  Interactive reinforcement learning has allowed speeding up the learning
process in autonomous agents by including a human trainer providing extra
information to the agent in real-time. Current interactive reinforcement
learning research has been limited to interactions that offer relevant advice
to the current state only. Additionally, the information provided by each
interaction is not retained and instead discarded by the agent after a
single-use. In this work, we propose a persistent rule-based interactive
reinforcement learning approach, i.e., a method for retaining and reusing
provided knowledge, allowing trainers to give general advice relevant to more
than just the current state. Our experimental results show persistent advice
substantially improves the performance of the agent while reducing the number
of interactions required for the trainer. Moreover, rule-based advice shows
similar performance impact as state-based advice, but with a substantially
reduced interaction count.
