refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조

에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게
[공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류.
문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인).

- Topic_Programming → Domain_Programming (내부 구조 보존)
- Topic_Graphic → Domain_Design
- Topic_Business → Domain_Product
- Topic_General → Domain_General
- _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning),
  Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing)
- 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서)
- 빈 폴더 정리 (memory/procedures)
- 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

10_Wiki/Topics/Domain_General/From_Other/Neurobiology-of-Reward.md

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+---
+id: wiki-2026-0508-neurobiology-of-reward
+title: Neurobiology of Reward
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [Reward System, Dopamine System, Mesolimbic Pathway]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [neuroscience, reward, dopamine, RL]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: python
+  framework: neuroscience-RL
+---
+
+# Neurobiology of Reward
+
+## 매 한 줄
+> **"매 dopamine 은 reward 자체 X, 매 reward prediction error 의 signal"**. 매 mesolimbic pathway (VTA → NAc) 가 매 expected vs actual outcome 의 차이를 encode 하며, 매 Schultz (1997) 가 매 발견. 매 modern RL (TD-learning, RLHF) 의 매 biological 의 root.
+
+## 매 핵심
+
+### 매 핵심 회로
+- **VTA (ventral tegmental area)**: 매 dopamine 의 source neurons.
+- **NAc (nucleus accumbens)**: 매 reward salience encoding.
+- **PFC (prefrontal cortex)**: 매 value-based decision-making.
+- **Amygdala**: 매 valence (positive/negative) encoding.
+
+### 매 RPE (Reward Prediction Error)
+- 매 RPE = actual_reward - expected_reward.
+- 매 positive RPE → dopamine burst → 매 reinforce action.
+- 매 negative RPE → dopamine dip → 매 weaken action.
+- 매 zero RPE (fully predicted reward) → no signal.
+
+### 매 응용
+1. **RL algorithms**: TD-learning 매 RPE 와 mathematically equivalent.
+2. **RLHF**: 매 reward model 매 human preference RPE 의 proxy.
+3. **Addiction research**: 매 hijacked dopamine → compulsive behavior.
+4. **UX design**: 매 variable reward schedule (slot machine effect).
+
+## 💻 패턴
+
+### TD-learning (Sutton & Barto, RL biological analog)
+```python
+# Temporal Difference learning — RPE 매 update signal
+import numpy as np
+
+def td_update(V, state, next_state, reward, alpha=0.1, gamma=0.99):
+    """V[s] ← V[s] + α(r + γV[s'] - V[s])"""
+    rpe = reward + gamma * V[next_state] - V[state]  # 매 RPE
+    V[state] += alpha * rpe
+    return V, rpe
+```
+
+### Dopamine neuron simulation
+```python
+def dopamine_response(predicted_r, actual_r, baseline=1.0):
+    """Schultz (1997) — 매 phasic firing rate."""
+    rpe = actual_r - predicted_r
+    return baseline * np.exp(rpe)  # scale baseline firing
+```
+
+### RLHF reward model (modern bridge)
+```python
+# transformers + trl
+from trl import PPOTrainer, PPOConfig
+from transformers import AutoModelForCausalLMWithValueHead
+
+# 매 reward model = learned approximation of human RPE
+config = PPOConfig(model_name="meta-llama/Llama-3.1-8B")
+trainer = PPOTrainer(config, model, tokenizer, reward_model=reward_fn)
+# Reward signal drives policy update → analog of dopamine update
+```
+
+### Variable reward schedule (UX)
+```python
+import random
+def variable_reward(action_count):
+    """매 intermittent reinforcement — strongest learning."""
+    if random.random() < 0.3:  # 30% reward
+        return "reward"
+    return "no_reward"
+```
+
+### Aversive learning (negative valence)
+```python
+def negative_rpe_update(V, s, s_, r, alpha=0.1):
+    """매 amygdala-mediated learning."""
+    rpe = r + V[s_] - V[s]  # r typically negative
+    V[s] += alpha * rpe
+    return V
+```
+
+## 매 결정 기준
+| 질문 | 답 |
+|---|---|
+| 매 dopamine 매 pleasure 인가? | X — RPE signal (wanting ≠ liking) |
+| 매 RL 의 reward 매 dopamine? | Functional analog yes (Schultz) |
+| 매 addiction 매 dopamine 과잉? | X — dysregulated RPE / hijacked salience |
+| 매 RLHF 매 brain-like? | At reward-update level yes (policy update) |
+
+**기본값**: 매 dopamine = "wanting / RPE", 매 opioid = "liking" 의 dissociation 기억.
+
+## 🔗 Graph
+- 부모: [[Reinforcement-Learning]]
+- 응용: [[RLHF]] · [[TD-Learning]] · [[Addiction]]
+- Adjacent: [[Operant-Conditioning]] · [[Habit-Formation]]
+
+## 🤖 LLM 활용
+**언제**: 매 reward modeling intuition, 매 RLHF reward shaping debugging, 매 motivation framework explanation.
+**언제 X**: 매 clinical psychiatry — 매 specialist 영역.
+
+## ❌ 안티패턴
+- **Dopamine = pleasure**: 매 popular myth — 실제는 RPE / wanting.
+- **More dopamine = better**: 매 tonic 과잉 매 schizophrenia, parkinson off-state.
+- **Reward hacking**: 매 RL agent 매 RPE exploit, 매 brain analog (addiction).
+
+## 🧪 검증 / 중복
+- Verified (Schultz 1997 *Science*; Berridge & Robinson 1998 wanting/liking; Sutton & Barto *RL Book* 2018 2e).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — RPE biology + RL bridge + RLHF analog |