Showing 4 open source projects for "android game unity"

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    MercuryMessaging

    MercuryMessaging

    Framework to facilitate communication in the Unity game engine

    The Mercury messaging toolkit is a new way to handle cross-component communication in the Unity game engine. It integrates seamlessly with the Unity Editor, and is both robust and expandable. It has been tested in Unity 2020 up until 2020.3.21f1, Unity 2019 up until 2019.2.17f1, Unity 2018 up until 2018.3.13f1, Unity 2017 up until 2017.4f1, and 5.6. The toolkit contains the Mercury messaging framework, which is a messaging and organizational framework built around the Mercury Protocol. ...
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  • 2
    AI4U

    AI4U

    Multi-engine plugin to specify agents with reinforcement learning

    AI4U is a multi-engine plugin (Godot and Unity) that allows you to design Non-Player Characters (NPCs) of games using an agent abstraction. In addition, AI4U has a low-level API that allows you to connect the agent to any algorithm made available in Python by the reinforcement learning community specifically and by the Artificial Intelligence community in general. Reinforcement learning promises to overcome traditional navigation mesh mechanisms in games and to provide more autonomous...
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  • 3
    UXF

    UXF

    Framework for creating human behaviour experiments in Unity

    The Unity Experiment Framework (UXF) is an open-source package for the Unity3D game engine that can be used to assist the development of virtual reality human behavior experiments. Developed by Jack Brookes, Immersive Cognition Group, University of Leeds. Automates the process of collecting the variables you define within your task, and stores them simple CSV files.
    Downloads: 2 This Week
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  • 4
    NatML

    NatML

    High performance, cross-platform machine learning for Unity Engine

    NatML allows developers to integrate machine learning into their Unity applications in under five lines of code with zero infrastructure. NatML completely removes the need to have any experience with machine learning in order to take advantage of the features it can provide. NatML takes advantage of hardware machine learning accelerators, like CoreML on iOS and macOS, NNAPI on Android, and DirectML on Windows.
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