Hands-on Reinforcement Learning with PyTorch [Video]

By Colibri Ltd
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  1. First Steps in Pytorch Reinforcement Learning

About this video

PyTorch, Facebook's deep learning framework, is clear, easy to code and easy to debug, thus providing a straightforward and simple experience for developers.

This course is your hands-on guide to the core concepts of deep reinforcement learning and its implementation in PyTorch. We will help you get your PyTorch environment ready before moving on to the core concepts that encompass deep reinforcement learning.

Following a practical approach, you will build reinforcement learning algorithms and develop/train agents in simulated OpenAI Gym environments. You'll learn the skills you need to implement deep reinforcement learning concepts so you can get started building smart systems that learn from their own experiences.

By the end of this course, you will have enhanced your knowledge of deep reinforcement learning algorithms and will be confident enough to effectively use PyTorch to build your RL projects.

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

Publication date:
September 2019
3 hours 6 minutes

About the Author

  • Colibri Ltd

    Colibri Digital is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as big data, data science, Machine Learning, and cloud computing. Over the past few years, they have worked with some of the World's largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the World's most popular soft drinks companies, helping each of them to better make sense of its data, and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence -> Action. Jim DiLorenzo is a freelance programmer and reinforcement learning enthusiast. He graduated from Columbia University and is working on his Masters in Computer Science. He has implemented many RL algorithms and variants from papers into code with PyTorch and written extensions for popular RL libraries for his experiments.

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