English(EN) Regularised RL improves sim-to-real transfer

新的正则化强化学习算法增强了机器人从模拟到现实的迁移能力

作者 PulseAugur 编辑部 · [2 个来源] · 2026-10-26 21:24

开发了一种新的强化学习算法——软缓最大值Actor-Critic (SMM-AC)，以改进策略从模拟到现实世界机器人的迁移。该算法基于软缓最大值价值函数，提供了强大的鲁棒性保证。在MuJoCo基准测试中，SMM-AC的表现与Soft Actor-Critic等现有方法相当。至关重要的是，在真实机器人上，SMM-AC展示了更优越的回报和对观测、环境及动力学变化的更大韧性，突显了正则化强化学习在模拟到现实迁移中的优势。 AI

影响这项研究表明，正则化强化学习可以提高人工智能策略在从模拟环境迁移到现实世界应用时的鲁棒性，从而可能加速机器人部署。

排序理由该集群包含一篇详细介绍新算法及其在基准测试和真实世界测试中表现的研究论文。

在 arXiv cs.LG 阅读 →

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报道来源 [2]

arXiv cs.LG TIER_1 English(EN) · Danilo Vucetic, Mohamad H. Danesh, Florence Clerc, Hsiu-Chin Lin, Jonathan Tremblay · 2026-10-27 04:00

正则化强化学习改进仿真到现实迁移

arXiv:2610.32855v1 Announce Type: new Abstract: Regularised reinforcement learning (RL) has provable robustness guarantees that should ease the transfer of policies from simulation to robots, known as sim-to-real. Yet, weakly regularised algorithms like Proximal Policy Optimisati…
r/singularity TIER_2 English(EN) · /u/wxnyc · 2026-10-26 21:24

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本文如何被排名

报道来源 [2]

正则化强化学习改进仿真到现实迁移

最可怕的万圣节服装

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