Title: TarMAC: Targeted Multi-Agent Communication. Abstract: We explore the collaborative multi-agent setting where a team of deep reinforcement learning agents attempt to solve a shared task in partially observable environments. In this scenario, learning an effective communication protocol is key. We propose a communication protocol that allows for targeted communication, where agents learn \emph{what} messages to send and \emph{who} to send them to. Additionally, we introduce a multi-stage communication approach where the agents co-ordinate via several rounds of communication before taking an action in the environment. We evaluate our approach on several cooperative multi-agent tasks, of varying difficulties with varying number of agents, in a variety of environments ranging from 2D grid layouts of shapes and simulated traffic junctions to complex 3D indoor environments. We demonstrate the benefits of targeted as well as multi-stage communication. Moreover, we show that the targeted communication strategies learned by the agents are quite interpretable and intuitive.