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Specifying rewards for reinforcement learned (RL) agents is challenging.Preference-based RL (PbRL) mitigates these challenges by inferring a rewardfrom feedback over sets of trajectories. However, the effectiveness of PbRL islimited by the amount of feedback needed to reliably recover the structure ofthe target reward. We present the PRIor Over Rewards (PRIOR) framework, whichincorporates priors about the structure of the reward function and thepreference feedback into the reward learning process. Imposing these priors assoft constraints on the reward learning objective reduces the amount offeedback required by half and improves overall reward recovery. Additionally,we demonstrate that using an abstract state space for the computation of thepriors further improves the reward learning and the agent's performance.

Symbol Guided Hindsight Priors for Reward Learning from Human Preferences