  Our work aims at efficiently leveraging ambiguous demonstrations for the
training of a reinforcement learning (RL) agent. An ambiguous demonstration can
usually be interpreted in multiple ways, which severely hinders the RL-Agent
from learning stably and efficiently. Since an optimal demonstration may also
suffer from being ambiguous, previous works that combine RL and learning from
demonstration (RLfD works) may not work well. Inspired by how humans handle
such situations, we propose to use self-explanation (an agent generates
explanations for itself) to recognize valuable high-level relational features
as an interpretation of why a successful trajectory is successful. This way,
the agent can provide some guidance for its RL learning. Our main contribution
is to propose the Self-Explanation for RL from Demonstrations (SERLfD)
framework, which can overcome the limitations of traditional RLfD works. Our
experimental results show that an RLfD model can be improved by using our
SERLfD framework in terms of training stability and performance.
