  Neuroscientific theory suggests that dopaminergic neurons broadcast global
reward prediction errors to large areas of the brain influencing the synaptic
plasticity of the neurons in those regions. We build on this theory to propose
a multi-agent learning framework with spiking neurons in the generalized linear
model (GLM) formulation as agents, to solve reinforcement learning (RL) tasks.
We show that a network of GLM spiking agents connected in a hierarchical
fashion, where each spiking agent modulates its firing policy based on local
information and a global prediction error, can learn complex action
representations to solve RL tasks. We further show how leveraging principles of
modularity and population coding inspired from the brain can help reduce
variance in the learning updates making it a viable optimization technique.
