cy0307/d-world-model

World model + planning (CEM)

Learns environment dynamics, then plans with the Cross-Entropy Method to reach a goal inside imagination.

Trained from scratch in Ropedia Academy — an interactive, bilingual course on embodied & spatial AI. Educational model: small and quick to train; the value is the method and a reproducible pipeline, not a leaderboard score. Try it live in the Ropedia demos Space.

At a glance

Base model	Trained from scratch (random initialization) — no pretrained base model.
Task	model-based control
Training objective	Supervised one-step dynamics prediction (MSE); planning by the Cross-Entropy Method (CEM).
Track	D · Scene & world models
Notebook

Dataset

• Name: 2D point-mass rollouts
• Type: synthetic — procedural env
• Size / stats: 60,000 transitions (3,000 random starts × 20 steps); state 4-D [x,y,vx,vy], action 2-D
• Split: train only
• Source: procedural env

Training config

Adam (lr 1e-3), 1500 steps for the dynamics model; planning by Cross-Entropy Method (CEM).

Evaluation results

metric	value	meaning
dyn_mse (final)	0.0
final_dist.planner	0.0062
final_dist.random	2.169

Inference example

import torch
state = torch.load("dynamics.pt", map_location="cpu")   # this repo's checkpoint
# Rebuild the exact module from the lab notebook (see "Reproduce"), then:
# model.load_state_dict(state); model.eval()

Limitations

Educational scale. Trained quickly on CPU on small or synthetic data, so absolute numbers are not competitive with production systems — the value is the method and a reproducible pipeline. No large-scale data, no hyperparameter sweep, and no multi-seed variance is reported. Not for production use.

Single 2D point-mass; one-step model errors compound over long horizons.

Failure cases

CEM plans poorly when the learned model is queried off-distribution; errors compound over the horizon.

Reproduce / train your own

One click: open the notebook in Colab → Runtime → GPU → Run all, then run its Publish to the Hugging Face Hub cell.

From a shell:

git clone https://github.com/ChaoYue0307/ropedia-academy.git && cd ropedia-academy
pip install torch numpy matplotlib scikit-learn scikit-image gymnasium
jupyter nbconvert --to notebook --execute notebooks/training/D_world_model.ipynb --output run.ipynb
# optional: override training length, e.g.  STEPS=2000  (or EPISODES=600)  before running

Files

• dynamics.pt
• figure.png
• metrics.json

License

Code & weights: MIT (this repository) — educational use encouraged.
Data: generated procedurally in the notebook — no external dataset.

Citation

If you use this model or the course materials, please cite:

@misc{ropedia_academy,
  title  = {Ropedia Academy: an interactive course on embodied & spatial AI},
  author = {Ropedia Academy},
  year   = {2026},
  howpublished = {\url{https://chaoyue0307.github.io/ropedia-academy/}}
}

Method / original work: Ha & Schmidhuber, World Models, NeurIPS 2018; Rubinstein, The Cross-Entropy Method, 1999.

Related assets

• 🚀 Live demos: https://huggingface.co/spaces/cy0307/ropedia-demos
• 🤗 All trained models + collection: https://huggingface.co/cy0307
• 📚 Course & all labs: https://chaoyue0307.github.io/ropedia-academy/ · Labs tab
• 💻 Source / notebooks: github.com/ChaoYue0307/ropedia-academy

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Part of the Ropedia Academy trained-model collection. Contributions & issues welcome on GitHub.