  Locomotion is a prime example for adaptive behavior in animals and biological
control principles have inspired control architectures for legged robots. While
machine learning has been successfully applied to many tasks in recent years,
Deep Reinforcement Learning approaches still appear to struggle when applied to
real world robots in continuous control tasks and in particular do not appear
as robust solutions that can handle uncertainties well. Therefore, there is a
new interest in incorporating biological principles into such learning
architectures. While inducing a hierarchical organization as found in motor
control has shown already some success, we here propose a decentralized
organization as found in insect motor control for coordination of different
legs. A decentralized and distributed architecture is introduced on a simulated
hexapod robot and the details of the controller are learned through Deep
Reinforcement Learning. We first show that such a concurrent local structure is
able to learn better walking behavior. Secondly, that the simpler organization
is learned faster compared to holistic approaches.
