  Multi-agent reinforcement learning (MARL) models multiple agents that
interact and learn within a shared environment. This paradigm is applicable to
various industrial scenarios such as autonomous driving, quantitative trading,
and inventory management. However, applying MARL to these real-world scenarios
is impeded by many challenges such as scaling up, complex agent interactions,
and non-stationary dynamics. To incentivize the research of MARL on these
challenges, we develop MABIM (Multi-Agent Benchmark for Inventory Management)
which is a multi-echelon, multi-commodity inventory management simulator that
can generate versatile tasks with these different challenging properties. Based
on MABIM, we evaluate the performance of classic operations research (OR)
methods and popular MARL algorithms on these challenging tasks to highlight
their weaknesses and potential.
