Integrating Functionalities To A System Via Autoencoder Hippocampus Network
This work addresses the problem of multi-functional system integration for AI and robotics researchers, but it is incremental as it builds on existing autoencoder and graph neural network techniques.
The paper tackles the challenge of integrating multiple functionalities into a system by proposing an autoencoder-inspired hippocampus network that maps policy parameters to skill vectors for dynamic task adjustment, achieving improved performance in multi-task scenarios with a 15% increase in efficiency over baseline methods.
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.