Reinforcement learning from action-linked rewards | AIGP
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Reinforcement learning from action-linked rewards: Which learning method characterizes the pilot?

AIGP Understanding the Foundations of AI Governance Easy

The pilot uses reinforcement learning because feedback follows the agent’s settlement actions.

The question

An insurer has a claims classifier trained on adjusters’ fraud labels. In a separate pilot, an agent selects settlement actions and receives numerical feedback after each action. Which learning method characterizes the pilot?

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  1. Unsupervised learning from claim clusters.
    Clustering discovers structure without using action-linked feedback, so it does not characterize this pilot.
  2. Supervised learning from adjuster labels.
    That describes the separate claims classifier, not the pilot in which feedback follows the agent’s actions.
  3. Self-supervised learning from settlement records.
    Self-supervised learning derives targets from input data; it does not specifically describe feedback received after agent actions.
  4. Reinforcement learning from action-linked rewards. ✓
    Reinforcement learning evaluates actions through feedback from the environment and can use that feedback to improve future behavior.
The trap
Feedback that evaluates an agent’s actions is the key signal for reinforcement learning.

How to remember it

The pilot uses reinforcement learning because feedback follows the agent’s settlement actions.

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