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Learn World Models(⚠️ Alpha Preview)

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git clone https://github.com/Powderbatpatch/learn-world-model-786.git
cd learn-world-model-786
npm install
npm start

Read Online GitHub Stars License: MIT

Learn world models by building them: from the intuition behind latent dynamics to a working simulation, planning, and evaluation system.

Caution

⚠️ Alpha Preview: This is an early build. Content is still being completed and revised: sections, examples, and wording may continue to change. Feedback via Issues is welcome.


✨ Preview

🏠 Course Home

Structured learning path with lecture and project cards.

Course home

📖 Lecture Pages

Concept-first explanations with mermaid diagrams and background callouts for deep-learning readers.

Lecture page

🗂️ Architecture Deep Dive

Seven architecture families, three planning mechanisms, side-by-side comparison tables.

Architecture lecture


What this course covers

Five lectures and five projects that take you from the intuition behind world models to a working three-model evaluation dashboard.

# Type Title Core Topics
L01 Lecture Internal Simulation & Historical Context Craik's mental models, predictive coding, four eras of world model evolution
L02 Lecture Observation Encoding & Latent Dynamics VAE, CNN encoder, ELBO, GRU → MDN-RNN → RSSM
L03 Lecture Architecture Patterns, Learning Paradigms & Planning Seven architecture families, CEM-MPC, latent Actor-Critic, TD-MPC
L04 Lecture Evaluation by World Model FID, reward correlation, consistency loss, PSNR, horizon drift
L05 Lecture Frontier Debates Language vs physical grounding, Bitter Lesson, AGI as a research target
P01 Project Train a VAE Encoder Small CNN VAE on 64×64 pixels; ELBO loss curve; latent slider visualization
P02 Project Build an RSSM Dynamics Model GRU, MDN-RNN, and RSSM compared; prior vs posterior rollout plots
P03 Project Train a Dreamer Agent Full training loop: encoder + RSSM + latent Actor-Critic on a small pixel env
P04 Project Swap the Dynamics Backbone Replace RSSM with a small causal Transformer (STORM-style); architecture comparison
P05 Project World Model Evaluation Dashboard Per-model metrics side by side: FID, reward correlation, PSNR, latent drift

Curriculum flow

flowchart TD
    L01["L01 History and Intuition"] --> L02A
    L02A["L02 Part A: VAE Encoder"] --> P01["P01 Train VAE, visualize latent space"]
    L02A --> L02B["L02 Part B: GRU to RSSM"]
    L02B --> P02["P02 Build RSSM, compare prior vs posterior"]
    L02B --> L03A["L03 Part A: Architecture Patterns"]
    L03A --> L03B["L03 Part B: Planning mechanisms"]
    L03B --> P03["P03 Train Dreamer agent"]
    P02 --> P04["P04 Swap RSSM for Transformer backbone"]
    L03A --> P04
    P03 & P04 --> L04["L04 Evaluation metrics"]
    L04 --> P05["P05 Evaluation dashboard"]
    P05 --> L05["L05 Frontier Debates"]
Loading

Suggested path: L01, L02, P01, P02, L03, P03, P04, L04, P05, L05

You do not need to finish all theory before starting a project. Build, then come back with questions.

You do not need to finish all theory before starting a project. Build, then come back with questions.


Repo structure

learn-world-model/
├── docs/                                  # VitePress documentation site
│   ├── .vitepress/config.mts             # nav and sidebar (EN + ZH)
│   ├── en/lectures/                       # 5 English lecture pages
│   ├── zh/lectures/                       # 5 Chinese lecture pages
│   ├── en/projects/                       # 5 English project pages
│   └── zh/projects/                       # 5 Chinese project pages
├── external/world-model-tutorial/         # PyTorch source referenced by projects
│   └── references.md                      # four-era history and architecture survey
├── scripts/                               # build utilities (screenshots, PDF)
└── package.json

Community

Scan the QR code to join the WeChat discussion group (微信交流群):

WeChat Group QR Code

Contributing

Contributions are welcome. Before submitting a pull request, read CLAUDE.md for the writing style rules that apply to all lecture and project files (no em dashes, no linear mermaid diagrams, no arrow-chain prose, EN/ZH sync, and others). Content that does not follow those rules will be asked to revise before merging.


Contributors (Tutorial)

Name Role Affiliation GitHub
Zhimin Zhao Project Lead Queen's University @zhimin-z
Qi Wang Project Lead Shanghai Jiao Tong University @qiwang067

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