  The market for domestic robots made to perform household chores is growing as
these robots relieve people of everyday responsibilities. Domestic robots are
generally welcomed for their role in easing human labor, in contrast to
industrial robots, which are frequently criticized for displacing human
workers. But before these robots can carry out domestic chores, they need to
become proficient in several minor activities, such as recognizing their
surroundings, making decisions, and picking up on human behaviors.
Reinforcement learning, or RL, has emerged as a key robotics technology that
enables robots to interact with their environment and learn how to optimize
their actions to maximize rewards. However, the goal of Deep Reinforcement
Learning is to address more complicated, continuous action-state spaces in
real-world settings by combining RL with Neural Networks. The efficacy of
DeepRL can be further augmented through interactive feedback, in which a
trainer offers real-time guidance to expedite the robot's learning process.
Nevertheless, the current methods have drawbacks, namely the transient
application of guidance that results in repeated learning under identical
conditions. Therefore, we present a novel method to preserve and reuse
information and advice via Deep Interactive Reinforcement Learning, which
utilizes a persistent rule-based system. This method not only expedites the
training process but also lessens the number of repetitions that instructors
will have to carry out. This study has the potential to advance the development
of household robots and improve their effectiveness and efficiency as learners.
