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Bildungswerkzeuge für KI

  • Simplified PyTorch implementation of AlphaStar, enabling StarCraft II RL agent training with modular network architecture and self-play.
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    What is mini-AlphaStar?
    mini-AlphaStar demystifies the complex AlphaStar architecture by offering an accessible, open-source PyTorch framework for StarCraft II AI development. It features spatial feature encoders for screen and minimap inputs, non-spatial feature processing, LSTM memory modules, and separate policy and value networks for action selection and state evaluation. Using imitation learning to bootstrap and reinforcement learning with self-play for fine-tuning, it supports environment wrappers compatible with StarCraft II via pysc2, logging through TensorBoard, and configurable hyperparameters. Researchers and students can generate datasets from human gameplay, train models on custom scenarios, evaluate agent performance, and visualize learning curves. The modular codebase enables easy experimentation with network variants, training schedules, and multi-agent setups. Designed for education and prototyping rather than production deployment.
    mini-AlphaStar Core Features
    • Spatial and non-spatial feature encoding
    • LSTM-based memory modules
    • Separate policy and value networks
    • Imitation learning and reinforcement learning pipelines
    • Self-play environment wrappers via pysc2
    • TensorBoard logging and visualization
    • Configurable hyperparameters
    • Modular PyTorch codebase
  • An open-source AI agent combining Mistral-7B with Delphi for interactive moral and ethical question answering.
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    What is DelphiMistralAI?
    DelphiMistralAI is an open-source Python toolkit that integrates the powerful Mistral-7B LLM with the Delphi moral reasoning model. It offers both a command-line interface and a RESTful API for delivering reasoned ethical judgments on user-supplied scenarios. Users can deploy the agent locally, customize judgment criteria, and inspect generated rationales for each moral decision. This tool aims to accelerate AI ethics research, educational demonstrations, and safe, explainable decision support systems.
  • AIpacman is a Python framework providing search-based, adversarial, and reinforcement learning agents to master the Pac-Man game.
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    What is AIpacman?
    AIpacman is an open-source Python project that simulates the Pac-Man game environment for AI experimentation. Users can choose from built-in agents or implement custom ones using search algorithms like DFS, BFS, A*, UCS; adversarial methods such as Minimax with Alpha-Beta pruning and Expectimax; or reinforcement learning techniques like Q-Learning. The framework provides configurable mazes, performance logging, visualization of agent decision-making, and a command-line interface for running matches and comparing scores. It is designed to facilitate educational lessons, research benchmarks, and hobbyist projects in AI and game development.
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