  Reinforcement Learning has suffered from poor reward specification, and
issues for reward hacking even in simple enough domains. Preference Based
Reinforcement Learning attempts to solve the issue by utilizing binary
feedbacks on queried trajectory pairs by a human in the loop indicating their
preferences about the agent's behavior to learn a reward model. In this work,
we present a state augmentation technique that allows the agent's reward model
to be robust and follow an invariance consistency that significantly improved
performance, i.e. the reward recovery and subsequent return computed using the
learned policy over our baseline PEBBLE. We validate our method on three
domains, Mountain Car, a locomotion task of Quadruped-Walk, and a robotic
manipulation task of Sweep-Into, and find that using the proposed augmentation
the agent not only benefits in the overall performance but does so, quite early
in the agent's training phase.
