English(EN) Action abstractions for amortized sampling

新的强化学习方法使用动作抽象来提高样本效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-03 04:00

研究人员通过引入动作抽象，开发了一种改进强化学习（RL）和生成流网络（GFlowNets）的新方法。该方法通过迭代地识别常见的动作子序列并将它们“分块”成更高级别的动作来解决长轨迹中的挑战。在合成和真实世界环境中的实证评估表明，在发现多样化、高回报目标方面，尤其是在复杂的探索任务中，样本效率得到了提高。抽象动作还通过捕捉奖励景观的潜在结构来提供可解释性。 AI

影响这项研究可能带来更高效的人工智能代理，能够应对复杂的探索和规划任务。

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 →

Oussama Boussif

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Oussama Boussif, L\'ena N\'ehale Ezzine, Joseph D Viviano, Micha{\l} Koziarski, Moksh Jain, Esmeralda S. Whitammer, Emmanuel Bengio, Rim Assouel, Yoshua Bengio · 2026-09-03 04:00

用于摊销采样的动作抽象

arXiv:2410.15184v2 Announce Type: replace-cross Abstract: As trajectories sampled by policies used by reinforcement learning (RL) and generative flow networks (GFlowNets) grow longer, credit assignment and exploration become more challenging, and the long planning horizon hinders…

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报道来源 [1]

用于摊销采样的动作抽象

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