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Algorithmus-Benchmarking

  • Emergent Communication in Agents

    Open-source PyTorch framework for multi-agent systems to learn and analyze emergent communication protocols in cooperative reinforcement learning tasks.
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    What is Emergent Communication in Agents?
    Emergent Communication in Agents is an open-source PyTorch framework designed for researchers exploring how multi-agent systems develop their own communication protocols. The library offers flexible implementations of cooperative reinforcement learning tasks, including referential games, combination games, and object identification challenges. Users define speaker and listener agent architectures, specify message channel properties like vocabulary size and sequence length, and select training strategies such as policy gradients or supervised learning. The framework includes end-to-end scripts for running experiments, analyzing communication efficiency, and visualizing emergent languages. Its modular design allows easy extension with new game environments or custom loss functions. Researchers can reproduce published studies, benchmark new algorithms, and probe compositionality and semantics of emergent agent languages.
    Emergent Communication in Agents Core Features
    • Implementations of referential and combination games
    • Configurable speaker-listener agent architectures
    • Customizable message channels (vocabulary, length)
    • Support for policy gradients and supervised learning
    • End-to-end training and evaluation scripts
    • Visualization tools for emergent languages
    • Modular design for adding new environments

  • 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.

  • 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.
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