An agent acts in an environment and receives rewards.
Improve its policy through trial and error.
Choose two correct statements.
Reinforcement learning learns a policy from states, actions, and rewards and must balance exploration with exploitation and reward design.
Detailed explanation
Outcomes are evaluated with a reward signal.
Outcomes are evaluated with a reward signal.
The agent must trade off learning and exploitation.
The agent must trade off learning and exploitation.
Interaction and reward are central.
Interaction and reward are central.
A poor reward can encourage unwanted actions.
A poor reward can encourage unwanted actions.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 1.3の強化学習、報酬、機械学習方法を確認する。Expected result
強化学習を教師あり学習と区別し、報酬設計の影響を説明できる。Key points
- State
- Action
- 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.