Horizon Imagination 增强了用于强化学习的扩散世界模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员推出了一种用于强化学习中基于扩散的世界模型的新方法 Horizon Imagination (HI)。该方法旨在提高生成未来观测值的效率，这是当前模型面临的关键挑战。HI 允许并行去噪多个未来观测值，并包含一个稳定机制和一个新颖的采样计划，该计划将去噪预算与有效视野解耦。在 Atari 100K 和 Craftium 上的实验表明，HI 可以在减少去噪步骤的情况下保持控制性能，并提供卓越的生成质量。 AI

影响这项研究通过改进强化学习智能体想象未来场景的方式，可能带来更高效、更有效的智能体。

排序理由该集群包含一篇详细介绍强化学习中扩散世界模型新方法的论文。[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) · Lior Cohen, Ofir Nabati, Kaixin Wang, Navdeep Kumar, Shie Mannor · 2026-10-28 04:00

Horizon Imagination: Efficient On-Policy Rollout in Diffusion World Models

arXiv:2602.08032v3 Announce Type: replace Abstract: We study diffusion-based world models for reinforcement learning, which offer high generative fidelity but face critical efficiency challenges in control. Current methods either require heavyweight models at inference or rely on…

本文如何被排名

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Horizon Imagination: Efficient On-Policy Rollout in Diffusion World Models

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