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English(EN) SMAC: Score-Matched Actor-Critics for Robust Offline-to-Online Transfer

新的SMAC方法实现了鲁棒的离线到在线强化学习迁移

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为得分匹配Actor-Critic (SMAC) 的新方法，以改进强化学习模型从离线到在线环境的迁移。传统方法在在线微调离线训练模型时，性能通常会下降，这归因于损失景观中的性能谷。SMAC通过在离线训练期间对Q函数进行正则化来解决这个问题，确保向在线学习的平稳过渡。实验表明，SMAC在迁移到Soft Actor-Critic和TD3等算法时，在各种D4RL任务上实现了显著的遗憾减少并保持了性能。 AI

影响 通过改进离线到在线迁移，使强化学习智能体在现实场景中能够更稳定有效地部署。

排序理由 该集群包含一篇详细介绍强化学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的SMAC方法实现了鲁棒的离线到在线强化学习迁移

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该集群包含一篇详细介绍强化学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Nathan Samuel de Lara, Florian Shkurti ·

    SMAC：用于鲁棒离线到在线迁移的得分匹配演员-评论家

    arXiv:2602.17632v3 Announce Type: replace-cross Abstract: Modern offline Reinforcement Learning (RL) methods find performant actor-critics, however, fine-tuning these actor-critics online with value-based RL algorithms typically causes immediate drops in performance. We provide e…

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