  Reinforcement learning (RL) requires access to a reward function that
incentivizes the right behavior, but these are notoriously hard to specify for
complex tasks. Preference-based RL provides an alternative: learning policies
using a teacher's preferences without pre-defined rewards, thus overcoming
concerns associated with reward engineering. However, it is difficult to
quantify the progress in preference-based RL due to the lack of a commonly
adopted benchmark. In this paper, we introduce B-Pref: a benchmark specially
designed for preference-based RL. A key challenge with such a benchmark is
providing the ability to evaluate candidate algorithms quickly, which makes
relying on real human input for evaluation prohibitive. At the same time,
simulating human input as giving perfect preferences for the ground truth
reward function is unrealistic. B-Pref alleviates this by simulating teachers
with a wide array of irrationalities, and proposes metrics not solely for
performance but also for robustness to these potential irrationalities. We
showcase the utility of B-Pref by using it to analyze algorithmic design
choices, such as selecting informative queries, for state-of-the-art
preference-based RL algorithms. We hope that B-Pref can serve as a common
starting point to study preference-based RL more systematically. Source code is
available at https://github.com/rll-research/B-Pref.

Machine Learning;Artificial Intelligence;Human-Computer Interaction