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Comprehensive entraînement évolutif Tools for Every Need

Get access to entraînement évolutif solutions that address multiple requirements. One-stop resources for streamlined workflows.

entraînement évolutif

  • RxAgent-Zoo

    RxAgent-Zoo uses reactive programming with RxPY to streamline development and experimentation of modular reinforcement learning agents.
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    What is RxAgent-Zoo?
    At its core, RxAgent-Zoo is a reactive RL framework that treats data events from environments, replay buffers, and training loops as observable streams. Users can chain operators to preprocess observations, update networks, and log metrics asynchronously. The library offers parallel environment support, configurable schedulers, and integration with popular Gym and Atari benchmarks. A plug-and-play API allows seamless swapping of agent components, facilitating reproducible research, rapid experimentation, and scalable training workflows.
    RxAgent-Zoo Core Features
    • Reactive RL pipelines with RxPY
    • Pre-implemented agents: DQN, PPO, A2C, DDPG
    • Parallel environment execution
    • Asynchronous data stream management
    • Built-in logging and monitoring

  • 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.

  • VMAS

    VMAS is a modular MARL framework that enables GPU-accelerated multi-agent environment simulation and training with built-in algorithms.
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    What is VMAS?
    VMAS is a comprehensive toolkit for building and training multi-agent systems using deep reinforcement learning. It supports GPU-based parallel simulation of hundreds of environment instances, enabling high-throughput data collection and scalable training. VMAS includes implementations of popular MARL algorithms like PPO, MADDPG, QMIX, and COMA, along with modular policy and environment interfaces for rapid prototyping. The framework facilitates centralized training with decentralized execution (CTDE), offers customizable reward shaping, observation spaces, and callback hooks for logging and visualization. With its modular design, VMAS seamlessly integrates with PyTorch models and external environments, making it ideal for research in cooperative, competitive, and mixed-motive tasks across robotics, traffic control, resource allocation, and game AI scenarios.
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