Adaptive Synchronous-Retrieval mechanism with Concurrent I/O using Reinforcement Learning.
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>
This project provides a framework for testing and comparing different machine learning algorithms (particularly reinforcement learning methods) in different scenarios. Its intended area of application is in research and education.
A Python class library of tools for learning agents, including reinforcement learning algorithms, function approximators, and vector quantizations algorithms. (Pronounced "plastic".)
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General purpose agents using reinforcement learning. Combines radial basis functions, temporal difference learning, planning, uncertainty estimations, and curiosity. Intended to be an out-of-the-box solution for roboticists and game developers.