SMAClite: A Lightweight Environment for Multi-Agent Reinforcement
  Learning
Adam Michalski Filippos Christianos Stefano V. Albrecht
There is a lack of standard benchmarks for Multi-Agent Reinforcement Learning
(MARL) algorithms. The Starcraft Multi-Agent Challenge (SMAC) has been widely
used in MARL research, but is built on top of a heavy, closed-source computer
game, StarCraft II. Thus, SMAC is computationally expensive and requires
knowledge and the use of proprietary tools specific to the game for any
meaningful alteration or contribution to the environment. We introduce SMAClite
-- a challenge based on SMAC that is both decoupled from Starcraft II and
open-source, along with a framework which makes it possible to create new
content for SMAClite without any special knowledge. We conduct experiments to
show that SMAClite is equivalent to SMAC, by training MARL algorithms on
SMAClite and reproducing SMAC results. We then show that SMAClite outperforms
SMAC in both runtime speed and memory.