Integrating Functionalities To A System Via Autoencoder Hippocampus Network

TL;DR

This paper presents a novel autoencoder-inspired hippocampus network that encodes and retrieves policy functions for multi-task systems, leveraging skill vectors and graph neural networks to manage subtask structures.

Contribution

It introduces an autoencoder-based memorization method for policy parameters and integrates a skill vectors graph neural network for subtask management.

Findings

Effective encoding and retrieval of policy functions.

Successful representation of subtask structures.

Enhanced multi-task learning capabilities.

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.