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Comprehensive パフォーマンス可視化 Tools for Every Need

Get access to パフォーマンス可視化 solutions that address multiple requirements. One-stop resources for streamlined workflows.

パフォーマンス可視化

  • CrewAI-Learning

    CrewAI-Learning enables collaborative multi-agent reinforcement learning with customizable environments and built-in training utilities.
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    What is CrewAI-Learning?
    CrewAI-Learning is an open-source library designed to streamline multi-agent reinforcement learning projects. It offers environment scaffolding, modular agent definitions, customizable reward functions, and a suite of built-in algorithms such as DQN, PPO, and A3C adapted for collaborative tasks. Users can define scenarios, manage training loops, log metrics, and visualize results. The framework supports dynamic configuration of agent teams and reward sharing strategies, making it easy to prototype, evaluate, and optimize cooperative AI solutions across various domains.
    CrewAI-Learning Core Features
    • Multi-agent environment scaffolding
    • Modular agent and policy definitions
    • Customizable reward sharing mechanisms
    • Built-in RL algorithms (DQN, PPO, A3C)
    • Scenario templating and dynamic configs
    • Training loop management and callbacks
    • Performance logging and visualization

  • MARO

    A multi-agent reinforcement learning platform offering customizable supply chain simulation environments to train and evaluate AI agents effectively.
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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.
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