  This paper addresses the problem of integrating local guide policies into a
Reinforcement Learning agent. For this, we show how to adapt existing
algorithms to this setting before introducing a novel algorithm based on a
noisy policy-switching procedure. This approach builds on a proper Approximate
Policy Evaluation (APE) scheme to provide a perturbation that carefully leads
the local guides towards better actions. We evaluated our method on a set of
classical Reinforcement Learning problems, including safety-critical systems
where the agent cannot enter some areas at the risk of triggering catastrophic
consequences. In all the proposed environments, our agent proved to be
efficient at leveraging those policies to improve the performance of any
APE-based Reinforcement Learning algorithm, especially in its first learning
stages.
