Ultimate KI-Experimentierumgebung Solutions for Everyone

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KI-Experimentierumgebung

  • 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.
    AIpacman Core Features
    • Search-based agents: DFS, BFS, UCS, A*
    • Adversarial agents: Minimax, Alpha-Beta, Expectimax
    • Reinforcement learning: Q-Learning
    • Configurable maze layouts
    • Game visualization and rendering
    • Performance logging and metrics
    • CLI-driven execution
  • Open source playground to test LLMs.
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    What is nat.dev?
    OpenPlayground is an open-source platform that allows users to experiment with and compare different large language models (LLMs). It's designed to help users understand the strengths and weaknesses of various LLMs by providing a user-friendly and interactive environment. The platform can be particularly useful for developers, researchers, and anyone interested in the capabilities of artificial intelligence. Users can sign up easily using their Google account or email.
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