  The use of interactive advice in reinforcement learning scenarios allows for
speeding up the learning process for autonomous agents. Current interactive
reinforcement learning research has been limited to real-time interactions that
offer relevant user advice to the current state only. Moreover, the information
provided by each interaction is not retained and instead discarded by the agent
after a single use. In this paper, we present a method for retaining and
reusing provided knowledge, allowing trainers to give general advice relevant
to more than just the current state. Results obtained show that the use of
broad-persistent advice substantially improves the performance of the agent
while reducing the number of interactions required for the trainer.
