  Purpose of review: Recent advances in sensing, actuation, and computation
have opened the door to multi-robot systems consisting of hundreds/thousands of
robots, with promising applications to automated manufacturing, disaster
relief, harvesting, last-mile delivery, port/airport operations, or search and
rescue. The community has leveraged model-free multi-agent reinforcement
learning (MARL) to devise efficient, scalable controllers for multi-robot
systems (MRS). This review aims to provide an analysis of the state-of-the-art
in distributed MARL for multi-robot cooperation.
  Recent findings: Decentralized MRS face fundamental challenges, such as
non-stationarity and partial observability. Building upon the "centralized
training, decentralized execution" paradigm, recent MARL approaches include
independent learning, centralized critic, value decomposition, and
communication learning approaches. Cooperative behaviors are demonstrated
through AI benchmarks and fundamental real-world robotic capabilities such as
multi-robot motion/path planning.
  Summary: This survey reports the challenges surrounding decentralized
model-free MARL for multi-robot cooperation and existing classes of approaches.
We present benchmarks and robotic applications along with a discussion on
current open avenues for research.
