  We consider the problem of robust multi-agent reinforcement learning (MARL)
for cooperative communication and coordination tasks. MARL agents, mainly those
trained in a centralized way, can be brittle because they can adopt policies
that act under the expectation that other agents will act a certain way rather
than react to their actions. Our objective is to bias the learning process
towards finding strategies that remain reactive towards others' behavior.
Social empowerment measures the potential influence between agents' actions. We
propose it as an additional reward term, so agents better adapt to other
agents' actions. We show that the proposed method results in obtaining higher
rewards faster and a higher success rate in three cooperative communication and
coordination tasks.
