Showing 4 open source projects for "framework-arduinoststm32"

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  • 1
    OpenSpiel

    OpenSpiel

    Environments and algorithms for research in general reinforcement

    OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and sequential, strictly turn-taking and simultaneous-move, perfect and imperfect information games, as well as traditional multiagent environments such as (partially- and fully- observable) grid worlds and social dilemmas. OpenSpiel also includes tools to...
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  • 2
    Lattice Visualiser

    Lattice Visualiser

    LV9: High-coherence C++ lattice explores reality's semantic limits.

    The Lattice Visualizer 9 provides a comprehensive computational platform for studying emergent cognition, successfully integrating heterogeneous physical, neurodynamic, and adaptive regulation mechanisms. The core strength of the LV9 framework lies in its intrinsic coupling: the physical structure is influenced by information flow, the information flow is regulated by the energetic state, and the energetic state is homeostatically tuned by the Self-Organized Criticality (SOC) feedback loop, ensuring the system consistently operates near its most computationally optimal point.
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  • 3
    TorchCraft

    TorchCraft

    Connecting Torch to StarCraft

    We present TorchCraft, a library that enables deep learning research on Real-Time Strategy (RTS) games such as StarCraft: Brood War, by making it easier to control these games from a machine learning framework, here Torch. This white paper argues for using RTS games as a benchmark for AI research, and describes the design and components of TorchCraft. TorchCraft is a BWAPI module that sends StarCraft data out over a ZMQ connection. This lets you parse StarCraft data and interact with BWAPI from anywhere. The TorchCraft client should be installed from C++, Python, or Lua. ...
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  • 4
    Parallel Reinforcement Evolutionary Artificial Neural Networks (PREANN) is a framework of flexible multi-layer ANN's with reinforcement learning based on genetic algorithms and a parallel implementation (using XMM registers and NVIDIA's CUDA).
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