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English(EN) ConTraIRL: Factorized Contrastive Abstractions for Transferable IRL

新的IRL框架通过解耦表示增强奖励迁移

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种名为ConTraIRL的新型框架，旨在改进逆强化学习（IRL）中的奖励迁移。该方法解决了当前IRL技术在策略需要泛化到新的环境动力学和任务目标时不可靠的问题。ConTraIRL通过学习独立的动力学和目标潜在表示来实现这一点，从而实现组合式奖励迁移，并在实验中展示了有效的少样本迁移能力。 AI

影响 该框架可以提高强化学习智能体在复杂、动态环境中的泛化能力和样本效率。

排序理由 该集群包含一篇详细介绍逆强化学习新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

新的IRL框架通过解耦表示增强奖励迁移

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该集群包含一篇详细介绍逆强化学习新框架的研究论文。[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) · Yikang Gui, Bikramjit Banerjee, Prashant Doshi ·

    ConTraIRL：可迁移IRL的因子化对比抽象

    arXiv:2606.03017v1 Announce Type: cross Abstract: Reward transfer in Inverse Reinforcement Learning (IRL) is unreliable when policies must generalize to unseen combinations of environment dynamics and task goals. We propose Factorized Contrastive Abstractions for Transferable IRL…

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