Preference-Guided Reinforcement Learning for Efficient Exploration

• URL: http://arxiv.org/abs/2407.06503v1
• Date: Tue, 9 Jul 2024 02:11:12 GMT
• Title: Preference-Guided Reinforcement Learning for Efficient Exploration
• Authors: Guojian Wang, Faguo Wu, Xiao Zhang, Tianyuan Chen, Xuyang Chen, Lin Zhao,
• Abstract summary: We introduce LOPE: Learning Online with trajectory Preference guidancE, an end-to-end preference-guided RL framework. Our intuition is that LOPE directly adjusts the focus of online exploration by considering human feedback as guidance. LOPE outperforms several state-of-the-art methods regarding convergence rate and overall performance.
• Score: 7.83845308102632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we investigate preference-based reinforcement learning (PbRL) that allows 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: Learning Online with trajectory Preference guidancE, 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, avoiding learning a separate reward model from preferences. Specifically, LOPE includes a two-step sequential policy optimization process consisting of trust-region-based policy improvement and preference guidance steps. We reformulate preference guidance as a novel 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 LOPE's effectiveness. When assessed in various challenging hard-exploration environments, LOPE outperforms several state-of-the-art methods regarding convergence rate and overall performance. The code used in this study is available at \url{https://github.com/buaawgj/LOPE}.

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