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Comprehensive experimental reproducibility Tools for Every Need

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

experimental reproducibility

  • MARTI

    MARTI is an open-source toolkit offering standardized environments and benchmarking tools for multi-agent reinforcement learning experiments.
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    What is MARTI?
    MARTI (Multi-Agent Reinforcement learning Toolkit and Interface) is a research-oriented framework that streamlines the development, evaluation, and benchmarking of multi-agent RL algorithms. It offers a plug-and-play architecture where users can configure custom environments, agent policies, reward structures, and communication protocols. MARTI integrates with popular deep learning libraries, supports GPU acceleration and distributed training, and generates detailed logs and visualizations for performance analysis. The toolkit’s modular design allows rapid prototyping of novel approaches and systematic comparison against standard baselines, making it ideal for academic research and pilot projects in autonomous systems, robotics, game AI, and cooperative multi-agent scenarios.

  • 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.

  • 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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