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  • Deep Research AI Agent

    An AI agent that automates web search, document retrieval, and advanced summarization for in-depth research reports.
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    What is Deep Research AI Agent?
    Deep Research AI Agent is an open-source Python framework designed for conducting comprehensive research tasks. It leverages integrated web search, PDF ingestion, and NLP pipelines to discover relevant sources, parse technical documents, and extract structured insights. The agent chains requests through LangChain and OpenAI, enabling context-aware question answering, automated citation formatting, and multi-document summarization. Researchers can adjust search scopes, filter by publication date or domain, and output reports in markdown or JSON. This tool minimizes manual literature review time and ensures consistent, high-quality summaries across diverse research domains.

  • 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.

  • Learning-to-Communicate-PyTorch

    A PyTorch framework enabling agents to learn emergent communication protocols in multi-agent reinforcement learning tasks.
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    What is Learning-to-Communicate-PyTorch?
    This repository implements emergent communication in multi-agent reinforcement learning using PyTorch. Users can configure sender and receiver neural networks to play referential games or cooperative navigation, encouraging agents to develop a discrete or continuous communication channel. It offers scripts for training, evaluation, and visualization of learned protocols, along with utilities for environment creation, message encoding, and decoding. Researchers can extend it with custom tasks, modify network architectures, and analyze protocol efficiency, fostering rapid experimentation in emergent agent communication.
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