Tree Ensembles for Contextual Bandits
Hannes Nilsson Rikard Johansson Niklas Åkerblom Morteza Haghir Chehreghani
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.