Practice Agent Interacts With Environment (1.2) - Reinforcement Learning and Decision Making

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9 questions

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6 sample questions

Question 1.
Define what an agent is in Reinforcement Learning.
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Think about who is making decisions in this context.
Question 2.
What does the environment refer to in this section?
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Consider the surroundings of the agent.
3Question 3.
What does the term 'agent' refer to in Reinforcement Learning?
- The learner
- The environment
- The reward function
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Think about who is acting in the learning process.
4Question 4.
The current situation of the agent is referred to as the...
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It's what the agent perceives before taking an action.
Question 5.
Design a simple RL algorithm to demonstrate how an agent might learn to prefer one route over others in a traffic simulation.
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Think about how to represent states and calculate rewards!
Question 6.
Analyze how sparse rewards can affect an agent's learning path in a maze-solving scenario.
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Consider the implications of not having rewards at every step.

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