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HMAS

HMAS

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HMAS
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What is HMAS?

HMAS is an open-source Python framework that enables development of hierarchical multi-agent systems. It offers abstractions for defining agent hierarchies, inter-agent communication protocols, environment integration, and built-in training loops. Researchers and developers can use HMAS to prototype complex multi-agent interactions, train coordinated policies, and evaluate performance in simulated environments. Its modular design makes it easy to extend and customize agents, environments, and training strategies.

Who will use HMAS?

  • Multi-agent RL researchers
  • AI developers
  • Robotics engineers
  • Academic institutions

How to use the HMAS?

  • Step1: Install via pip install hmas
  • Step2: Import HMAS modules in Python
  • Step3: Define hierarchical agent classes and communication channels
  • Step4: Configure environment wrappers and training parameters
  • Step5: Run training loop with hmas.train()
  • Step6: Evaluate trained policies with hmas.evaluate()

Platform

  • mac
  • windows
  • linux

HMAS's Core Features & Benefits

The Core Features

  • Hierarchical agent architecture
  • Inter-agent communication protocols
  • Environment wrapper interfaces
  • Built-in training and evaluation loops
  • Modular extension support

The Benefits

  • Modular and scalable agent design
  • Streamlined policy training
  • Easy environment integration
  • Customizable communication patterns
  • Rapid prototyping for multi-agent research

HMAS's Main Use Cases & Applications

  • Coordinated control in swarm robotics
  • Multi-agent reinforcement learning research
  • Game AI with hierarchical team strategies
  • Traffic flow optimization simulations

FAQs of HMAS

HMAS Company Information

HMAS Reviews

5/5
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HMAS's Main Competitors and alternatives?

  • PettingZoo
  • Ray RLlib MultiAgent
  • OpenAI Multi-Agent Particle Environment
  • SMARTS

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