MARO

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MARO is an open-source platform providing a suite of customizable supply chain and logistics simulation environments. It enables researchers and engineers to develop, train, and evaluate multi-agent reinforcement learning algorithms at scale. With tools for tracking, visualization, and parallel execution, MARO accelerates end-to-end AI agent development for complex resource optimization tasks.
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MARO

MARO

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MARO
MARO is an open-source platform providing a suite of customizable supply chain and logistics simulation environments. It enables researchers and engineers to develop, train, and evaluate multi-agent reinforcement learning algorithms at scale. With tools for tracking, visualization, and parallel execution, MARO accelerates end-to-end AI agent development for complex resource optimization tasks.
Added on:
Social & Email:
Platform:
May 06 2025
--
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What is MARO?

MARO (Multi-Agent Resource Optimization) is a Python-based framework designed to support the development and evaluation of multi-agent reinforcement learning agents in supply chain, logistics, and resource management scenarios. It includes environment templates for inventory management, truck scheduling, cross-docking, container rental, and more. MARO offers a unified agent API, built-in trackers for experiment logging, parallel simulation capabilities for large-scale training, and visualization tools for performance analysis. The platform is modular, extensible and integrates with popular RL libraries, enabling reproducible research and rapid prototyping of AI-driven optimization solutions.

Who will use MARO?

  • Reinforcement learning researchers
  • Supply chain and logistics engineers
  • Data scientists
  • AI and ML practitioners
  • Academic institutions

How to use the MARO?

  • Step1: Install Python 3.7+ on your system
  • Step2: pip install maro
  • Step3: Import and configure a built-in supply chain environment
  • Step4: Initialize your multi-agent RL algorithm using MARO’s Agent API
  • Step5: Run training with parallel simulations enabled
  • Step6: Log and visualize metrics using MARO trackers
  • Step7: Evaluate trained agents and fine-tune hyperparameters

Platform

  • mac
  • windows
  • linux

MARO's Core Features & Benefits

The Core Features

  • Customizable supply chain and logistics environments
  • Unified multi-agent RL Agent API
  • Parallel simulation engine
  • Built-in experiment trackers
  • Visualization tools for performance analysis

The Benefits

  • Accelerates end-to-end AI agent development
  • Scalable large-scale simulations
  • Reproducible research workflows
  • Integration with popular RL libraries
  • Open-source and extensible architecture

MARO's Main Use Cases & Applications

  • Inventory replenishment optimization
  • Truck fleet scheduling
  • Cross-docking allocation
  • Dynamic container rental management
  • Energy distribution resource planning

FAQs of MARO

MARO Company Information

MARO Reviews

5/5
Do You Recommend MARO? Leave a Comment Below!

MARO's Main Competitors and alternatives?

  • OpenAI Gym
  • PettingZoo
  • RLlib
  • Unity ML-Agents
  • Stable Baselines3

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