# A Reinforcement Learning Approach to Online Learning of Decision Trees

arXiv: https://arxiv.org/abs/1507.06923 · HTML: https://mlsanity.com/paper/1507.06923/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2015.

## Built on by (most cited first, 4 of 4)

- [Breiman meets Bellman: Non-Greedy Decision Trees with MDPs](https://mlsanity.com/paper/2309.12701/) (2023)
- [Efficient Online Decision Tree Learning with Active Feature Acquisition](https://mlsanity.com/paper/2305.02093/) (2023)
- [Learning Decision Trees as Amortized Structure Inference](https://mlsanity.com/paper/2503.06985/) (2025)
- [Optimal Interpretability-Performance Trade-off of Classification Trees with Black-Box Reinforcement Learning](https://mlsanity.com/paper/2304.05839/) (2023)

## Abstract (arXiv)

Online decision tree learning algorithms typically examine all features of a new data point to update model parameters. We propose a novel alternative, Reinforcement Learning- based Decision Trees (RLDT), that uses Reinforcement Learning (RL) to actively examine a minimal number of features of a data point to classify it with high accuracy. Furthermore, RLDT optimizes a long term return, providing a better alternative to the traditional myopic greedy approach to growing decision trees. We demonstrate that this approach performs as well as batch learning algorithms and other online decision tree learning algorithms, while making significantly fewer queries about the features of the data points. We also show that RLDT can effectively handle concept drift.