TarMAC: Targeted Multi-Agent Communication

Understand

We propose a targeted communication architecture for multi-agent reinforcement learning, where agents learn both what messages to send and whom to address them to while performing cooperative tasks in partially-observable environments.

• This targeting behavior is learnt solely from downstream task-specific reward without any communication supervision.
• We additionally augment this with a multi-round communication approach where agents coordinate via multiple rounds of communication before taking actions in the environment.
• We evaluate our approach on a diverse set of 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 3D indoor environments, and demonstrate the benefits of targeted and multi-round communication.