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 XGBoost, a popular tree ensemble method. Compared to state-of-the-art methods based on 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.
↧
