2nd/10_Wiki/Topics/General Knowledge/Model-Free RL vs Model-Based RL.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-model-free-rl-vs-model-based-rl	Model-Free RL vs Model-Based RL	10_Wiki/Topics	verified	self	MFRL vs MBRL Model-Based Reinforcement Learning World Model	none	A	0.9	applied	reinforcement-learning machine-learning planning world-model		2026-05-10	pending	language framework Python PyTorch/JAX

Model-Free RL vs Model-Based RL

매 한 줄

"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.

매 핵심

매 dichotomy

• Model-free: \pi(a|s) 또는 Q(s,a) 의 직접 learn. 매 transition p(s'|s,a) 의 access 의 X.
• Model-based: \hat{p}(s'|s,a), \hat{r}(s,a) 의 learn → 매 plan / imagined rollout / Dyna-style.

매 trade-off table

Axis	Model-Free	Model-Based
Sample efficiency	Low	High (10-100×)
Compute per update	Low	High
Asymptotic perf	Often higher	Bounded by model error
Stability	Stable	Compounding model error
Transfer	Poor	Better (model 의 reuse)
Implementation	Simple	Complex

매 modern flavors

• Model-free: PPO, SAC, DQN family, TD3.
• Model-based: Dreamer V3 (RSSM), MuZero (planning + value tree), TD-MPC2, PILCO (Gaussian process).
• Hybrid: MBPO (model-generated rollouts → SAC), Dyna-Q.

매 응용

1. Robotics (sample-efficient sim-to-real).
2. Atari/board game (MuZero).
3. Drug design (sample-efficient exploration).
4. Game NPC behavior (PPO 의 still default).

💻 패턴

PPO — model-free policy gradient (gymnasium)

import torch, torch.nn as nn, torch.nn.functional as F
from torch.distributions import Categorical

class ActorCritic(nn.Module):
    def __init__(self, obs_dim, n_act):
        super().__init__()
        self.shared = nn.Sequential(nn.Linear(obs_dim, 64), nn.Tanh(),
                                    nn.Linear(64, 64), nn.Tanh())
        self.pi = nn.Linear(64, n_act)
        self.v  = nn.Linear(64, 1)
    def forward(self, x):
        h = self.shared(x)
        return Categorical(logits=self.pi(h)), self.v(h).squeeze(-1)

def ppo_step(ac, opt, batch, clip=0.2, vf_c=0.5, ent_c=0.01):
    dist, v = ac(batch.obs)
    logp = dist.log_prob(batch.act)
    ratio = torch.exp(logp - batch.logp_old)
    surr1 = ratio * batch.adv
    surr2 = torch.clamp(ratio, 1-clip, 1+clip) * batch.adv
    pi_loss = -torch.min(surr1, surr2).mean()
    v_loss = F.mse_loss(v, batch.ret)
    ent = dist.entropy().mean()
    loss = pi_loss + vf_c * v_loss - ent_c * ent
    opt.zero_grad(); loss.backward(); opt.step()
    return loss.item()

Dreamer-style world model (RSSM skeleton)

class RSSM(nn.Module):
    def __init__(self, obs_dim, act_dim, h=200, z=32):
        super().__init__()
        self.gru = nn.GRUCell(z + act_dim, h)
        self.prior = nn.Linear(h, 2 * z)         # μ, σ
        self.post  = nn.Linear(h + obs_dim, 2 * z)
        self.dec_obs = nn.Linear(h + z, obs_dim)
        self.dec_rew = nn.Linear(h + z, 1)

    def step(self, h, z, a, obs=None):
        h = self.gru(torch.cat([z, a], -1), h)
        pri_mu, pri_log = self.prior(h).chunk(2, -1)
        if obs is not None:
            po_mu, po_log = self.post(torch.cat([h, obs], -1)).chunk(2, -1)
            z = po_mu + torch.exp(po_log) * torch.randn_like(po_mu)
        else:
            z = pri_mu + torch.exp(pri_log) * torch.randn_like(pri_mu)
        return h, z, (pri_mu, pri_log)

    def imagine(self, h, z, policy, T=15):
        states = []
        for _ in range(T):
            a = policy(torch.cat([h, z], -1))
            h, z, _ = self.step(h, z, a)
            states.append((h, z))
        return states

Dyna-Q (hybrid — tabular)

def dyna_q(env, n_planning=10, episodes=500, alpha=0.1, gamma=0.99, eps=0.1):
    Q = defaultdict(lambda: np.zeros(env.action_space.n))
    model = {}                                          # (s,a) → (r, s')
    for _ in range(episodes):
        s, _ = env.reset()
        done = False
        while not done:
            a = np.random.randint(env.action_space.n) if np.random.random() < eps \
                else int(np.argmax(Q[s]))
            s2, r, done, *_ = env.step(a)
            Q[s][a] += alpha * (r + gamma * Q[s2].max() - Q[s][a])
            model[(s, a)] = (r, s2)
            for _ in range(n_planning):                 # 매 imagined step
                (sp, ap), (rp, sp2) = random.choice(list(model.items())), None
                rp, sp2 = model[(sp, ap)]
                Q[sp][ap] += alpha * (rp + gamma * Q[sp2].max() - Q[sp][ap])
            s = s2

MuZero (planning sketch — value/policy net + MCTS)

# 매 environment 의 black-box; learned (representation, dynamics, prediction) heads
# search 매 imagined trajectory 의 over MCTS — replay 매 (search policy, search value, n-step return)
# 의 train. (full impl 매 muzero_general repo)

매 결정 기준

상황	Approach
Lots of cheap simulation	Model-free (PPO/SAC) — simpler
Real-robot, expensive samples	Model-based (Dreamer V3, TD-MPC2)
Discrete board game	MuZero — planning 의 wins
Continuous control benchmark	SAC or DreamerV3
Fast prototype	PPO — most stable, easiest to tune
Long-horizon planning	Model-based + planning

기본값: prototype 매 PPO. Sample 매 expensive — Dreamer V3 / TD-MPC2.

🔗 Graph

• 부모: Reinforcement Learning
• 변형: PPO
• Adjacent: World Model · Planning

🤖 LLM 활용

언제: trade-off explanation, algorithm choice, pseudocode skeleton. 언제 X: 매 hyperparameter — paper-specific 의 cross-check (Dreamer V3 매 sensitivity 의 paper 의 careful).

❌ 안티패턴

• MBRL 의 default 의 reach: 매 cheap-sim 환경 의 PPO 의 win 매 simpler.
• Imagined rollout 의 too-long horizon: 매 model error compounds — 5-15 step 의 typical.
• MFRL 의 sparse reward 의 hope: 매 exploration 의 add (RND, ICM) — 또는 의 model-based 의 switch.
• MuZero 의 small problem 의 use: 매 overkill — tabular Q 의 enough.
• Single-seed report: 매 RL variance huge — 5+ seeds 의 IQM (Agarwal et al. 2021).

🧪 검증 / 중복

• Verified (Sutton & Barto 2nd ed. 2018; Hafner DreamerV3 2023; Schrittwieser MuZero Nature 2020).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — MFRL/MBRL trade-off + DreamerV3/MuZero 정리