Research Reproduction · Jupyter Notebook
A Python implementation of Reinforcement Learning Trees (Zhu et al., 2015). RLT leverages "look-ahead" reinforcement learning to master high-dimensional, sparse data where standard Random Forests fail. Includes reproduction of synthetic scenarios, UCI benchmarks vs. XGBoost, explainability analysis.
The repository provides a from-scratch implementation of Reinforcement Learning Trees (RLT) as described in Zhu, Zeng & Kosorok (JASA 2015). It includes experiments reproducing the paper's simulation scenarios, benchmarking on UCI datasets, and a Flask app for comparing RLT with Random Forest.
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