  A significant challenge facing researchers in the area of multi-agent
reinforcement learning (MARL) pertains to the identification of a library that
can offer fast and compatible development for multi-agent tasks and algorithm
combinations, while obviating the need to consider compatibility issues. In
this paper, we present MARLlib, a library designed to address the
aforementioned challenge by leveraging three key mechanisms: 1) a standardized
multi-agent environment wrapper, 2) an agent-level algorithm implementation,
and 3) a flexible policy mapping strategy. By utilizing these mechanisms,
MARLlib can effectively disentangle the intertwined nature of the multi-agent
task and the learning process of the algorithm, with the ability to
automatically alter the training strategy based on the current task's
attributes. The MARLlib library's source code is publicly accessible on GitHub:
\url{https://github.com/Replicable-MARL/MARLlib}.
