  An important goal in artificial intelligence is to create agents that can
both interact naturally with humans and learn from their feedback. Here we
demonstrate how to use reinforcement learning from human feedback (RLHF) to
improve upon simulated, embodied agents trained to a base level of competency
with imitation learning. First, we collected data of humans interacting with
agents in a simulated 3D world. We then asked annotators to record moments
where they believed that agents either progressed toward or regressed from
their human-instructed goal. Using this annotation data we leveraged a novel
method - which we call "Inter-temporal Bradley-Terry" (IBT) modelling - to
build a reward model that captures human judgments. Agents trained to optimise
rewards delivered from IBT reward models improved with respect to all of our
metrics, including subsequent human judgment during live interactions with
agents. Altogether our results demonstrate how one can successfully leverage
human judgments to improve agent behaviour, allowing us to use reinforcement
learning in complex, embodied domains without programmatic reward functions.
Videos of agent behaviour may be found at https://youtu.be/v_Z9F2_eKk4.
