Score Regularized Policy Optimization through Diffusion Behavior

• URL: http://arxiv.org/abs/2310.07297v3
• Date: Fri, 15 Mar 2024 03:42:03 GMT
• Title: Score Regularized Policy Optimization through Diffusion Behavior
• Authors: Huayu Chen, Cheng Lu, Zhengyi Wang, Hang Su, Jun Zhu,
• Abstract summary: Recent developments in offline reinforcement learning have uncovered the immense potential of diffusion modeling. We propose to extract an efficient deterministic inference policy from critic models and pretrained diffusion behavior models. Our method boosts action sampling speed by more than 25 times compared with various leading diffusion-based methods in locomotion tasks.
• Score: 25.926641622408752
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent developments in offline reinforcement learning have uncovered the immense potential of diffusion modeling, which excels at representing heterogeneous behavior policies. However, sampling from diffusion policies is considerably slow because it necessitates tens to hundreds of iterative inference steps for one action. To address this issue, we propose to extract an efficient deterministic inference policy from critic models and pretrained diffusion behavior models, leveraging the latter to directly regularize the policy gradient with the behavior distribution's score function during optimization. Our method enjoys powerful generative capabilities of diffusion modeling while completely circumventing the computationally intensive and time-consuming diffusion sampling scheme, both during training and evaluation. Extensive results on D4RL tasks show that our method boosts action sampling speed by more than 25 times compared with various leading diffusion-based methods in locomotion tasks, while still maintaining state-of-the-art performance.

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