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  • PommerLearn

    An RL framework offering PPO, DQN training and evaluation tools for developing competitive Pommerman game agents.
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    What is PommerLearn?
    PommerLearn enables researchers and developers to train multi-agent RL bots in the Pommerman game environment. It includes ready-to-use implementations of popular algorithms (PPO, DQN), flexible configuration files for hyperparameters, automatic logging and visualization of training metrics, model checkpointing, and evaluation scripts. Its modular architecture makes it easy to extend with new algorithms, customize environments, and integrate with standard ML libraries such as PyTorch.
    PommerLearn Core Features
    • PPO algorithm implementation
    • DQN algorithm implementation
    • Pommerman environment wrappers
    • Configurable hyperparameters
    • Logging and TensorBoard integration
    • Model checkpointing and saving
    • Evaluation scripts

  • AI Agent Debate Autogen Tutorial

    A hands-on tutorial demonstrating how to orchestrate debate-style AI agents using LangChain AutoGen in Python.
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    What is AI Agent Debate Autogen Tutorial?
    The AI Agent Debate Autogen Tutorial provides a step-by-step framework for orchestrating multiple AI agents engaged in structured debates. It leverages LangChain’s AutoGen module to coordinate messaging, tool execution, and debate resolution. Users can customize templates, configure debate parameters, and view detailed logs and summaries of each round. Ideal for researchers evaluating model opinions or educators demonstrating AI collaboration, this tutorial delivers reusable code components for end-to-end debate orchestration in Python.
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