Introduction to Deep Reinforcement Learning


This tutorial is all about deep reinforcement learning. You might have heard about it in the media, from its use in generative language models (reinforcement learning from human feedback) or more directly in one of the many applications of this fascinating technology.

The goal of this tutorial is to give you a hands-on-ish walkthrough of what reinforcement learning is, why we need it to be deep, and how it's used in practice.

You will learn the background theory, explore use cases, and have fun with a notebook that provides a practical example of what we're talking about.

They'll also be an opportunity for you to ask questions to find out more how we at Winder.AI are using RL in commercial projects.

Session Outline:

Part 1: Introduction to Reinforcement Learning
Familiarise yourself with RL, the theory, how it is both similar and different to machine learning, and how it is used in practice.

Part 2: Why Deep? And Why Now?
Learn about ""deep"" RL and why it is necessary. Investigate cutting-edge use cases. Learn about a process we've developed to help discover and develop RL problems.

Part 3: Hands-on with Deep RL
Learn about the common libraries and frameworks to help deliver production RL solutions. Explore a notebook that demonstrates a practical example of deep RL.

Learning objectives:
* Understand what RL is and how it differs from ML
* Appreciate why and when you should use RL
* Evaluate the need for deep techniques
* Explore the ecosystem of tools and a simple practical example of how to use them


Dr. Phil Winder is a multidisciplinary engineer and data scientist. As the CEO of Winder.AI, an AI consultancy, he provides AI, ML, Data Science, and MLOps development and consulting services to businesses of all sizes. Previous clients include the likes of Google, Microsoft, Shell, Nestle, the UK Government and many more. More information is available on the website: https://Winder.AI.

Phil is also the author of the book “Reinforcement Learning: Industrial Applications of Intelligent Agents” published by O’Reilly ( and was an early champion of MLOps. Over the past decade, he has also trained thousands of data scientists and is a celebrated global speaker on AI topics.

Phil holds a Ph.D. and M.Eng. in electronic engineering from the University of Hull and lives in Yorkshire, U.K., with his brewing equipment and family.

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