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Integrating Functionalities To A System Via Autoencoder Hippocampus Network

Siwei Luo

TL;DR

The paper addresses how to endow a single system with multiple functions by separating memorization from task execution. It introduces an autoencoder hippocampus network that encodes policy weights into a skill vector $S$ via $S = E(W)$ and reconstructs them with $W = D(S)$, while a graph neural network manages subtasks through a skill-vector graph. The approach enables zero-shot inference: given a skill vector, the decoder recalls the corresponding policy parameters without revisiting raw data. Framed within dynamical hierarchical reinforcement learning and classical control supervision, the method aims to improve sample efficiency, scalability, and adaptability for memory-driven, multi-functional systems.

Abstract

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.

Integrating Functionalities To A System Via Autoencoder Hippocampus Network

TL;DR

The paper addresses how to endow a single system with multiple functions by separating memorization from task execution. It introduces an autoencoder hippocampus network that encodes policy weights into a skill vector via and reconstructs them with , while a graph neural network manages subtasks through a skill-vector graph. The approach enables zero-shot inference: given a skill vector, the decoder recalls the corresponding policy parameters without revisiting raw data. Framed within dynamical hierarchical reinforcement learning and classical control supervision, the method aims to improve sample efficiency, scalability, and adaptability for memory-driven, multi-functional systems.

Abstract

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.

Paper Structure

This paper contains 6 sections, 7 equations, 3 figures.

Table of Contents

  1. Introduction
  2. Supervised learning based on classical control theory
  3. Autoencoder Hippocampus Network
  4. Execution via traversing skill vectors graph
  5. Conclusion
  6. Data availability

Figures (3)

  • Figure 1: Available classical control can serve as the ground truth for actions and assist in training the policy function through supervised learning.
  • Figure 2: The parameters of the policy function can be encoded, stored, and retrieved via an autoencoder hippocampus network.
  • Figure 3: The latent layer of an autoencoder hippocampus network provides an interface for retrieving the parameters of the policy function. By traversing the graph of skill vectors, a complex task can be completed, and a corresponding sequence of behaviors can be generated.