用自编码器模拟海马体，实现多任务系统的动态记忆与调度。
Integrating Functionalities To A System Via Autoencoder Hippocampus Network
- 用自编码器将策略参数压缩为技能向量，实现记忆与恢复。
- 通过图神经网络管理子任务拓扑结构，支持动态任务执行。
- 适合需要持续学习多技能的智能体系统研究者参考。
将多种功能整合到一个系统中是深度学习领域的关键挑战。尽管大脑如何编码、解码信息并学习多样技能的精确机制尚不明确，但记忆在其中扮演着核心角色。本文探讨了一种受自编码器启发的海马体网络在多功能系统中的实现与应用。提出一种基于自编码器的策略函数参数记忆方法：编码器将策略参数映射为技能向量，解码器则通过该向量还原参数，使策略可针对不同任务动态调整。进一步采用技能向量图神经网络表示子任务的同胚拓扑结构，并管理子任务执行流程。
原文摘要 · Abstract (English)
Integrating multiple functionalities into a system poses a fascinating challenge to the field of deep learning. While the precise mechanisms by which the brain encodes and decodes information, and learns diverse skills, remain elusive, memorization undoubtedly plays a pivotal role in this process. In this article, we delve into the implementation and application of an autoencoder-inspired hippocampus network in a multi-functional system. We propose an autoencoder-based memorization method for policy function's parameters. Specifically, the encoder of the autoencoder maps policy function's parameters to a skill vector, while the decoder retrieves the parameters via this skill vector. The policy function is dynamically adjusted tailored to corresponding tasks. Henceforth, a skill vectors graph neural network is employed to represent the homeomorphic topological structure of subtasks and manage subtasks execution.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。