MARO Resource Optimization Platform

What does this agent do, and when should you use it?

MARO is a Multi-Agent Resource Optimization platform presented as an instance of Reinforcement Learning as a Service for real-world resource optimization. Its simulation toolkit supplies predefined scenarios and reusable building blocks for new ones, while its RL toolkit exposes abstractions including agent manager, agent, RL algorithms, learner, actor, and shapers. A distributed toolkit adds communication components, user-defined-function interfaces for message auto-handling, cluster provision, and job orchestration. Documented domains include container inventory management, bike repositioning, virtual-machine provisioning, and financial asset management; operations-research mechanisms are also supported. Python users create an environment with `maro.simulator.Env`, advance it with `step`, and inspect metrics; source installation additionally provides CLI and visualization support.

A user constructs an environment with Env(scenario=..., topology=..., start_tick=..., durations=...), then calls env.step(None) to receive metrics, decision_event, and is_done. The documented example uses the cim scenario and toy.5p_ssddd_l0.0 topology, repeatedly steps until completion, and reads final metrics from env.metrics. Setting options={"enable-dump-snapshot": "./dump_data"} when creating the environment writes a snapshot that can be inspected with maro inspector dashboard --source_path ./dump_data/YOUR_SNAPSHOT_DUMP_FOLDER. The source distribution also includes distributed communication, automatic message-handling interfaces for user-defined functions, cluster provision, and job orchestration.

1. A logistics research team evaluating resource-balancing decisions in a container inventory management simulation.
2. A transportation researcher modeling bike repositioning with the documented Citi Bike scenario.
3. A data-center team exploring virtual-machine provisioning decisions in a simulated environment.
4. A finance researcher framing asset management as a resource-optimization problem.
5. An RL engineer running an `Env` step loop to collect environment metrics and decision events.
6. A team that wants a Docker-based trial environment with Jupyter Lab and Redis Commander.

What are this agent's strengths and limitations?

How do you install or deploy this agent?

For core functionality on macOS or Linux, run pip install pymaro. On Windows, first run pip install torch===1.6.0 torchvision===0.7.0 -f https://download.pytorch.org/whl/torch_stable.html, then run pip install pymaro. CLI commands and visualization require source installation: clone the repository, then on macOS/Linux run bash scripts/install_maro.sh followed by pip install -r ./requirements.dev.txt; on Windows run ./scripts/install_maro.bat followed by pip install -r ./requirements.dev.txt. Source installation requires a C++ compiler: gcc on Linux/macOS or Visual Studio 2017 Build Tools on Windows. If the package cannot be found, set PYTHONPATH=PATH-TO-MARO. No required credentials are documented.

How do you use this agent?

Import Env with from maro.simulator import Env, then create env = Env(scenario="cim", topology="toy.5p_ssddd_l0.0", start_tick=0, durations=100). Call metrics, decision_event, is_done = env.step(None) and continue calling env.step(None) while is_done is false; inspect env.metrics after the loop. For the documented playground, run docker pull maro2020/playground, then docker run -p 40009:40009 -p 40010:40010 maro2020/playground. The documented endpoints are Redis Commander at http://127.0.0.1:40009 and Jupyter Lab with MARO at http://127.0.0.1:40010.

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