新的Short-to-Long方法增强了深度强化学习性能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员引入了一种名为Short-to-Long (S2L)的新方法，通过解决从持续变化的价值目标中学习的挑战来改进深度强化学习。S2L为长视界行为学习器提供由小折扣源网络训练的特征，并使用分离连接来防止行为价值损失更新源。该方法在Atari游戏、在线连续控制和离线连续控制中展示了整体性能提升，尤其是在57个Atari游戏中的40个上提高了中位数性能。 AI

影响这种新方法可能导致在具有延迟奖励的复杂环境中更有效地训练AI代理。

排序理由该集群包含一篇详细介绍深度强化学习新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Tong Che, Boris Ivanovic, Marco Pavone · 2026-10-23 04:00

Short-to-Long: Detached Features for Long-Horizon Value Learning

arXiv:2610.30461v1 Announce Type: new Abstract: Learning useful representations from continually changing value targets remains a challenge in deep reinforcement learning. We study the temporal horizon of this supervision: smaller discounts make these targets less sensitive to di…

本文如何被排名

报道来源 [1]

Short-to-Long: Detached Features for Long-Horizon Value Learning

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