A Neural Networks Committee for the Contextual Bandit Problem
This work addresses the contextual bandit problem for machine learning applications requiring adaptive decision-making in dynamic environments, representing an incremental improvement by combining neural networks with multi-experts approaches.
The authors tackled the contextual bandit problem by proposing NeuralBandit, a new algorithm that uses multiple neural networks to model rewards without assuming stationarity, and tested it successfully on a large dataset with and without stationary rewards, showing improved performance in non-stationary settings.
This paper presents a new contextual bandit algorithm, NeuralBandit, which does not need hypothesis on stationarity of contexts and rewards. Several neural networks are trained to modelize the value of rewards knowing the context. Two variants, based on multi-experts approach, are proposed to choose online the parameters of multi-layer perceptrons. The proposed algorithms are successfully tested on a large dataset with and without stationarity of rewards.