  In this empirical paper, we investigate how learning agents can be arranged
in more efficient communication topologies for improved learning. This is an
important problem because a common technique to improve speed and robustness of
learning in deep reinforcement learning and many other machine learning
algorithms is to run multiple learning agents in parallel. The standard
communication architecture typically involves all agents intermittently
communicating with each other (fully connected topology) or with a centralized
server (star topology). Unfortunately, optimizing the topology of communication
over the space of all possible graphs is a hard problem, so we borrow results
from the networked optimization and collective intelligence literatures which
suggest that certain families of network topologies can lead to strong
improvements over fully-connected networks. We start by introducing alternative
network topologies to DRL benchmark tasks under the Evolution Strategies
paradigm which we call Network Evolution Strategies. We explore the relative
performance of the four main graph families and observe that one such family
(Erdos-Renyi random graphs) empirically outperforms all other families,
including the de facto fully-connected communication topologies. Additionally,
the use of alternative network topologies has a multiplicative performance
effect: we observe that when 1000 learning agents are arranged in a carefully
designed communication topology, they can compete with 3000 agents arranged in
the de facto fully-connected topology. Overall, our work suggests that
distributed machine learning algorithms would learn more efficiently if the
communication topology between learning agents was optimized.
