MARO Resource Optimization Platform
Simulate and optimize real-world resource decisions with reinforcement learning and distributed components.
- Source repo
- microsoft/maro
- Stars
- ★ 926
- Last updated
- 3y ago
- License
- MIT
- Primary language
- Python
- FA score
- 44/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platform
- Cost
- Free, no paid service needed
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- A logistics research team evaluating resource-balancing decisions in a container inventory management simulation.
- Not a fit if
- Teams wanting a no-build, zero-configuration install
- pip-only users who need the CLI and visualization tools
- Teams needing documented production auth, authorization, and failure recovery
- Source review
- 44/100 · Major gaps 7 safety controls not found
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.
- A logistics research team evaluating resource-balancing decisions in a container inventory management simulation.
- A transportation researcher modeling bike repositioning with the documented Citi Bike scenario.
- A data-center team exploring virtual-machine provisioning decisions in a simulated environment.
- A finance researcher framing asset management as a resource-optimization problem.
- An RL engineer running an
Envstep loop to collect environment metrics and decision events. - A team that wants a Docker-based trial environment with Jupyter Lab and Redis Commander.
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.
What are this agent's strengths and limitations?
- Combines simulation, RL abstractions, and distributed communication/job-orchestration components in one platform.
- Provides named resource-optimization scenarios for container inventory management, Citi Bike, and VM scheduling.
- Its documented
Envinterface returns metrics, decision events, and completion state in a clear stepwise execution model. - Supports snapshot dumping with
maro inspector dashboardand offers a Docker playground for hands-on evaluation.
- The PyPI
pymaropackage excludes CLI commands and visualization, requiring a source installation for those features. - Source installation requires a C++ compiler; Windows specifically calls for Visual Studio 2017 Build Tools.
- The documented Windows package path requires Torch 1.6.0 and torchvision 0.7.0 before installing MARO.
- The supplied material documents no native integration with ChatGPT, Codex, Claude, or model-provider APIs.
- No production authentication, authorization, cost model, or failure-recovery behavior is documented in the supplied material.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| MARO Resource Optimization Platform This agent | 44 · Major gaps | Library / SDKFree | ★ 926 | 3y ago | Python | — |
| Agent Lightning | 40 · Major gaps | CLIFree + model costs | ★ 19k | 1d ago | Python | OpenAI API |
| AgileRL | 71 · Some gaps | Library / SDKFreemium | ★ 965 | 1d ago | Python | — |
| Mava | 41 · Major gaps | CLIFree | ★ 941 | 1mo ago | Python | — |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows: repository is from Microsoft official organization, has clear MIT license and SECURITY.md, provides security vulnerability reporting channel. However, no specific implementation or documentation for least privilege, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback. Therefore, except source attribution, other trust criteria are not supported.
Evidence shows: README and docs provide clear structure, installation instructions and quick example are consistent, dependencies available on PyPI and Docker Hub. For failure messages, only exception raising in tests, no user-facing error handling documentation.
Evidence shows: README identifies target users (researchers, engineers) and application scenarios (CIM, Citi Bike, VM scheduling), provides multiple installation methods and examples. For trigger precision, no clear trigger conditions or event definitions, capability boundaries partially described in docs.
Evidence shows: README provides clear information architecture, detailed installation notes, stable naming (pymaro), examples and FAQ (via docs links), clear license (MIT), versioning and changelog (via GitHub releases), clear maintenance responsibility (Microsoft). But known limitations not explicitly listed.
Evidence shows: output usability via Python API and visualization tools, examples show usage. Marginal value via multiple scenarios and tools, but cost-benefit lacks performance benchmarks or resource consumption data.
Evidence shows: claims in README (e.g., support RL and OR) supported by related papers and docs, multiple sources (README, docs, CI) corroborate, facts and inferences distinguished in docs.
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: dependency securityPin versions and run a dependency audit (npm audit, pip-audit) before installing; prefer running it in a container.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- No specific implementation or documentation for least privilege, user confirmation, data flow transparency, etc., assess before use.
- Dependency security: no vulnerability scan results or security audit reports provided, check dependency versions.
- Known limitations not explicitly listed, may affect judgment of tool suitability.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
-
maro-0.3.2a4 maro-0.3.2a4 🚀
Release notes excerpt
- Add DDQN - Add prioritized sampling - Improve explore strategy - Fix minor issuesView on GitHub ↗ -
maro-0.3.2a3 maro-0.3.2a3 🚀
Release notes excerpt
- Fix DiscreteRLPolicy set_state bug (https://github.com/microsoft/maro/commit/a9a638134bef84176a0623fdadc5b789c2d7c3cf, https://github.com/microsoft/maro/commit/b3c6a589ad9036b03221e776a6929b2bc1eb4680)View on GitHub ↗ -
maro-0.3.2a1 maro-0.3.2a1 🚀
Release notes excerpt
- Refine RL workflow + Add **kwargs to support more problem setting (e.g., Graph based ones) (#589) - add **kwargs to RL models' forward funcs and _shape_check() - add **kwargs to RL policies' get_action related funcs and _post_check() - add **kwargs to choose_actions of AbsEnvSampler; remain it None in current sample() and eval() + Add detached loss to the return value of update_critic() and update_actor() of current TrainOps; add default False early_stop to update_actor() of current TrainOps (#589) …View on GitHub ↗
FAQ
Does the PyPI package include the inspector CLI?
pymaro package does not include CLI commands, including visualization tools; install from source to use them.Are API keys or cloud credentials required?
What does the first environment run return?
Env.step(None) returns metrics, decision_event, and is_done; final environment metrics are available through env.metrics.Are there extra Windows requirements?
Is there a quick Docker trial?
maro2020/playground image exposes Redis Commander on port 40009 and Jupyter Lab with MARO on port 40010.