Finite-Time Error Analysis of Online Model-Based Q-Learning with a Relaxed Sampling Model
<|reference_start|>Finite-Time Error Analysis of Online Model-Based Q-Learning with a Relaxed Sampling Model: Reinforcement learning has witnessed significant advancements, particularly with the emergence of model-based approaches. Among these, $Q$-learning has proven to be a powerful algorithm in model-free settings. However, the extension of $Q$-learning to a model-based framework remains relatively unexplored. In this paper, we delve into the sample complexity of $Q$-learning when integrated with a model-based approach. Through theoretical analyses and empirical evaluations, we seek to elucidate the conditions under which model-based $Q$-learning excels in terms of sample efficiency compared to its model-free counterpart.<|reference_end|>
@article{lim2024finite-time,
    title={Finite-Time Error Analysis of Online Model-Based Q-Learning with a
  Relaxed Sampling Model},
    author={Han-Dong Lim, HyeAnn Lee, Donghwan Lee},
    journal={arXiv preprint arXiv:2402.11877},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.11877},
    primaryClass={cs.LG cs.AI}
}