cartpole-base

WorldKit / cartpole-base

A base CartPole-v1 world model trained with WorldKit.

Model Details

Usage

pip install worldkit

from worldkit import WorldModel

# Load this model
model = WorldModel.from_hub("DilpreetBansi/cartpole-base")

# Encode an observation
z = model.encode(observation)  # -> (192,) latent vector

# Predict future states
result = model.predict(current_frame, actions)

# Plan to reach a goal
plan = model.plan(current_frame, goal_frame, max_steps=50)

# Score physical plausibility
score = model.plausibility(video_frames)

Task: CartPole-v1

The CartPole-v1 environment requires an agent to balance a pole on a cart by applying left/right forces. The world model learns to predict future visual observations from pixel inputs, enabling planning and control in latent space.

Training

Trained using WorldKit's built-in training pipeline on 200 episodes of pixel observations for 100 epochs:

from worldkit import WorldModel

model = WorldModel.train(
    data="cartpole_train.h5",
    config="base",
    epochs=100,
    batch_size=32,
    lr=3e-4,
    lambda_reg=0.5,
)

Architecture

Based on the LeWorldModel paper (Maes et al., 2026):

• Encoder: Vision Transformer (ViT) with CLS token pooling
• Predictor: Transformer with AdaLN-Zero conditioning on actions
• Loss: L_pred + lambda * SIGReg(Z)
• Planner: Cross-Entropy Method (CEM) in latent space

Links

• PyPI: pypi.org/project/worldkit
• GitHub: github.com/DilpreetBansi/worldkit

Citation

If you use this model, please cite WorldKit:

@software{worldkit,
  title = {WorldKit: The Open-Source World Model Runtime},
  author = {Bansi, Dilpreet},
  year = {2026},
  url = {https://github.com/DilpreetBansi/worldkit}
}

License

MIT License. See WorldKit LICENSE.

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