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

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