// the find
microsoft/maro
Multi-Agent Resource Optimization (MARO) platform is an instance of Reinforcement Learning as a Service (RaaS) for real-world resource optimization problems.
MARO is a Microsoft Research platform for applying multi-agent reinforcement learning to logistics and operations research problems — think container shipping, bike-share rebalancing, VM scheduling. It bundles domain-specific simulators, RL algorithm implementations, and distributed training infrastructure into one package. Aimed at researchers and engineers who want to run MARL experiments on real-world scheduling problems without building the simulation layer themselves.
The built-in simulators (CIM, Citi Bike, VM scheduling) are the real differentiator — they model actual operational complexity with discrete-event engines backed by Cython/C++ for performance, not toy GridWorlds. The snapshot system lets you replay and inspect environment state at any tick, which is genuinely useful for debugging agent behavior. Distributed training support (grass cluster, k8s) is baked in rather than bolted on, so scaling rollouts doesn't require rewriting your experiment. The OR baseline implementations (ILP, greedy) alongside RL examples make it easy to benchmark learned policies against classical methods.
Last meaningful activity looks to be 2021-era Microsoft Research output — 921 stars and 157 forks for a four-year-old Microsoft repo is a sign this isn't actively developed or widely adopted. The Cython build step (requiring a C++ compiler) and manual PYTHONPATH wrangling make installation brittle, especially on Windows. The distributed layer depends on Redis as a coordination backend, which adds operational complexity that most researchers don't want. Documentation reads like an academic paper appendix — lots of architecture diagrams, thin on practical guidance for adding your own scenario, which is the thing most people actually want to do.