Improved Learning in Evolution Strategies via Sparser Inter-Agent Network Topologies

TL;DR

This paper explores how sparser inter-agent network topologies, inspired by human collective intelligence, can enhance deep reinforcement learning by enabling faster reward acquisition and reducing communication costs.

Contribution

It introduces the idea that less connected, sparser networks can outperform fully-connected ones in deep learning, challenging conventional network design.

Findings

Sparser networks learn higher rewards faster.

Sparser networks reduce communication costs.

Alternative topologies improve training efficiency.

Abstract

We draw upon a previously largely untapped literature on human collective intelligence as a source of inspiration for improving deep learning. Implicit in many algorithms that attempt to solve Deep Reinforcement Learning (DRL) tasks is the network of processors along which parameter values are shared. So far, existing approaches have implicitly utilized fully-connected networks, in which all processors are connected. However, the scientific literature on human collective intelligence suggests that complete networks may not always be the most effective information network structures for distributed search through complex spaces. Here we show that alternative topologies can improve deep neural network training: we find that sparser networks learn higher rewards faster, leading to learning improvements at lower communication costs.