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Optimization Methods for Interpretable Differentiable Decision Trees in Reinforcement Learning
| Content Provider | Semantic Scholar |
|---|---|
| Author | Silva, A. H. T. Eranga De Killian, Taylor W. Rodriguez, Ivan Dario Jimenez Son, Sung-Hyun Gombolay, Matthew Craig |
| Copyright Year | 2019 |
| Abstract | Decision trees are ubiquitous in machine learning for their ease of use and interpretability. Yet, these models are not typically employed in reinforcement learning as they cannot be updated online via stochastic gradient descent. We overcome this limitation by allowing for a gradient update over the entire tree that improves sample complexity affords interpretable policy extraction. First, we include theoretical motivation on the need for policy-gradient learning by examining the properties of gradient descent over differentiable decision trees. Second, we demonstrate that our approach equals or outperforms a neural network on all domains and can learn discrete decision trees online with average rewards up to 7x higher than a batch-trained decision tree. Third, we conduct a user study to quantify the interpretability of a decision tree, rule list, and a neural network with statistically significant results ($p < 0.001$). |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://arxiv.org/pdf/1903.09338v4.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
