Practice - Exploration Strategies: ε-greedy, Softmax
Practice Questions
Test your understanding with targeted questions
What does ε represent in the ε-greedy strategy?
💡 Hint: Think about how often the agent randomizes its action.
What is the main advantage of the ε-greedy strategy?
💡 Hint: Consider the implications of trying new options.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What does the ε in ε-greedy represent?
💡 Hint: What percentage is usually used for exploration?
True or False: The softmax strategy always chooses the highest expected reward action.
💡 Hint: Is it a fixed choice every time?
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Challenge Problems
Push your limits with advanced challenges
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.
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.
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Reference links
Supplementary resources to enhance your learning experience.