Why long model-based rollouts are no reason for bad Q-value estimates
This addresses a key bottleneck for practitioners in reinforcement learning, though it appears incremental as it builds on existing critiques and successes.
The paper tackles the problem of compounding errors in model-based offline reinforcement learning with long rollouts, showing that long rollouts do not necessarily lead to exponentially growing errors and can produce better Q-value estimates than model-free methods.
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.