Theoretical Analysis of Meta Reinforcement Learning: Generalization Bounds and Convergence Guarantees
<|reference_start|>Theoretical Analysis of Meta Reinforcement Learning: Generalization Bounds and Convergence Guarantees: This research delves deeply into Meta Reinforcement Learning (Meta RL) through a exploration focusing on defining generalization limits and ensuring convergence. By employing a approach this article introduces an innovative theoretical framework to meticulously assess the effectiveness and performance of Meta RL algorithms. We present an explanation of generalization limits measuring how well these algorithms can adapt to learning tasks while maintaining consistent results. Our analysis delves into the factors that impact the adaptability of Meta RL revealing the relationship, between algorithm design and task complexity. Additionally we establish convergence assurances by proving conditions under which Meta RL strategies are guaranteed to converge towards solutions. We examine the convergence behaviors of Meta RL algorithms across scenarios providing a comprehensive understanding of the driving forces behind their long term performance. This exploration covers both convergence and real time efficiency offering a perspective, on the capabilities of these algorithms.<|reference_end|>
@article{wang2024theoretical,
    title={Theoretical Analysis of Meta Reinforcement Learning: Generalization
  Bounds and Convergence Guarantees},
    author={Cangqing Wang, Mingxiu Sui, Dan Sun, Zecheng Zhang, Yan Zhou},
    journal={arXiv preprint arXiv:2405.13290},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.13290},
    primaryClass={cs.LG cs.AI}
}