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开源软件名称:mimoralea/applied-reinforcement-learning开源软件地址:https://github.com/mimoralea/applied-reinforcement-learning开源编程语言:Jupyter Notebook 98.2%开源软件介绍:Applied Reinforcement LearningI've been studying reinforcement learning and decision-making for a couple of years now. One of the most difficult things that I've encountered is not necessarily related to the concepts but how these concepts have been explained. To me, learning occurs when one is able to make a connection with the concepts being taught. For this, often an intuitive explanation is required, and likely a hands-on approach helps build that kind of understanding. My goal for this repository is to create, with the community, a resource that would help newcomers understand reinforcement learning in an intuitive way. Consider what you see here my initial attempt to teach some of these concepts as plain and simple as I can possibly explain them. If you'd like to collaborate, whether a typo, or an entire addition to the text, maybe a fix to a notebook or a whole new notebook, please feel free to send your issue and/or pull request to make things better. As long as your pull request aligns with the goal of the repository, it is very likely we will merge. I'm not the best teacher, or reinforcement learning researcher, but I do believe we can make reinforcement learning and decision-making easy for anyone to understand. Well, at least easier. Table of Contents
Notebooks InstallationThis repository contains Jupyter Notebooks to follow along with the lectures. However, there are several packages and applications that need to be installed. To make things easier on you, I took a little longer time to setup a reproducible environment that you can use to follow along. Install gitFollow the instructions at (https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) Install DockerFollow the instructions at (https://docs.docker.com/engine/getstarted/step_one/#step-2-install-docker) Run NotebooksTL;DR version
A little more detailed version:
Open the Notebooks in your browser:
Open TensorBoard at the following address:
This will help you visualize the Neural Network in the lessons with function approximation. Docker Tips
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