aiqbench analysis
This paper introduces semi-offline reinforcement learning as a balanced paradigm that mitigates the high time costs of online exploration while retaining better performance than purely offline methods. By providing a theoretical foundation for comparing RL settings, it offers an optimal approach for optimizing text generation tasks with reduced overfitting risks. The work represents a significant methodological advance in machine learning that addresses the practical trade-off between training efficiency and exploration capability.
Semi-Offline Reinforcement Learning for Optimized Text Generation
arXiv:2306.09712v2 Announce Type: replace-cross Abstract: In reinforcement learning (RL), there are two major settings for interacting with the environment: online and offline. Online methods explore the environment at significant time cost, and offline methods efficiently obtain reward signals by sacrificing exploration capability. We propose semi-offline RL, a novel paradigm that smoothly transits from offline to online settings, balances exploration capability and training cost, and provides a theoretical foundation for comparing different RL settings. Based on the semi-offline formulation, we present the RL setting that is optimal in terms of optimization cost, asymptotic error, and overfitting error bound. Extensive experiments show that our semi-offline approach is efficient and yields comparable or often better performance compared with state-of-the-art methods.
Abstract:In reinforcement learning (RL), there are two major settings for interacting with the environment: online and offline. Online methods explore the environment at significant time cost, and offline methods efficiently obtain reward signals by sacrificing exploration capability. We propose semi-offline RL, a novel paradigm that smoothly transits from offline to online settings, balances exploration capability and training cost, and provides a theoretical foundation for comparing different RL settings. Based on the semi-offline formulation, we present the RL setting that is optimal in terms of optimization cost, asymptotic error, and overfitting error bound. Extensive experiments show that our semi-offline approach is efficient and yields comparable or often better performance compared with state-of-the-art methods.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2306.09712 [cs.LG]
(or arXiv:2306.09712v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2306.09712 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 211 words