| title | emoji | colorFrom | colorTo | sdk | sdk_version | app_file | pinned | license |
|---|---|---|---|---|---|---|---|---|
RL Interpretable Policy Via Kolmogorov Arnold Network |
🧠➡️🔢 |
red |
purple |
gradio |
4.29.0 |
app.py |
false |
mit |
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
- Choose a RL environment from the gymnasium library. A policy from a pre-trained Proximal Policy Optimization (PPO) agent will automatically be loaded, which generates an expert dataset and videos of the agent's performance in the selected environment.
- Click the "Compute Symbolic Policy" button to train a KAN policy on the expert dataset. Once it is done, you can visualize the KAN network and watch videos of the KAN agent's performance in the selected environment !
