Practice Policies (10.2.2) - Reinforcement Learning - AI Course Fundamental

Practice: Policies

Lesson 10.2.2Reinforcement Learning

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9 questions

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3 cards from this section. Quick, good the night before a test.
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Quiz
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6 sample questions

Question 1.
Define what a policy is in reinforcement learning.
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Think about how decisions are made.
Question 2.
What is a deterministic policy?
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Remember: fixed actions for fixed states.
3Question 3.
What does a policy do in reinforcement learning?
- Maps states to actions
- Maximizes rewards
- Updates Q-values
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Consider the fundamental function of a policy.
4Question 4.
True or False: A deterministic policy will always choose the same action in a given state.
- True
- False
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Reflect on the meaning of 'deterministic'.
Question 5.
Consider a reinforcement learning scenario in a grid-based game. Design both deterministic and stochastic policies for moving the agent towards the goal while avoiding obstacles. Discuss the potential outcomes of each strategy.
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Analyze the strengths of both approaches in navigating the grid.
Question 6.
Discuss how the choice of policy type could change the training time and efficiency of an agent in a dynamic environment. Explore which factors could tip the scale toward choosing one policy over the other.
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Consider the impact of uncertainty and learning curves.

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