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Comprehensive integración con TensorBoard Tools for Every Need

Get access to integración con TensorBoard solutions that address multiple requirements. One-stop resources for streamlined workflows.

integración con TensorBoard

  • Mava

    Mava is an open-source multi-agent reinforcement learning framework by InstaDeep, offering modular training and distributed support.
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    What is Mava?
    Mava is a JAX-based open-source library for developing, training, and evaluating multi-agent reinforcement learning systems. It offers pre-built implementations of cooperative and competitive algorithms such as MAPPO and MADDPG, along with configurable training loops that support single-node and distributed workflows. Researchers can import environments from PettingZoo or define custom environments, then use Mava’s modular components for policy optimization, replay buffer management, and metric logging. The framework’s flexible architecture allows seamless integration of new algorithms, custom observation spaces, and reward structures. By leveraging JAX’s auto-vectorization and hardware acceleration capabilities, Mava ensures efficient large-scale experiments and reproducible benchmarking across various multi-agent scenarios.
    Mava Core Features
    • Open-source JAX-based multi-agent RL algorithms
    • Modular training and evaluation pipelines
    • Support for PettingZoo and custom environments
    • Distributed training across multiple devices
    • Integrated logging and visualization with TensorBoard

  • 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.

  • RL Shooter

    RL Shooter provides a customizable Doom-based reinforcement learning environment for training AI agents to navigate and shoot targets.
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    What is RL Shooter?
    RL Shooter is a Python-based framework that integrates ViZDoom with OpenAI Gym APIs to create a flexible reinforcement learning environment for FPS games. Users can define custom scenarios, maps, and reward structures to train agents on navigation, target detection, and shooting tasks. With configurable observation frames, action spaces, and logging facilities, it supports popular deep RL libraries such as Stable Baselines and RLlib, enabling clear performance tracking and reproducibility across experiments.
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