  In this paper, we consider the inventory management (IM) problem where we
need to make replenishment decisions for a large number of stock keeping units
(SKUs) to balance their supply and demand. In our setting, the constraint on
the shared resources (such as the inventory capacity) couples the otherwise
independent control for each SKU. We formulate the problem with this structure
as Shared-Resource Stochastic Game (SRSG)and propose an efficient algorithm
called Context-aware Decentralized PPO (CD-PPO). Through extensive experiments,
we demonstrate that CD-PPO can accelerate the learning procedure compared with
standard MARL algorithms.
