Listwise Reward Estimation for Offline Preference-based Reinforcement Learning
<|reference_start|>Listwise Reward Estimation for Offline Preference-based Reinforcement Learning: In Reinforcement Learning (RL), designing precise reward functions remains to be a challenge, particularly when aligning with human intent. Preference-based RL (PbRL) was introduced to address this problem by learning reward models from human feedback. However, existing PbRL methods have limitations as they often overlook the second-order preference that indicates the relative strength of preference. In this paper, we propose Listwise Reward Estimation (LiRE), a novel approach for offline PbRL that leverages second-order preference information by constructing a Ranked List of Trajectories (RLT), which can be efficiently built by using the same ternary feedback type as traditional methods. To validate the effectiveness of LiRE, we propose a new offline PbRL dataset that objectively reflects the effect of the estimated rewards. Our extensive experiments on the dataset demonstrate the superiority of LiRE, i.e., outperforming state-of-the-art baselines even with modest feedback budgets and enjoying robustness with respect to the number of feedbacks and feedback noise. Our code is available at https://github.com/chwoong/LiRE<|reference_end|>
@article{choi2024listwise,
    title={Listwise Reward Estimation for Offline Preference-based Reinforcement
  Learning},
    author={Heewoong Choi, Sangwon Jung, Hongjoon Ahn, Taesup Moon},
    journal={arXiv preprint arXiv:2408.04190},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04190},
    primaryClass={cs.LG cs.AI}
}