Memory based Multiagent One Shot Learning

One shot learning is particularly difficult in multiagent systems where the relevant information is distributed across agents, and inter-agent interactions shape global emergent behavior. This paper introduces a distributed learning framework called Distributed Modular Memory Unit (DMMU) that creates a shared external memory to enable one shot adaptive learning in multiagent systems. In DMMU, a shared external memory is selectively accessed by agents acting asynchronously and in parallel. Each agent processes its own stream of sequential information independently while interacting with the shared external memory to identify, retain, and propagate salient information. This enables DMMU to rapidly assimilate task features from a group of distributed agents, consolidate it into a reconfigurable external memory, and use it for one shot multiagent learning. We compare the performance of the DMMU framework on a simulated cybersecurity task with traditional feedforward ensembles, LSTM based agents, and a centralized framework. Results demonstrate that DMMU significantly outperforms the other methods and exhibits distributed one shot learning.