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5 cards from this section. Quick, good the night before a test.
Define what an 'agent' is in reinforcement learning.
Think about who is learning or making decisions in RL.
What does 'reward' mean in the context of reinforcement learning?
What do agents receive when they perform well?
What is an agent in reinforcement learning?
Think about the role of a student in a learning process.
True or False: In reinforcement learning, exploration is more important than exploitation.
Consider why you must try new things and also stick with successes.
Design a reinforcement learning strategy for a video game character to become better at winning matches. Consider the balance of exploration vs. exploitation.
Think of how players learn to adapt in both familiar and new scenarios.
Analyze the rewards system in a real-world application of reinforcement learning, such as personalized advertising. Discuss how rewards can shape user experience.
Consider how social media platforms use your interactions to tailor content.
7 more questions in this section. Enroll free to practise them all.
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