Preference-Guided Reinforcement Learning for Efficient Exploration

• URL: http://arxiv.org/abs/2407.06503v2
• Date: Mon, 10 Nov 2025 10:56:56 GMT
• Title: Preference-Guided Reinforcement Learning for Efficient Exploration
• Authors: Guojian Wang, Jianxiang Liu, Xinyuan Li, Faguo Wu, Xiao Zhang, Tianyuan Chen, Xuyang Chen,
• Abstract summary: We introduce LOPE: textbfLearning textbfOnline with trajectory textbfPreferencedanctextbfE, an end-to-end preference-guided RL framework.<n>Our intuition is that LOPE directly adjusts the focus of online exploration by considering human feedback as guidance.<n>LOPE outperforms several state-of-the-art methods in terms of convergence rate and overall performance.
• Score: 14.058764537783086
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we investigate preference-based reinforcement learning (PbRL), which enables reinforcement learning (RL) agents to learn from human feedback. This is particularly valuable when defining a fine-grain reward function is not feasible. However, this approach is inefficient and impractical for promoting deep exploration in hard-exploration tasks with long horizons and sparse rewards. To tackle this issue, we introduce LOPE: \textbf{L}earning \textbf{O}nline with trajectory \textbf{P}reference guidanc\textbf{E}, an end-to-end preference-guided RL framework that enhances exploration efficiency in hard-exploration tasks. Our intuition is that LOPE directly adjusts the focus of online exploration by considering human feedback as guidance, thereby avoiding the need to learn a separate reward model from preferences. Specifically, LOPE includes a two-step sequential policy optimization technique consisting of trust-region-based policy improvement and preference guidance steps. We reformulate preference guidance as a trajectory-wise state marginal matching problem that minimizes the maximum mean discrepancy distance between the preferred trajectories and the learned policy. Furthermore, we provide a theoretical analysis to characterize the performance improvement bound and evaluate the effectiveness of the LOPE. When assessed in various challenging hard-exploration environments, LOPE outperforms several state-of-the-art methods in terms of convergence rate and overall performance.The code used in this study is available at https://github.com/buaawgj/LOPE.

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