新方法通过对抗性采样增强深度强化学习策略的鲁棒性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-14 04:00

研究人员开发了一种名为Adversarial Importance Sampling (Advis) 的新方法，用于增强深度强化学习 (DRL) 策略在对抗性攻击下的鲁棒性。Advis 通过在现有训练轨迹上使用重要性采样来优化可验证的最坏情况回报，无需额外的环境交互或辅助网络。为了促进研究和可复现性，创建了一个名为advrl的模块化PyTorch库，提供了各种鲁棒性方法和对抗性攻击的实现。研究还强调了针对广泛攻击者评估策略的重要性，因为最优对抗性超参数不会跨代理转移，可能导致对鲁棒性估计过高。 AI

影响引入了一种在对抗性环境中提高AI代理安全性和可靠性的方法。

排序理由学术论文，详细介绍了一种新的DRL鲁棒性方法及相关库。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Amine Andam, Jamal Bentahar, Mustapha Hedabou · 2026-09-14 04:00

通过对抗性重要性采样实现鲁棒策略优化

arXiv:2609.13044v1 Announce Type: new Abstract: Significant progress has been made in safeguarding deep reinforcement learning (DRL) policies against input perturbations. Developing robust DRL involves three main stages: algorithm design, implementation, and evaluation. In this w…

本文如何被排名

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通过对抗性重要性采样实现鲁棒策略优化

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