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English(EN) TaskFrame: Connecting Task-Level Planning with Frame-Level Control for Game Agents

TaskFrame框架整合了游戏代理的规划与控制

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了TaskFrame，一个旨在通过整合高级任务规划与低延迟动作控制来增强游戏代理的新型框架。该系统利用视觉语言任务规划器将复杂目标分解为顺序指令，特殊的目标标记为控制器提供连续的嵌入。然后，因果Transformer控制器利用这些嵌入和最近的观察来预测动作，在Minecraft中取得了有竞争力的性能，包括比基线代理更高的战斗成功率。 AI

影响 该框架可能在复杂的模拟环境中带来更复杂、更有能力的人工智能代理。

排序理由 该集群描述了一篇关于游戏代理新颖框架的最新研究论文。[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 →

TaskFrame框架整合了游戏代理的规划与控制

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该集群描述了一篇关于游戏代理新颖框架的最新研究论文。[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) · Guangzhi Wang, Junfu Pu, Yiran Wang, Xingyilang Yin, Honghao Chen, Cheng Cheng, Lingen Li, Yinan Zhou, Jiarui Wu, Yinuo Jiang, Hongbo Jin, Xiaodong Cun, Ying Shan ·

    TaskFrame：将任务级规划与帧级控制连接起来，用于游戏代理

    arXiv:2610.40276v1 Announce Type: cross Abstract: Building general game agents requires both high-level task reasoning and low-latency action control in rapidly changing visual environments. These capabilities favor models with substantially different computational profiles, maki…

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