1.1Basic
An agent acts in an environment and receives feedback.
Map the learning elements.
Choose two descriptions.
× Incorrect
Reinforcement learning improves a policy through interaction, actions, and rewards, considering cumulative future reward.
Detailed explanation
CorrectRewards provide feedback toward desirable behavior and future returns
Cumulative reward matters.
IncorrectIt always ignores future effects
Future reward is part of the objective.
CorrectAn agent observes a state, chooses an action, and receives a reward
It improves through interaction.
IncorrectReinforcement learning is identical to labeled classification
It uses reward feedback instead.
Check official documentation
Compare the following points with the official documentation.
AWS公式AIF-C01 Domain 1.1の強化学習、エージェント、環境、報酬を確認する。
Official references
What to confirm
強化学習と教師あり学習の違い、報酬と方策の関係を説明できる。
Key points
- Agent
- Environment
- Reward
Notes
- Environment: AWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
- Compare the definition with the AIF-C01 objective and the service documentation before applying it.
- Keep the concept separate from a particular answer choice or implementation step.
Foundation review
Reinforcement learning
Learning behavior through interaction and feedback rather than fixed target labels.
Agent
The learner that observes conditions and chooses actions.
Question IDAWS-AIF-413
EnvironmentAWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
Last technical review2026-08-20
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