Hands-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

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