Temporally Interpretable Differentiable Decision Trees
Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: Temporally Interpretable Differentiable Decision Trees. Researchers introduce temporal interpretability for DDTs, improving decision-making via action chunking. Their method achieves 80% fewer parameters while matching neural policies in three domains.
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Environment: simulation
“introducing two novel policy gradient algorithms that incorporate action chunking. Additionally, to maintain parameter-efficient trees, we develop an information-theoretic tree restructuring algorithm that modifies the tree during training. Across four simulation environments, we find that warm-starting action chunked DDTs from a distilled action chunked policy is the most effective way to obtain temporally interpret”
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Artifact availability · code: available
“able trees: they match neural network policies in three of the four domains while using up to 80$\%$ fewer parameters. Our code is available at https://github.com/ei5uke/temp-interp.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsTemporally Interpretable Differentiable Decision Trees
Researchers introduce temporal interpretability for DDTs, improving decision-making via action chunking. Their method achieves 80% fewer parameters while matching neural policies in three domains.
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