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