Why long model-based rollouts are no reason for bad Q-value estimates
<|reference_start|>Why long model-based rollouts are no reason for bad Q-value estimates: 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.<|reference_end|>
@article{wissmann2024why,
    title={Why long model-based rollouts are no reason for bad Q-value estimates},
    author={Philipp Wissmann, Daniel Hein, Steffen Udluft, Volker Tresp},
    journal={arXiv preprint arXiv:2407.11751},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11751},
    primaryClass={cs.LG}
}