  Reinforcement Learning in domains with sparse rewards is a difficult problem,
and a large part of the training process is often spent searching the state
space in a more or less random fashion for any learning signals. For control
problems, we often have some controller readily available which might be
suboptimal but nevertheless solves the problem to some degree. This controller
can be used to guide the initial exploration phase of the learning controller
towards reward yielding states, reducing the time before refinement of a viable
policy can be initiated. In our work, the agent is guided through an auxiliary
behaviour cloning loss which is made conditional on a Q-filter, i.e. it is only
applied in situations where the critic deems the guiding controller to be
better than the agent. The Q-filter provides a natural way to adjust the
guidance throughout the training process, allowing the agent to exceed the
guiding controller in a manner that is adaptive to the task at hand and the
proficiency of the guiding controller. The contribution of this paper lies in
identifying shortcomings in previously proposed implementations of the Q-filter
concept, and in suggesting some ways these issues can be mitigated. These
modifications are tested on the OpenAI Gym Fetch environments, showing clear
improvements in adaptivity and yielding increased performance in all robotic
environments tested.
