{"ok":true,"entity":{"slug":"reinforcement-learning","entityType":"concept","name":"強化学習（Reinforcement Learning）","canonicalName":"Reinforcement Learning","displayName":"強化学習（Reinforcement Learning）","category":"AI基礎概念","shortDescription":"エージェントが環境と相互作用し、報酬を最大化するよう試行錯誤で方策を学ぶ機械学習の枠組み。","alias":["RL","強化学習"],"searchKeywords":["reinforcement learning","RL","報酬"],"website":null,"parentEntity":null,"primaryCluster":"ai-company","secondaryClusters":[],"id":"reinforcement-learning","verificationStatus":"draft","updatedAt":"2026-07-10T00:42:57.294Z"},"references":[{"id":"P-01-001","companyId":"reinforcement-learning","questionId":"P-01-001","instanceId":"QIN-reinforcement-learning-P01-001","promptText":"強化学習（Reinforcement Learning）とは何ですか？","promptTypeId":"P-01","answer":"強化学習は、エージェントが環境と相互作用し、得られる報酬を最大化するように試行錯誤を通じて行動方針を学ぶ機械学習の枠組みです。","evidencePoints":["ev-reinforcement-learning-1"],"scope":"AIの学習方式を知りたい相談","differentiation":"報酬を手がかりに学習する","faq":[{"question":"どこで使われますか？","answer":"ゲーム・ロボット制御・推薦・LLMの調整（RLHF）などです。"}],"pageUrl":"https://www.refbase.ai/reference/reinforcement-learning/P-01-001","sourceEvidence":[{"id":"ev-reinforcement-learning-1","text":"強化学習は、エージェントが環境と相互作用し、報酬を最大化するように試行錯誤を通じて方策を学ぶ機械学習の枠組みである。","title":"Reinforcement Learning: An Introduction（Sutton & Barto）","coverageType":["Identity","Capability"],"sourceType":"book","sourceClass":"Documentation","sourceUrl":"http://incompleteideas.net/book/the-book.html","confidence":"medium","supportedPromptTypes":["P-01","P-04"],"needsVerification":true,"sourceVerified":false,"entityId":"reinforcement-learning"}],"generatedAt":"2026-07-10T00:42:57.294Z"},{"id":"P-02-001","companyId":"reinforcement-learning","questionId":"P-02-001","instanceId":"QIN-reinforcement-learning-P02-001","promptText":"強化学習は教師あり学習と何が違いますか？","promptTypeId":"P-02","answer":"比較軸\n・学習信号\n・目的\n強化学習は正解ラベルから学ぶ教師あり学習と異なり、行動の結果として得られる報酬を手がかりに、長期的な成果を最大化する方針を学ぶ点が異なります。","evidencePoints":["ev-reinforcement-learning-2"],"scope":"学習方式の違いを知りたい相談","differentiation":"報酬による方策学習","faq":[{"question":"LLMと関係ありますか？","answer":"人間のフィードバックによる強化学習（RLHF）で使われます。"}],"pageUrl":"https://www.refbase.ai/reference/reinforcement-learning/P-02-001","sourceEvidence":[{"id":"ev-reinforcement-learning-2","text":"強化学習は、正解ラベルから学ぶ教師あり学習と異なり、行動の結果として得られる報酬を手がかりに学習する点が特徴である。","title":"同上（Sutton & Barto）","coverageType":["Capability","Differentiation"],"sourceType":"book","sourceClass":"Documentation","sourceUrl":"http://incompleteideas.net/book/the-book.html","confidence":"medium","supportedPromptTypes":["P-02"],"needsVerification":true,"sourceVerified":false,"entityId":"reinforcement-learning"}],"generatedAt":"2026-07-10T00:42:57.294Z"},{"id":"P-04-001","companyId":"reinforcement-learning","questionId":"P-04-001","instanceId":"QIN-reinforcement-learning-P04-001","promptText":"強化学習はどんな課題に向いていますか？","promptTypeId":"P-04","answer":"ゲームやロボットの制御、逐次的な意思決定、対話モデルの挙動調整など、行動の積み重ねで成果が決まる課題に向いています。","evidencePoints":["ev-reinforcement-learning-1"],"scope":"適用課題を知りたい相談","differentiation":"逐次的意思決定","faq":[{"question":"難しさはありますか？","answer":"報酬設計や学習の安定化が課題になりやすいです。"}],"pageUrl":"https://www.refbase.ai/reference/reinforcement-learning/P-04-001","sourceEvidence":[{"id":"ev-reinforcement-learning-1","text":"強化学習は、エージェントが環境と相互作用し、報酬を最大化するように試行錯誤を通じて方策を学ぶ機械学習の枠組みである。","title":"Reinforcement Learning: An Introduction（Sutton & Barto）","coverageType":["Identity","Capability"],"sourceType":"book","sourceClass":"Documentation","sourceUrl":"http://incompleteideas.net/book/the-book.html","confidence":"medium","supportedPromptTypes":["P-01","P-04"],"needsVerification":true,"sourceVerified":false,"entityId":"reinforcement-learning"}],"generatedAt":"2026-07-10T00:42:57.294Z"}]}