  To widen their accessibility and increase their utility, intelligent agents
must be able to learn complex behaviors as specified by (non-expert) human
users. Moreover, they will need to learn these behaviors within a reasonable
amount of time while efficiently leveraging the sparse feedback a human trainer
is capable of providing. Recent work has shown that human feedback can be
characterized as a critique of an agent's current behavior rather than as an
alternative reward signal to be maximized, culminating in the COnvergent
Actor-Critic by Humans (COACH) algorithm for making direct policy updates based
on human feedback. Our work builds on COACH, moving to a setting where the
agent's policy is represented by a deep neural network. We employ a series of
modifications on top of the original COACH algorithm that are critical for
successfully learning behaviors from high-dimensional observations, while also
satisfying the constraint of obtaining reduced sample complexity. We
demonstrate the effectiveness of our Deep COACH algorithm in the rich 3D world
of Minecraft with an agent that learns to complete tasks by mapping from raw
pixels to actions using only real-time human feedback in 10-15 minutes of
interaction.
