• Full-stack observability with actually useful AI | Grafana Cloud Icon
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  • 1
    SLM Lab

    SLM Lab

    Modular Deep Reinforcement Learning framework in PyTorch

    ...It provides implementations of various state-of-the-art RL algorithms and emphasizes reproducibility, scalability, and detailed experiment tracking. SLM Lab is structured around a flexible experiment management system, allowing users to define, run, and analyze RL experiments efficiently.
    Downloads: 0 This Week
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  • 2
    Habitat-Lab

    Habitat-Lab

    A modular high-level library to train embodied AI agents

    ...Allowing users to train agents in a wide variety of single and multi-agent tasks (e.g. navigation, rearrangement, instruction following, question answering, human following), as well as define novel tasks. Configuring and instantiating a diverse set of embodied agents, including commercial robots and humanoids, specifying their sensors and capabilities. Providing algorithms for single and multi-agent training (via imitation or reinforcement learning, or no learning at all as in SensePlanAct pipelines), as well as tools to benchmark their performance on the defined tasks using standard metrics.
    Downloads: 0 This Week
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  • 3
    RecNN

    RecNN

    Reinforced Recommendation toolkit built around pytorch 1.7

    This is my school project. It focuses on Reinforcement Learning for personalized news recommendation. The main distinction is that it tries to solve online off-policy learning with dynamically generated item embeddings. I want to create a library with SOTA algorithms for reinforcement learning recommendation, providing the level of abstraction you like.
    Downloads: 0 This Week
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  • 4
    Deep Reinforcement Learning for Keras

    Deep Reinforcement Learning for Keras

    Deep Reinforcement Learning for Keras.

    ...This means that evaluating and playing around with different algorithms is easy. Of course, you can extend keras-rl according to your own needs. You can use built-in Keras callbacks and metrics or define your own. Even more so, it is easy to implement your own environments and even algorithms by simply extending some simple abstract classes. Documentation is available online.
    Downloads: 0 This Week
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  • 5
    ConvNetJS

    ConvNetJS

    Deep learning in Javascript to train convolutional neural networks

    ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat. ConvNetJS is an implementation of Neural networks, together with nice browser-based demos. It currently supports common Neural Network modules (fully connected layers, non-linearities), classification (SVM/Softmax) and Regression (L2) cost functions, ability to specify and...
    Downloads: 0 This Week
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