Two-Step Offline Preference-Based Reinforcement Learning with Constrained Actions

• URL: http://arxiv.org/abs/2401.00330v3
• Date: Fri, 25 Oct 2024 17:31:50 GMT
• Title: Two-Step Offline Preference-Based Reinforcement Learning with Constrained Actions
• Authors: Yinglun Xu, Tarun Suresh, Rohan Gumaste, David Zhu, Ruirui Li, Zhengyang Wang, Haoming Jiang, Xianfeng Tang, Qingyu Yin, Monica Xiao Cheng, Qi Zeng, Chao Zhang, Gagandeep Singh,
• Abstract summary: We develop a novel two-step learning method called PRC: preference-based reinforcement learning with constrained actions. We empirically verify that our method has high learning efficiency on various datasets in robotic control environments.
• Score: 38.48223545539604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Preference-based reinforcement learning (PBRL) in the offline setting has succeeded greatly in industrial applications such as chatbots. A two-step learning framework where one applies a reinforcement learning step after a reward modeling step has been widely adopted for the problem. However, such a method faces challenges from the risk of reward hacking and the complexity of reinforcement learning. To overcome the challenge, our insight is that both challenges come from the state-actions not supported in the dataset. Such state-actions are unreliable and increase the complexity of the reinforcement learning problem at the second step. Based on the insight, we develop a novel two-step learning method called PRC: preference-based reinforcement learning with constrained actions. The high-level idea is to limit the reinforcement learning agent to optimize over a constrained action space that excludes the out-of-distribution state-actions. We empirically verify that our method has high learning efficiency on various datasets in robotic control environments.

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