  Communication is essential for coordination among humans and animals.
Therefore, with the introduction of intelligent agents into the world,
agent-to-agent and agent-to-human communication becomes necessary. In this
paper, we first study learning in matrix-based signaling games to empirically
show that decentralized methods can converge to a suboptimal policy. We then
propose a modification to the messaging policy, in which the sender
deterministically chooses the best message that helps the receiver to infer the
sender's observation. Using this modification, we see, empirically, that the
agents converge to the optimal policy in nearly all the runs. We then apply
this method to a partially observable gridworld environment which requires
cooperation between two agents and show that, with appropriate approximation
methods, the proposed sender modification can enhance existing decentralized
training methods for more complex domains as well.
