Learngene:智能体中可遗传的“基因”

IF 4.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Fu Feng , Jing Wang , Xu Yang , Xin Geng
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引用次数: 0

摘要

生物智能推动了人工智能(AI)的重大进步,但一个关键的差距仍然存在:生物系统从基因中继承了天生的能力,大脑是由35亿年的进化蓝图初始化的,而机器则严重依赖于低效的、数据驱动的从零开始学习。这种差距是由于机器缺乏遗传机制来传递和积累可遗传的知识。为了弥补这一差距,我们提出了学习基因,即作为机器可遗传“基因”的网络片段。与传统的知识转移方法不同,学习基因通过选择性地封装与任务无关的知识来实现高效和普遍的知识转移。为了促进任务不可知论知识在代际间的转移和积累,我们引入了遗传强化学习(GRL),这是一个遵循拉马克原理模拟智能代理中生物体的学习和进化的框架。通过GRL,我们将学习基因识别为智能体策略网络中的网络片段,为新生智能体提供快速适应新任务的先天能力。我们展示了基于学习基因的知识转移相对于基于进化的搜索和传统的预训练模型的优势,并展示了学习基因如何通过任务不可知知识的积累而进化。总的来说,这项工作为人工智能中的知识转移和模型初始化建立了一个新的范例,为更具适应性、效率和可扩展性的学习系统提供了新的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learngene: Inheritable “genes” in intelligent agents
Biological intelligence has driven significant progress in artificial intelligence (AI), but a critical gap remains: biological systems inherit innate abilities from genes, with brains initialized by blueprints refined over 3.5 billion years of evolution, while machines rely heavily on inefficient, data-driven learning from scratch. This gap arises from the lack of a genetic mechanism in machines to transfer and accumulate inheritable knowledge across generations. To bridge this gap, we propose learngenes, network fragments that act as inheritable “genes” for machines. Unlike conventional knowledge transfer methods, learngenes enable efficient and universal knowledge transfer by selectively encapsulating task-agnostic knowledge. To facilitate the transfer and accumulation of task-agnostic knowledge across generations, we introduce Genetic Reinforcement Learning (GRL), a framework that simulates the learning and evolution of organisms in intelligent agents following Lamarckian principles. Through GRL, we identify learngenes as network fragments within agents' policy networks, equipping newborn agents with innate abilities for rapid adaptation to novel tasks. We demonstrate the advantages of learngene-based knowledge transfer over evolution-based search and traditional pre-trained models, and show how learngenes evolve through the accumulation of task-agnostic knowledge. Overall, this work establishes a novel paradigm for knowledge transfer and model initialization in AI, offering new possibilities for more adaptive, efficient, and scalable learning systems.
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来源期刊
Artificial Intelligence
Artificial Intelligence 工程技术-计算机:人工智能
CiteScore
11.20
自引率
1.40%
发文量
118
审稿时长
8 months
期刊介绍: The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.
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