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--- |
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title: RL Interpretable Policy Via Kolmogorov Arnold Network |
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emoji: 🧠➡️🔢 |
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colorFrom: red |
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colorTo: purple |
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sdk: gradio |
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sdk_version: 4.29.0 |
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app_file: app.py |
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pinned: false |
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license: mit |
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--- |
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
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### Application demo : |
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- 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. |
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- 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 ! |
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<img alt="Interpretability app demo" src="demo/app_demo.gif"> |
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