Online Limited Memory Neural-Linear Bandits with Likelihood Matching

Understand

We study neural-linear bandits for solving problems where {\em both} exploration and representation learning play an important role.

• Neural-linear bandits harnesses the representation power of Deep Neural Networks (DNNs) and combines it with efficient exploration mechanisms by leveraging uncertainty estimation of the model, designed for linear contextual bandits on top of the last hidden layer.
• In order to mitigate the problem of representation change during the process, new uncertainty estimations are computed using stored data from an unlimited buffer.
• Nevertheless, when the amount of stored data is limited, a phenomenon called catastrophic forgetting emerges.