  Fluid human-agent communication is essential for the future of
human-in-the-loop reinforcement learning. An agent must respond appropriately
to feedback from its human trainer even before they have significant experience
working together. Therefore, it is important that learning agents respond well
to various feedback schemes human trainers are likely to provide. This work
analyzes the COnvergent Actor-Critic by Humans (COACH) algorithm under three
different types of feedback-policy feedback, reward feedback, and advantage
feedback. For these three feedback types, we find that COACH can behave
sub-optimally. We propose a variant of COACH, episodic COACH (E-COACH), which
we prove converges for all three types. We compare our COACH variant with two
other reinforcement-learning algorithms: Q-learning and TAMER.
