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English(EN) Sampling-Based Safe Reinforcement Learning

新框架增强了多智能体和基于采样的强化学习的安全性

研究人员开发了新的安全强化学习(RL)框架,以应对实际部署中的挑战。一种名为SafeDiSTL的方法分解了多智能体RL任务中的联合约束,允许根据智能体对共享安全违规行为的影响来分配信用。另一种方法,基于采样的安全强化学习(SBSRL),在动态样本中强制执行约束,以确保探索过程中的安全性,并为策略恢复提供理论保证。这两种方法在模拟中都显示出更高的安全性和效率,而SBSRL在机器人硬件上也表现出色。 AI

影响 安全强化学习的进步对于在现实世界场景中部署AI智能体至关重要,尤其是在机器人和自主系统中。

排序理由 两篇arXiv论文介绍了安全强化学习的新方法。

在 arXiv cs.AI 阅读 →

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新框架增强了多智能体和基于采样的强化学习的安全性

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Research
两篇arXiv论文介绍了安全强化学习的新方法。
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paper, safety
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Hongrui Zheng, Aditya Singh, Rahul Mangharam ·

    SafeDiSTL:分解联合约束以实现安全的强化学习多智能体

    arXiv:2610.13737v1 Announce Type: cross Abstract: Cooperative multi-agent tasks often impose constraints on a shared objective or collective safety. A team level violation cost function can measure this constraint outcome, but does not distinguish which agents' actions most stron…

  2. arXiv cs.AI TIER_1 English(EN) · Luca Vignola, Bruce D. Lee, Manish Prajapat, Manuel Wendl, Melanie Zeilinger, Andreas Krause, Yarden As ·

    基于采样的安全强化学习

    arXiv:2605.19469v2 Announce Type: replace-cross Abstract: Safe exploration remains a fundamental challenge in reinforcement learning (RL), limiting the deployment of RL agents in the real world. We propose Sampling-Based Safe Reinforcement Learning (SBSRL), a model-based RL algor…