Dynamic Memory for Interpretable Sequential Optimisation. (arXiv:2206.13960v1 [cs.LG])

Real-world applications of reinforcement learning for recommendation and
experimentation faces a practical challenge: the relative reward of different
bandit arms can evolve over the lifetime of the learning agent. To deal with
these non-stationary cases, the agent must forget some historical knowledge, as
it may no longer be relevant to minimise regret. We present a solution to
handling non-stationarity that is suitable for deployment at scale, to provide
business operators with automated adaptive optimisation. Our solution aims to
provide interpretable …

arxiv lg memory