Antigravity Agent
34 lines
1.0 KiB
Markdown
34 lines
1.0 KiB
Markdown
---
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id: wiki-2026-0508-policy-gradient-methods
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title: Policy Gradient Methods
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category: 10_Wiki/Topics
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status: duplicate
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canonical_id: policy-optimization
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duplicate_of: "[[Policy-Optimization]]"
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aliases: []
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source_trust_level: A
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confidence_score: 0.9
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verification_status: redirected
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tags: [duplicate, reinforcement-learning, policy-gradient]
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last_reinforced: 2026-05-10
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github_commit: pending
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---
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# Policy Gradient Methods
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> **이 문서는 [[Policy-Optimization]] 의 중복본입니다.** Canonical 문서로 redirect.
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## 핵심 요약 (PG-specific aspects)
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- 매 policy gradient = ∇J = E[∇log π · A] — 매 foundational identity.
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- 매 REINFORCE → A2C → TRPO → PPO → GRPO → DPO 매 lineage 매 [[Policy-Optimization]] 에 정리.
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- 매 vanilla PG 매 high variance — 매 baseline + GAE 의 mitigate.
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## 🔗 Graph
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- 부모: [[Policy-Optimization]] (canonical)
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## 🕓 변경 이력
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | 중복 처리 — canonical 문서로 redirect |
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