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
CorrectAn agent observes a state, chooses an action, and receives a reward
It improves through interaction.
It improves through interaction.
CorrectRewards provide feedback toward desirable behavior and future returns
Cumulative reward matters.
Cumulative reward matters.
IncorrectReinforcement learning is identical to labeled classification
It uses reward feedback instead.
It uses reward feedback instead.
IncorrectIt always ignores future effects
Future reward is part of the objective.
Future reward is part of the objective.
Try it yourself
An example you can run in a temporary verification environment.
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。Expected result
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
- Command output formatting can vary slightly by distribution or tool version.
- Run the example in a temporary directory or process when possible.
Foundation review
Read the scope first
Check whether the command acts on the current shell, a new process, an existing process, or a file.
Verify the observable result
Use the supplied command and compare the output with the expected result.
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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