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Hands-on Reinforcement Learning with TensorFlow

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Hands-on Reinforcement Learning with TensorFlowHands-on Reinforcement Learning with TensorFlow
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 3 Hours 42M | 796 MB
Genre: eLearning | Language: English

You’ve probably heard of Deepmind’s AI playing games and getting really good at playing them (like AlphaGo beating the Go world champion). Such agents are built with the help of a paradigm of machine learning called “Reinforcement Learning” (RL).

In this course, you’ll walk through different approaches to RL. You’ll move from a simple Q-learning to a more complex, deep RL architecture and implement your algorithms using Tensorflow’s Python API. You’ll be training your agents on two different games in a number of complex scenarios to make them more intelligent and perceptive.
By the end of this course, you’ll be able to implement RL-based solutions in your projects from scratch using Tensorflow and Python.

The code bundle for this video course is available at: https://github.com/PacktPublishing/-Hands-on-Reinforcement-Learning-with-TensorFlow

Hands-on Reinforcement Learning with TensorFlow

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  1. TensorFlow强化学习实践教程 你可能已经听过Deepmind的 AI玩游戏并玩得还不错。这样的代理是通过被称作强化学习的机器学习范例所开发的。 在本教程中,你将会学习不同的RL方法。你将会从简单的Q学习到一个更复杂、深入的RL架构并将你的算法使用Tensorflow的Python API部署。你将会在两个不同的游戏中使用大量复杂的场景训练你的代理,令他们更加智能和更具有认知。 学习完本教程,你将能够在你的项目中使用Tensorflow和Python部署基于RL的解决方案。
    wilde(特殊组-翻译)3个月前 (09-01)