Listwise Reward Estimation for Offline Preference-based Reinforcement Learning

• URL: http://arxiv.org/abs/2408.04190v1
• Date: Thu, 8 Aug 2024 03:18:42 GMT
• Title: Listwise Reward Estimation for Offline Preference-based Reinforcement Learning
• Authors: Heewoong Choi, Sangwon Jung, Hongjoon Ahn, Taesup Moon,
• Abstract summary: Listwise Reward Estimation (LiRE) is a novel approach for offline Preference-based Reinforcement Learning (PbRL) LiRE builds on existing PbRL methods by constructing a Ranked List of Trajectories (RLT) Our experiments demonstrate the superiority of LiRE, even with modest feedback budgets and enjoying robustness with respect to the number of feedbacks and feedback noise.
• Score: 20.151932308777553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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