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Comprehensive algorithm benchmarking Tools for Every Need

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

algorithm benchmarking

  • Cooperative Search Environment

    A Python-based multi-agent reinforcement learning environment for cooperative search tasks with configurable communication and rewards.
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    What is Cooperative Search Environment?
    Cooperative Search Environment provides a flexible, gym-compatible multi-agent reinforcement learning environment tailored for cooperative search tasks in both discrete grid and continuous spaces. Agents operate under partial observability and can share information based on customizable communication topologies. The framework supports predefined scenarios like search-and-rescue, dynamic target tracking, and collaborative mapping, with APIs to define custom environments and reward structures. It integrates seamlessly with popular RL libraries such as Stable Baselines3 and Ray RLlib, includes logging utilities for performance analysis, and offers built-in visualization tools for real-time monitoring. Researchers can adjust grid sizes, agent counts, sensor ranges, and reward sharing mechanisms to evaluate coordination strategies and benchmark new algorithms effectively.
    Cooperative Search Environment Core Features
    • Gym-compatible multi-agent environment
    • Configurable grid-based and continuous scenarios
    • Partial observability and customizable communication topologies
    • Customizable reward sharing mechanisms
    • Integration with Stable Baselines3 and Ray RLlib

  • Multi-Agent Miners

    An RL environment simulating multiple cooperative and competitive agent miners collecting resources in a grid-based world for multi-agent learning.
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    What is Multi-Agent Miners?
    Multi-Agent Miners offers a grid-world environment where multiple autonomous miner agents navigate, dig, and collect resources while interacting with each other. It supports configurable map sizes, agent counts, and reward structures, allowing users to create competitive or cooperative scenarios. The framework integrates with popular RL libraries via PettingZoo, providing standardized APIs for reset, step, and render functions. Visualization modes and logging support help analyze behaviors and outcomes, making it ideal for research, education, and algorithm benchmarking in multi-agent reinforcement learning.

  • AutoDRIVE Cooperative MARL

    An open-source multi-agent reinforcement learning framework for cooperative autonomous vehicle control in traffic scenarios.
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    What is AutoDRIVE Cooperative MARL?
    AutoDRIVE Cooperative MARL is an open-source framework designed to train and deploy cooperative multi-agent reinforcement learning (MARL) policies for autonomous driving tasks. It integrates with realistic simulators to model traffic scenarios like intersections, highway platooning, and merging. The framework implements centralized training with decentralized execution, enabling vehicles to learn shared policies that maximize overall traffic efficiency and safety. Users can configure environment parameters, choose from baseline MARL algorithms, visualize training progress, and benchmark agent coordination performance.
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