Showing 4 open source projects for "ai agents"

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  • 1
    Unity ML-Agents Toolkit

    Unity ML-Agents Toolkit

    Unity machine learning agents toolkit

    ...Using ML-Agents allows developers to create more compelling gameplay and an enhanced game experience. Advancement of artificial intelligence (AI) research depends on figuring out tough problems in existing environments using current benchmarks for training AI models. Using Unity and the ML-Agents toolkit, you can create AI environments that are physically, visually, and cognitively rich.
    Downloads: 9 This Week
    Last Update:
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  • 2
    dotRL

    dotRL

    A platform for rapid Reinforcement Learning methods development

    Application allowing convenient experimentation in Reinforcement Learning - a Machine Learning domain. Project goals are: - keep adding new environments and agents as simple as possible - provide a rich set of state-of-art algorithms and problems - integrate with other existing Reinforcement Learning platforms If you found this application useful please cite this work: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6643987
    Downloads: 0 This Week
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  • 3
    neural network designer

    neural network designer

    a dbms for neural nets. Chatbots, DTrees, random forests, n-grams,...

    ...As such, the library is optimized for handling any type of data-size by using advanced streaming and caching algorithms. With the designer, you are able to create different types of decision trees, random forests, n-grams, pattern-matchers, conversational agents and all sorts of AI related algorithms. You can combine statistical approaches as well as pattern matchers or others. Do natural language processing, image or data analysis & interpretation,...
    Downloads: 0 This Week
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  • 4
    HoldemAI

    HoldemAI

    Texas Holdem Poker AI

    Full ring Texas Hold'em poker game built around an intelligent AI system. The AI uses players' betting actions to calculate a probability distribution of their hole cards and uses it to evaluate hand strength and the best possible action. Small random changes are made to mimic human behavior and make the AI less predictable. Future versions will include adaptive opponent modeling using neural networks to improve the AI's strength. The AI code can be easily adapted for input from screen scrapers.
    Downloads: 0 This Week
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