Why long model-based rollouts are no reason for bad Q-value estimates

TL;DR

This paper challenges the belief that long model-based rollouts lead to poor Q-value estimates, showing they can improve accuracy and effectiveness in offline reinforcement learning.

Contribution

It demonstrates that long model rollouts do not necessarily cause exponential errors and can outperform model-free methods in Q-value estimation.

Findings

Long rollouts do not always lead to exponential error growth.

Model-based methods can produce better Q-value estimates than model-free approaches.

Long rollouts can enhance reinforcement learning performance.

Abstract

This paper explores the use of model-based offline reinforcement learning with long model rollouts. While some literature criticizes this approach due to compounding errors, many practitioners have found success in real-world applications. The paper aims to demonstrate that long rollouts do not necessarily result in exponentially growing errors and can actually produce better Q-value estimates than model-free methods. These findings can potentially enhance reinforcement learning techniques.