  A promising approach to improve the robustness and exploration in
Reinforcement Learning is collecting human feedback and that way incorporating
prior knowledge of the target environment. It is, however, often too expensive
to obtain enough feedback of good quality. To mitigate the issue, we aim to
rely on a group of multiple experts (and non-experts) with different skill
levels to generate enough feedback. Such feedback can therefore be inconsistent
and infrequent. In this paper, we build upon prior work -- Advise, a Bayesian
approach attempting to maximise the information gained from human feedback --
extending the algorithm to accept feedback from this larger group of humans,
the trainers, while also estimating each trainer's reliability. We show how
aggregating feedback from multiple trainers improves the total feedback's
accuracy and make the collection process easier in two ways. Firstly, this
approach addresses the case of some of the trainers being adversarial.
Secondly, having access to the information about each trainer reliability
provides a second layer of robustness and offers valuable information for
people managing the whole system to improve the overall trust in the system. It
offers an actionable tool for improving the feedback collection process or
modifying the reward function design if needed. We empirically show that our
approach can accurately learn the reliability of each trainer correctly and use
it to maximise the information gained from the multiple trainers' feedback,
even if some of the sources are adversarial.
