  There are several real-world tasks that would benefit from applying
multiagent reinforcement learning (MARL) algorithms, including the coordination
among self-driving cars. The real world has challenging conditions for
multiagent learning systems, such as its partial observable and nonstationary
nature. Moreover, if agents must share a limited resource (e.g. network
bandwidth) they must all learn how to coordinate resource use. This paper
introduces a deep recurrent multiagent actor-critic framework (R-MADDPG) for
handling multiagent coordination under partial observable set-tings and limited
communication. We investigate recurrency effects on performance and
communication use of a team of agents. We demonstrate that the resulting
framework learns time dependencies for sharing missing observations, handling
resource limitations, and developing different communication patterns among
agents.
