  We discuss the role of coordination as a direct learning objective in
multi-agent reinforcement learning (MARL) domains. To this end, we present a
novel means of quantifying coordination in multi-agent systems, and discuss the
implications of using such a measure to optimize coordinated agent policies.
This concept has important implications for adversary-aware RL, which we take
to be a sub-domain of multi-agent learning.
