Antigravity Agent
181 lines
7.1 KiB
Markdown
181 lines
7.1 KiB
Markdown
---
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id: wiki-2026-0508-model-free-rl-vs-model-based-rl
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title: Model-Free RL vs Model-Based RL
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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: [MFRL vs MBRL, Model-Based Reinforcement Learning, World Model]
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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: [reinforcement-learning, machine-learning, planning, world-model]
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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: PyTorch/JAX
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---
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# Model-Free RL vs Model-Based RL
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## 매 한 줄
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> **"매 environment dynamics 의 learn 하나, 의 X 하나 — sample efficiency 의 vs simplicity 의 trade"**. Model-free (Q-learning, PPO) 매 reward signal 의 만으로 policy 의 update — simple 의 brittle. Model-based (Dreamer, MuZero) 매 world model 의 learn → 매 imagined rollout 의 train. 2026 의 Dreamer V3, EfficientZero, DayDreamer 의 robotics deployment — sample efficiency 의 1-2 orders.
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## 매 핵심
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### 매 dichotomy
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- **Model-free**: $\pi(a|s)$ 또는 $Q(s,a)$ 의 직접 learn. 매 transition $p(s'|s,a)$ 의 access 의 X.
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- **Model-based**: $\hat{p}(s'|s,a)$, $\hat{r}(s,a)$ 의 learn → 매 plan / imagined rollout / Dyna-style.
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### 매 trade-off table
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| Axis | Model-Free | Model-Based |
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|---|---|---|
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| Sample efficiency | Low | **High** (10-100×) |
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| Compute per update | Low | **High** |
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| Asymptotic perf | **Often higher** | Bounded by model error |
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| Stability | **Stable** | Compounding model error |
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| Transfer | Poor | **Better** (model 의 reuse) |
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| Implementation | **Simple** | Complex |
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### 매 modern flavors
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- **Model-free**: PPO, SAC, DQN family, TD3.
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- **Model-based**: Dreamer V3 (RSSM), MuZero (planning + value tree), TD-MPC2, PILCO (Gaussian process).
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- **Hybrid**: MBPO (model-generated rollouts → SAC), Dyna-Q.
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### 매 응용
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1. Robotics (sample-efficient sim-to-real).
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2. Atari/board game (MuZero).
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3. Drug design (sample-efficient exploration).
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4. Game NPC behavior (PPO 의 still default).
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## 💻 패턴
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### PPO — model-free policy gradient (gymnasium)
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```python
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import torch, torch.nn as nn, torch.nn.functional as F
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from torch.distributions import Categorical
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class ActorCritic(nn.Module):
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def __init__(self, obs_dim, n_act):
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super().__init__()
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self.shared = nn.Sequential(nn.Linear(obs_dim, 64), nn.Tanh(),
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nn.Linear(64, 64), nn.Tanh())
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self.pi = nn.Linear(64, n_act)
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self.v = nn.Linear(64, 1)
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def forward(self, x):
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h = self.shared(x)
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return Categorical(logits=self.pi(h)), self.v(h).squeeze(-1)
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def ppo_step(ac, opt, batch, clip=0.2, vf_c=0.5, ent_c=0.01):
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dist, v = ac(batch.obs)
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logp = dist.log_prob(batch.act)
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ratio = torch.exp(logp - batch.logp_old)
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surr1 = ratio * batch.adv
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surr2 = torch.clamp(ratio, 1-clip, 1+clip) * batch.adv
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pi_loss = -torch.min(surr1, surr2).mean()
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v_loss = F.mse_loss(v, batch.ret)
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ent = dist.entropy().mean()
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loss = pi_loss + vf_c * v_loss - ent_c * ent
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opt.zero_grad(); loss.backward(); opt.step()
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return loss.item()
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```
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### Dreamer-style world model (RSSM skeleton)
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```python
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class RSSM(nn.Module):
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def __init__(self, obs_dim, act_dim, h=200, z=32):
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super().__init__()
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self.gru = nn.GRUCell(z + act_dim, h)
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self.prior = nn.Linear(h, 2 * z) # μ, σ
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self.post = nn.Linear(h + obs_dim, 2 * z)
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self.dec_obs = nn.Linear(h + z, obs_dim)
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self.dec_rew = nn.Linear(h + z, 1)
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def step(self, h, z, a, obs=None):
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h = self.gru(torch.cat([z, a], -1), h)
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pri_mu, pri_log = self.prior(h).chunk(2, -1)
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if obs is not None:
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po_mu, po_log = self.post(torch.cat([h, obs], -1)).chunk(2, -1)
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z = po_mu + torch.exp(po_log) * torch.randn_like(po_mu)
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else:
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z = pri_mu + torch.exp(pri_log) * torch.randn_like(pri_mu)
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return h, z, (pri_mu, pri_log)
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def imagine(self, h, z, policy, T=15):
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states = []
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for _ in range(T):
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a = policy(torch.cat([h, z], -1))
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h, z, _ = self.step(h, z, a)
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states.append((h, z))
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return states
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```
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### Dyna-Q (hybrid — tabular)
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```python
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def dyna_q(env, n_planning=10, episodes=500, alpha=0.1, gamma=0.99, eps=0.1):
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Q = defaultdict(lambda: np.zeros(env.action_space.n))
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model = {} # (s,a) → (r, s')
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for _ in range(episodes):
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s, _ = env.reset()
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done = False
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while not done:
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a = np.random.randint(env.action_space.n) if np.random.random() < eps \
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else int(np.argmax(Q[s]))
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s2, r, done, *_ = env.step(a)
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Q[s][a] += alpha * (r + gamma * Q[s2].max() - Q[s][a])
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model[(s, a)] = (r, s2)
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for _ in range(n_planning): # 매 imagined step
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(sp, ap), (rp, sp2) = random.choice(list(model.items())), None
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rp, sp2 = model[(sp, ap)]
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Q[sp][ap] += alpha * (rp + gamma * Q[sp2].max() - Q[sp][ap])
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s = s2
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```
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### MuZero (planning sketch — value/policy net + MCTS)
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```python
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# 매 environment 의 black-box; learned (representation, dynamics, prediction) heads
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# search 매 imagined trajectory 의 over MCTS — replay 매 (search policy, search value, n-step return)
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# 의 train. (full impl 매 muzero_general repo)
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Lots of cheap simulation | **Model-free** (PPO/SAC) — simpler |
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| Real-robot, expensive samples | **Model-based** (Dreamer V3, TD-MPC2) |
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| Discrete board game | **MuZero** — planning 의 wins |
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| Continuous control benchmark | SAC or DreamerV3 |
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| Fast prototype | PPO — most stable, easiest to tune |
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| Long-horizon planning | Model-based + planning |
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**기본값**: prototype 매 PPO. Sample 매 expensive — Dreamer V3 / TD-MPC2.
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## 🔗 Graph
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- 부모: [[Reinforcement Learning]] · [[Markov Decision Process]]
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- 변형: [[PPO]] · [[SAC]] · [[Dreamer V3]] · [[MuZero]] · [[TD-MPC2]]
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- 응용: [[Sim-to-Real]] · [[Robotics RL]] · [[AlphaZero]]
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- Adjacent: [[World Model]] · [[Planning]] · [[Dyna-Q]]
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## 🤖 LLM 활용
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**언제**: trade-off explanation, algorithm choice, pseudocode skeleton.
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**언제 X**: 매 hyperparameter — paper-specific 의 cross-check (Dreamer V3 매 sensitivity 의 paper 의 careful).
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## ❌ 안티패턴
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- **MBRL 의 default 의 reach**: 매 cheap-sim 환경 의 PPO 의 win 매 simpler.
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- **Imagined rollout 의 too-long horizon**: 매 model error compounds — 5-15 step 의 typical.
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- **MFRL 의 sparse reward 의 hope**: 매 exploration 의 add (RND, ICM) — 또는 의 model-based 의 switch.
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- **MuZero 의 small problem 의 use**: 매 overkill — tabular Q 의 enough.
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- **Single-seed report**: 매 RL variance huge — 5+ seeds 의 IQM (Agarwal et al. 2021).
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## 🧪 검증 / 중복
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- Verified (Sutton & Barto 2nd ed. 2018; Hafner _DreamerV3_ 2023; Schrittwieser _MuZero_ Nature 2020).
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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 — MFRL/MBRL trade-off + DreamerV3/MuZero 정리 |
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