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.
AI Platforms & Frameworks
#multi-agent reinforcement learning
#Supply Chain Optimization
#Logistics Management
#AI-driven Solutions
#Python Framework
#parallel simulations
#inventory management systems
#Open-source AI
#experiment tracking
#performance visualization
#container rental optimization
#truck scheduling
#agent API
#reproducible research
#Scalable Training
#Resource Management
#simulation environment
#modular architecture
#large-scale testing
#integrated RL libraries
...
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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.
AI Platforms & Frameworks
#multi-agent reinforcement learning
#Supply Chain Optimization
#Logistics Management
#AI-driven Solutions
#Python Framework
#parallel simulations
#inventory management systems
#Open-source AI
#experiment tracking
#performance visualization
#container rental optimization
#truck scheduling
#agent API
#reproducible research
#Scalable Training
#Resource Management
#simulation environment
#modular architecture
#large-scale testing
#integrated RL libraries
...
Featured
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
- Microsoft
MARO Reviews
5/5
MARO's Main Competitors and alternatives?
- OpenAI Gym
- PettingZoo
- RLlib
- Unity ML-Agents
- Stable Baselines3
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