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