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Comprehensive визуализация кривых обучения Tools for Every Need

Get access to визуализация кривых обучения solutions that address multiple requirements. One-stop resources for streamlined workflows.

визуализация кривых обучения

  • Multi-Agent Reinforcement Learning

    An open-source framework for training and evaluating cooperative and competitive multi-agent reinforcement learning algorithms across diverse environments.
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    What is Multi-Agent Reinforcement Learning?
    Multi-Agent Reinforcement Learning by alaamoheb is a comprehensive open-source library designed to facilitate the development, training, and evaluation of multiple agents acting in shared environments. It includes modular implementations of value-based and policy-based algorithms such as DQN, PPO, MADDPG, and more. The repository supports integration with OpenAI Gym, Unity ML-Agents, and the StarCraft Multi-Agent Challenge, allowing users to experiment in both research and real-world inspired scenarios. With configurable YAML-based experiment setups, logging utilities, and visualization tools, practitioners can monitor learning curves, tune hyperparameters, and compare different algorithms. This framework accelerates experimentation in cooperative, competitive, and mixed multi-agent tasks, streamlining reproducible research and benchmarking.

  • Scalable MADDPG

    Scalable MADDPG is an open-source multi-agent reinforcement learning framework implementing deep deterministic policy gradient for multiple agents.
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    What is Scalable MADDPG?
    Scalable MADDPG is a research-oriented framework for multi-agent reinforcement learning, offering a scalable implementation of the MADDPG algorithm. It features centralized critics during training and independent actors at runtime for stability and efficiency. The library includes Python scripts to define custom environments, configure network architectures, and adjust hyperparameters. Users can train multiple agents in parallel, monitor metrics, and visualize learning curves. It integrates with OpenAI Gym-like environments and supports GPU acceleration via TensorFlow. By providing modular components, Scalable MADDPG enables flexible experimentation on cooperative, competitive, or mixed multi-agent tasks, facilitating rapid prototyping and benchmarking.
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