  Preference-based reinforcement learning (RL) provides a framework to train
agents using human feedback through pairwise preferences over pairs of
behaviors, enabling agents to learn desired behaviors when it is difficult to
specify a numerical reward function. While this paradigm leverages human
feedback, it currently treats the feedback as given by a single human user.
Meanwhile, incorporating preference feedback from crowds (i.e. ensembles of
users) in a robust manner remains a challenge, and the problem of training RL
agents using feedback from multiple human users remains understudied. In this
work, we introduce Crowd-PrefRL, a framework for performing preference-based RL
leveraging feedback from crowds. This work demonstrates the viability of
learning reward functions from preference feedback provided by crowds of
unknown expertise and reliability. Crowd-PrefRL not only robustly aggregates
the crowd preference feedback, but also estimates the reliability of each user
within the crowd using only the (noisy) crowdsourced preference comparisons.
Most importantly, we show that agents trained with Crowd-PrefRL outperform
agents trained with majority-vote preferences or preferences from any
individual user in most cases, especially when the spread of user error rates
among the crowd is large. Results further suggest that our method can identify
minority viewpoints within the crowd.
