  Deep reinforcement learning algorithms have recently been used to train
multiple interacting agents in a centralised manner whilst keeping their
execution decentralised. When the agents can only acquire partial observations
and are faced with tasks requiring coordination and synchronisation skills,
inter-agent communication plays an essential role. In this work, we propose a
framework for multi-agent training using deep deterministic policy gradients
that enables concurrent, end-to-end learning of an explicit communication
protocol through a memory device. During training, the agents learn to perform
read and write operations enabling them to infer a shared representation of the
world. We empirically demonstrate that concurrent learning of the communication
device and individual policies can improve inter-agent coordination and
performance in small-scale systems. Our experimental results show that the
proposed method achieves superior performance in scenarios with up to six
agents. We illustrate how different communication patterns can emerge on six
different tasks of increasing complexity. Furthermore, we study the effects of
corrupting the communication channel, provide a visualisation of the
time-varying memory content as the underlying task is being solved and validate
the building blocks of the proposed memory device through ablation studies.
