[Paper Review] How to Fine-tune the Model: Unified Model Shift and Model Bias Policy Optimization

Hai Zhang, Hang Yu+6 more|arXiv (Cornell University)|Sep 22, 2023

Reinforcement Learning in RoboticsComputer Science3 citations

TL;DR

This paper proposes USB-PO, a unified policy optimization framework that jointly minimizes model shift and model bias during model-based reinforcement learning fine-tuning. By deriving a theoretically grounded objective that adaptively balances these two factors, USB-PO ensures performance improvement guarantees while preventing overfitting, achieving state-of-the-art results on challenging benchmarks.

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.

Motivation & Objective

• To address the challenge of high coupling between model learning and policy optimization in model-based reinforcement learning (MBRL).
• To overcome the limitations of prior methods that rely on fixed thresholds to constrain model shift, which reduce adaptability during training.
• To unify the treatment of model shift and model bias into a single, theoretically grounded optimization objective.
• To develop a fine-tuning process that adaptively adjusts model updates to ensure performance improvement while avoiding overfitting.
• To achieve state-of-the-art performance on challenging MBRL benchmarks through a principled, adaptive algorithm.

Proposed method

• The authors derive a unified optimization objective that simultaneously accounts for model shift and model bias, enabling joint minimization.
• The method introduces a performance difference bound that explicitly incorporates model shift, avoiding reliance on fixed thresholds.
• It formulates a fine-tuning process that dynamically adjusts model updates based on the trade-off between bias and shift.
• The approach uses a policy optimization framework that integrates the unified objective, ensuring theoretical performance guarantees.
• The resulting algorithm, USB-PO (Unified model Shift and model Bias Policy Optimization), is straightforward to implement and integrates seamlessly into existing MBRL pipelines.

Experimental results

Research questions

• RQ1 How can model shift and model bias be jointly optimized in model-based reinforcement learning to ensure performance improvement?
• RQ2 What is a theoretically grounded, adaptive alternative to fixed-threshold constraints on model shift?
• RQ3 Can a unified objective that balances model shift and bias lead to improved generalization and robustness in MBRL?
• RQ4 How does the proposed method compare to existing MBRL algorithms in terms of sample efficiency and final performance on challenging environments?
• RQ5 What is the impact of adaptive model update scheduling on preventing overfitting and maintaining policy performance?

Key findings

• USB-PO achieves state-of-the-art performance on several challenging model-based reinforcement learning benchmarks, outperforming prior methods.
• The algorithm ensures a performance improvement guarantee by adaptively balancing model shift and model bias during training.
• By avoiding fixed thresholds for model shift, USB-PO demonstrates greater adaptability and robustness across diverse training regimes.
• Empirical results show that the unified objective effectively prevents overfitting and maintains stable policy learning.
• The method achieves superior sample efficiency and final policy performance compared to existing MBRL algorithms with similar design principles.