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lepong
sauravvvv/lepong
lepong is a reinforcement learning model from sauravvvv. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as mit.
A ~13M-parameter Joint Embedding Predictive Architecture (JEPA) that watches 128×128 pixels of PettingZoo Knights-Archers-Zombies (knightsarcherszombiesv10) and predicts a 28-dim game state (archer/knight positions +…
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Updated Jul 23, 2026
Repo size
156 MB
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At a glance
- Task
- Reinforcement Learning
- Library
- pytorch
- License
- mit
- Access
- Public
- Created
- Jul 23, 2026
- Updated
- Jul 23, 2026
- SHA
- 403da4ac
Model card
From the Hugging Face model README
lepong — KAZ JEPA world model
A ~13M-parameter Joint Embedding Predictive Architecture (JEPA) that watches
128×128 pixels of PettingZoo Knights-Archers-Zombies (knights_archers_zombies_v10)
and predicts a 28-dim game state (archer/knight positions + up to 10 zombies) from a
frozen embedding. Pixels in, state out — the model reads no RAM at inference time.
Checkpoints
Multi-step rollout sweep, 100 epochs each on 100K frames (kaz_ma_128x128.lance):
| File | Rollout steps | Warmup | Notes |
|---|---|---|---|
kaz_R1.pt | 1 | – | single-step baseline |
kaz_R3.pt | 3 | 10 | discount 0.9 |
kaz_R5.pt | 5 | 15 | discount 0.9 |
Each checkpoint is self-describing: it carries game, state_dim=28,
state_names, state_mean/std, num_actions=6, and embed_dim.
Usage
import torch
ckpt = torch.load("kaz_R5.pt", map_location="cpu", weights_only=False)
print(ckpt["game"], ckpt["state_dim"], ckpt["state_names"])
Load and drive it with the lepong repo's unified player:
python -m server.play --game kaz --checkpoint checkpoints/kaz_R5.pt
License
MIT
- Task
- Reinforcement Learning
- Library
- pytorch
- License
- mit
- Created
- Jul 23, 2026
- Updated
- Jul 23, 2026