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What is 'exploration' in the context of reinforcement learning?
What is 'exploration' in the context of reinforcement learning?
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What is 'exploration' in the context of reinforcement learning?
Using known information to make decisions
Trying new actions to discover their effects
Evaluating the performance of the agent
Clustering similar actions
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Exploration involves trying new actions to discover their potential rewards, which is essential for learning in uncertain environments.
Questions & Step-by-step Solutions
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Q: What is 'exploration' in the context of reinforcement learning?
Solution:
Exploration involves trying new actions to discover their potential rewards, which is essential for learning in uncertain environments.
Steps: 6
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Step 1: Understand that reinforcement learning is a type of machine learning where an agent learns to make decisions.
Step 2: Know that the agent can take different actions in an environment.
Step 3: Realize that some actions may lead to rewards, while others may not.
Step 4: Learn that exploration means the agent tries new actions it hasn't taken before.
Step 5: Understand that by exploring, the agent can find out which actions give the best rewards.
Step 6: Remember that exploration is important because it helps the agent learn and improve its decision-making.
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