Practice Markov Decision Processes (MDPs) - 9.2 | 9. Reinforcement Learning and Bandits | Advance Machine Learning
K12 Students

Academics

AI-Powered learning for Grades 8–12, aligned with major Indian and international curricula.

Academics
Professionals

Professional Courses

Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.

Professional Courses
Games

Interactive Games

Fun, engaging games to boost memory, math fluency, typing speed, and English skillsβ€”perfect for learners of all ages.

games

9.2 - Markov Decision Processes (MDPs)

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What are the five key components of an MDP?

πŸ’‘ Hint: Think of what makes up the framework of decision-making.

Question 2

Easy

What does the discount factor (Ξ³) do?

πŸ’‘ Hint: Consider how future rewards are valued compared to immediate rewards.

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 an MDP primarily model?

  • Supervised Learning
  • Random Processes
  • Decision-Making

πŸ’‘ Hint: Think of how agents make choices under risk.

Question 2

True or False: The discount factor in an MDP can be greater than 1.

  • True
  • False

πŸ’‘ Hint: Recall how future rewards are treated.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Imagine an MDP for a shopping agent that can either buy an item or save money. Define the states, actions, transition probabilities, rewards, and discount factor.

πŸ’‘ Hint: Think about how the environment and choices can influence the agent's state and future decisions.

Question 2

Given an MDP for a maze navigation task, how would you adjust the discount factor if the agent is highly focused on immediate rewards?

πŸ’‘ Hint: Consider how the agent views immediate vs. future rewards and the implications for its strategies.

Challenge and get performance evaluation