Tree Ensemble Methods Outperform Neural Networks in Contextual Multi-Armed Bandits for Regret Minimization and Computational Efficiency

Core Concepts

This paper introduces a novel framework utilizing tree ensembles for contextual multi-armed bandits, demonstrating superior performance in regret minimization and computational efficiency compared to state-of-the-art methods based on decision trees and neural networks.

Abstract

• Bibliographic Information: Nilsson, H., Johansson, R., Åkerblom, N., & Chehreghani, M. H. (2024). Tree Ensembles for Contextual Bandits. arXiv:2402.06963v3 [cs.LG].

• Research Objective: This paper proposes a new framework for contextual multi-armed bandits based on tree ensembles, adapting Upper Confidence Bound and Thompson Sampling for both standard and combinatorial settings.

• Methodology: The authors develop two novel algorithms, Tree Ensemble Upper Confidence Bound (TEUCB) and Tree Ensemble Thompson Sampling (TETS), which leverage tree ensembles to model the relationship between contextual features and expected rewards. They evaluate their methods on benchmark datasets from the UCI machine learning repository, comparing them to existing algorithms like NeuralUCB, NeuralTS, TreeBootstrap, LinUCB, and LinTS. Additionally, they investigate the performance of their framework in a combinatorial contextual bandit setting by applying it to a real-world navigation problem on the road network of Luxembourg.

• Key Findings: The experimental results demonstrate that TEUCB and TETS, employing either XGBoost or random forests, consistently outperform other state-of-the-art methods in terms of regret minimization across all tested datasets and the real-world navigation problem. Moreover, the tree ensemble-based methods exhibit superior computational efficiency compared to their neural network counterparts.

• Main Conclusions: The authors conclude that tree ensembles offer a powerful and efficient approach to solving contextual multi-armed bandit problems. Their proposed framework, particularly TEUCB and TETS, provides a practical and effective solution for decision-making under uncertainty in various applications.

• Significance: This research significantly contributes to the field of contextual multi-armed bandits by introducing a novel framework based on tree ensembles. It highlights the advantages of tree-based methods over neural networks in terms of both performance and computational cost, paving the way for more efficient and scalable solutions in real-world applications.

• Limitations and Future Research: While the study focuses on practical applicability, future work could explore theoretical regret bounds for the proposed methods. Additionally, investigating the impact of different tree ensemble architectures and hyperparameters on performance could further enhance the framework's effectiveness.

Stats

The time horizon for the benchmark dataset experiments was set to 10,000, except for the Mushroom dataset, where a horizon of 8,124 was used.
The neural network agents in the benchmark experiments used an architecture of one hidden layer with 100 neurons.
All tree ensemble bandits used ensembles of 100 trees.
The maximum tree depth for XGBoost and random forests was set to 10.
The road network of Luxembourg used in the combinatorial bandit experiment consisted of 2,247 vertices and 5,651 edges.
The neural agents in the navigation experiment utilized a network with two hidden layers, each containing 100 neurons.

Quotes

"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."
"Our methods, called TEUCB and TETS, yield superior results on UCI benchmark datasets."
"Additionally, our methods benefit from more effective learning with less computational overhead on most problem instances, compared to existing methods that use similarly expressive machine learning models."

Key Insights Distilled From

Tree Ensembles for Contextual Bandits

by Hann... at arxiv.org 11-04-2024

https://arxiv.org/pdf/2402.06963.pdf