Residual Reward Models for Preference-based Reinforcement Learning

• URL: http://arxiv.org/abs/2507.00611v1
• Date: Tue, 01 Jul 2025 09:43:57 GMT
• Title: Residual Reward Models for Preference-based Reinforcement Learning
• Authors: Chenyang Cao, Miguel Rogel-García, Mohamed Nabail, Xueqian Wang, Nicholas Rhinehart,
• Abstract summary: Preference-based Reinforcement Learning (PbRL) provides a way to learn high-performance policies in environments where the reward signal is hard to specify.<n>PbRL can suffer from slow convergence speed since it requires training in a reward model.<n>We propose a method to effectively leverage prior knowledge with a Residual Reward Model (RRM)
• Score: 11.797520525358564
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Preference-based Reinforcement Learning (PbRL) provides a way to learn high-performance policies in environments where the reward signal is hard to specify, avoiding heuristic and time-consuming reward design. However, PbRL can suffer from slow convergence speed since it requires training in a reward model. Prior work has proposed learning a reward model from demonstrations and fine-tuning it using preferences. However, when the model is a neural network, using different loss functions for pre-training and fine-tuning can pose challenges to reliable optimization. In this paper, we propose a method to effectively leverage prior knowledge with a Residual Reward Model (RRM). An RRM assumes that the true reward of the environment can be split into a sum of two parts: a prior reward and a learned reward. The prior reward is a term available before training, for example, a user's ``best guess'' reward function, or a reward function learned from inverse reinforcement learning (IRL), and the learned reward is trained with preferences. We introduce state-based and image-based versions of RRM and evaluate them on several tasks in the Meta-World environment suite. Experimental results show that our method substantially improves the performance of a common PbRL method. Our method achieves performance improvements for a variety of different types of prior rewards, including proxy rewards, a reward obtained from IRL, and even a negated version of the proxy reward. We also conduct experiments with a Franka Panda to show that our method leads to superior performance on a real robot. It significantly accelerates policy learning for different tasks, achieving success in fewer steps than the baseline. The videos are presented at https://sunlighted.github.io/RRM-web/.

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