Practice Exploration Strategies: ε-greedy, Softmax - 9.4.4 | 9. Reinforcement Learning and Bandits | Advance Machine Learning
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9.4.4 - Exploration Strategies: ε-greedy, Softmax

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does ε represent in the ε-greedy strategy?

💡 Hint: Think about how often the agent randomizes its action.

Question 2

Easy

What is the main advantage of the ε-greedy strategy?

💡 Hint: Consider the implications of trying new options.

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 ε in ε-greedy represent?

  • A fixed action
  • Probability of exploration
  • An exploitative strategy

💡 Hint: What percentage is usually used for exploration?

Question 2

True or False: The softmax strategy always chooses the highest expected reward action.

  • True
  • False

💡 Hint: Is it a fixed choice every time?

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

In a scenario with three arms of a bandit, with expected rewards of [1, 2, 4] and ε = 0.2, calculate the probability of selecting each action using ε-greedy.

💡 Hint: Divide ε properly among the arms.

Question 2

Given the following expected rewards for four actions: [3, 4, 7, 8], compute their softmax probabilities with τ=1.

💡 Hint: Be careful with your calculations while applying softmax.

Challenge and get performance evaluation