  In the field of cooperative multi-agent reinforcement learning (MARL), the
standard paradigm is the use of centralised training and decentralised
execution where a central critic conditions the policies of the cooperative
agents based on a central state. It has been shown, that in cases with large
numbers of redundant agents these methods become less effective. In a more
general case, there is likely to be a larger number of agents in an environment
than is required to solve the task. These redundant agents reduce performance
by enlarging the dimensionality of both the state space and and increasing the
size of the joint policy used to solve the environment. We propose leveraging
layerwise relevance propagation (LRP) to instead separate the learning of the
joint value function and generation of local reward signals and create a new
MARL algorithm: relevance decomposition network (RDN). We find that although
the performance of both baselines VDN and Qmix degrades with the number of
redundant agents, RDN is unaffected.
