conditions.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Using the Bellman Equation, devise a strategy for a simple maze navigation task where states are intersections, actions are moving forward/backward/turning, and rewards are given for reaching the exit. Include probabilities based on the agent's movement accuracy.

💡 Hint: Consider if the agent has a 75% success rate - how does this affect P?

Question 2

Design an MDP for a delivery drone with states representing locations, actions concerning flight paths, and rewards that vary based on package delivery efficiency. How would you configure the transition probabilities?

💡 Hint: Think about real-life variables affecting the drone's flight.

Challenge and get performance evaluation