Rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch

栏目: IT技术 · 发布时间: 4年前

rlpyt includes modular, optimized implementations of common deep RL algorithms in PyTorch, with unified infrastructure supporting all three major families of model-free algorithms: policy gradient, deep-q learning, and q-function policy gradient. It is intended to be a high-throughput code-base for small- to medium-scale research (large-scale meaning like OpenAI Dota with 100’s GPUs). A conceptual overview is provided in the white paper , and the code (with examples) in the github repository .

This documentation aims to explain the intent of the code structure, to make it easier to use and modify (it might not detail every keyword argument as in a fixed library). See the github README for installation instructions and other introductory notes. Please share any questions or comments to do with documenantation on the github issues.

The sections are organized as follows. First, several of the base classes are introduced. Then, each algorithm family and associated agents and models are grouped together. Infrastructure code such as the runner classes and sampler classes are covered next. All the remaining components are covered thereafter, in no particular order.


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性能之巅

性能之巅

Brendan Gregg / 徐章宁、吴寒思、陈磊 / 电子工业出版社 / 2015-8-15 / 128

《性能之巅:洞悉系统、企业与云计算》基于Linux 和Solaris 系统阐述了适用于所有系统的性能理论和方法,Brendan Gregg 将业界普遍承认的性能方法、工具和指标收集于本书之中。阅读本书,你能洞悉系统运作的方式,学习到分析和提高系统与应用程序性能的方法,这些性能方法同样适用于大型企业与云计算这类最为复杂的环境的性能分析与调优。一起来看看 《性能之巅》 这本书的介绍吧!

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