  We propose a novel framework for contextual multi-armed bandits based on tree
ensembles. Our framework integrates two widely used bandit methods, Upper
Confidence Bound and Thompson Sampling, for both standard and combinatorial
settings. We demonstrate the effectiveness of our framework via several
experimental studies, employing both XGBoost and random forest, two popular
tree ensemble methods. Compared to state-of-the-art methods based on decision
trees and neural networks, our methods exhibit superior performance in terms of
both regret minimization and computational runtime, when applied to benchmark
datasets and the real-world application of navigation over road networks.
