  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 real-time interactions that offer
relevant user 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.
