How to Fine-tune the Model: Unified Model Shift and Model Bias Policy Optimization

• URL: http://arxiv.org/abs/2309.12671v2
• Date: Tue, 24 Oct 2023 06:09:44 GMT
• Title: How to Fine-tune the Model: Unified Model Shift and Model Bias Policy Optimization
• Authors: Hai Zhang, Hang Yu, Junqiao Zhao, Di Zhang, Chang Huang, Hongtu Zhou, Xiao Zhang, Chen Ye
• Abstract summary: This paper develops an algorithm for model-based reinforcement learning. It unifies model shift and model bias and then formulates a fine-tuning process. It achieves state-of-the-art performance on several challenging benchmark tasks.
• Score: 13.440645736306267
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Designing and deriving effective model-based reinforcement learning (MBRL) algorithms with a performance improvement guarantee is challenging, mainly attributed to the high coupling between model learning and policy optimization. Many prior methods that rely on return discrepancy to guide model learning ignore the impacts of model shift, which can lead to performance deterioration due to excessive model updates. Other methods use performance difference bound to explicitly consider model shift. However, these methods rely on a fixed threshold to constrain model shift, resulting in a heavy dependence on the threshold and a lack of adaptability during the training process. In this paper, we theoretically derive an optimization objective that can unify model shift and model bias and then formulate a fine-tuning process. This process adaptively adjusts the model updates to get a performance improvement guarantee while avoiding model overfitting. Based on these, we develop a straightforward algorithm USB-PO (Unified model Shift and model Bias Policy Optimization). Empirical results show that USB-PO achieves state-of-the-art performance on several challenging benchmark tasks.

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