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  • 1
    OSWorld

    OSWorld

    Benchmarking Multimodal Agents for Open-Ended Tasks

    ...It provides a richly simulated 3D world where multiple agents can interact, perform tasks, and learn complex behaviors. OSWorld emphasizes multi-modal interaction, enabling agents to process visual, auditory, and symbolic data for grounded learning in a simulated world.
    Downloads: 2 This Week
    Last Update:
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  • 2
    AgentUniverse

    AgentUniverse

    agentUniverse is a LLM multi-agent framework

    AgentUniverse is a multi-agent AI framework that enables coordination between multiple intelligent agents for complex task execution and automation.
    Downloads: 1 This Week
    Last Update:
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  • 3

    Astrape

    Optical-packet node transceiver frequency allocation

    In an optical network scenario which consists of multiple nodes (whiteboxes) at its edges and ROADMs in-between, the coherent transceiver average laser configuration time is improved. The process is evaluated according to a testbed setup. This is facilitated in the appropriate lab equipment (or via simulation when required). For that purpose, a software agent (Netconf server) residing at the whiteboxes, is developed receiving input from the Software-Defined Networking (SDN) packet controller (PacketCTL - a Netconf client). ...
    Downloads: 0 This Week
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  • 4
    Coach

    Coach

    Enables easy experimentation with state of the art algorithms

    ...It exposes a set of easy-to-use APIs for experimenting with new RL algorithms and allows simple integration of new environments to solve. Coach collects statistics from the training process and supports advanced visualization techniques for debugging the agent being trained. Coach supports many state-of-the-art reinforcement learning algorithms, which are separated into three main classes - value optimization, policy optimization, and imitation learning. Coach supports a large number of environments which can be solved using reinforcement learning.
    Downloads: 0 This Week
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  • 5
    ConvNetJS

    ConvNetJS

    Deep learning in Javascript to train convolutional neural networks

    ...It currently supports common Neural Network modules (fully connected layers, non-linearities), classification (SVM/Softmax) and Regression (L2) cost functions, ability to specify and train Convolutional Networks that process images, and experimental Reinforcement Learning modules, based on Deep Q Learning. The library allows you to formulate and solve Neural Networks in Javascript. If you would like to add features to the library, you will have to change the code in src/ and then compile the library into the build/ directory. The compilation script simply concatenates files in src/ and then minifies the result.
    Downloads: 0 This Week
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  • 6

    iget

    Adaptive Parallel Data Retrieval Mechanism

    ...A data retrieval mechanism that can adapt to the continuous contraction and expansion of the network bottleneck so that an optimal concurrency index can be maintained at any time during the data retrieval process. Sample usage: python iget.py <target url> <output file>
    Downloads: 0 This Week
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