  Reinforcement learning has enjoyed multiple successes in recent years.
However, these successes typically require very large amounts of data before an
agent achieves acceptable performance. This paper introduces a novel way of
combating such requirements by leveraging existing (human or agent) knowledge.
In particular, this paper uses demonstrations from agents and humans, allowing
an untrained agent to quickly achieve high performance. We empirically compare
with, and highlight the weakness of, HAT and CHAT, methods of transferring
knowledge from a source agent/human to a target agent. This paper introduces an
effective transfer approach, DRoP, combining the offline knowledge
(demonstrations recorded before learning) with online confidence-based
performance analysis. DRoP dynamically involves the demonstrator's knowledge,
integrating it into the reinforcement learning agent's online learning loop to
achieve efficient and robust learning.
