Reinforcement Learning - Winder.AI Blog

Industrial insight and articles from Winder.AI, focusing on the topic Reinforcement Learning

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RL Presentation: Finding and Executing Reinforcement Learning Projects

RL Presentation: Finding and Executing Reinforcement Learning Projects

Tue Nov 22, 2022, by Phil Winder, in Reinforcement Learning, Talk

Learn about the framework that we use to help organizations find and develop problems that are best suited to reinforcement learning. This includes information about how to find business problems that are suited to RL, how to derisk the development process, and what technical tasks are involved.

ReFrame Part 2: How to Overcome Reinforcement Learning Challenges

ReFrame Part 2: How to Overcome Reinforcement Learning Challenges

Wed Dec 14, 2022, by Phil Winder, in Reinforcement Learning

When: Wed Dec 14, 2022 at 16:30 UTC Where: Linkedin Live Dr. Phil Winder shares experiences of Winder.AI’s reinforcement learning consulting experience at a variety of large and small organizations. This is a follow-up presentation to ReFrame: A Process for Finding and Executing Reinforcement Learning Projects and discusses some of the greatest challenges for a reinforcement learning project. I will share some of the challenges we have faced and how we have overcome them.

An Introduction to Reinforcement Learning: All You Need to Know

An Introduction to Reinforcement Learning: All You Need to Know

Thu Oct 13, 2022, by Phil Winder, in Reinforcement Learning

When a child wants to ride a bike, they learn by doing. This process of trial and error is also known as learning through reinforcement, because positive and negative experiences promote or discourage certain behaviours, respectively. Children learn to ride by avoiding actions that cause them to crash; for the thrill of feeling wind in their hair.

Optimising Industrial Processes with Reinforcement Learning

Optimising Industrial Processes with Reinforcement Learning

Tue Aug 9, 2022, by Winder.AI, in Case Study, Reinforcement Learning

Winder.AI helped CMPC, a large paper milling company, to optimise their production process by using reinforcement learning. CMPC are now able to automate industrial processes that were previously manual. This case study describes our approach and the results.

Reinforcement Learning Presentation: Cyber Security

Reinforcement Learning Presentation: Cyber Security

Wed Jan 19, 2022, by Phil Winder, in Reinforcement Learning, Talk

Dr. Phil Winder shares experiences of Winder.AI’s reinforcement learning consulting experience at a variety of large and small organizations. Abstract In this talk he focuses on RL applications, looking at the use of RL in cyber security and discusses one interesting case study about how Winder.AI helped an internal security team develop a tool to hack web application firewalls. About This Series Welcome to Winder.AI talks. A series of free interactive webinars hosted by Dr Phil Winder, CEO of Winder.

Using Reinforcement Learning to Attack Web Application Firewalls

Using Reinforcement Learning to Attack Web Application Firewalls

Fri Sep 3, 2021, by Phil Winder, in Reinforcement Learning, Case Study

Introduction Ideally, the best way to improve the security of any system is to detect all vulnerabilities and patch them. Unfortunately this is rarely possible due to the extreme complexity of modern systems. One primary threat are payloads arriving from the public internet, with the attacker using them to discover and exploit vulnerabilities. For this reason, web application firewalls (WAF) are introduced to detect suspicious behaviour. These are often rules based and when they detect nefarious activities they significantly reduce the overall damage.

Automating Cyber-Security with Reinforcement Learning

Automating Cyber-Security with Reinforcement Learning

Wed May 5, 2021, by Phil Winder, in Reinforcement Learning, Use Case

The best way to improve the security of any system is to detect all vulnerabilities and patch them. Unfortunately this is rarely possible due to the extreme complexity of modern systems. The common suggestion is to test for security, often leveraging the expertise of security-focussed engineers or automated scripts. But there are two fundamental issues with this approach: 1) security engineers do not scale, and 2) scripts are unlikely to cover all security concerns to begin with, let alone deal with new threats or increased attack surfaces.

CloudNativeX Interview: Reinforcement Learning

CloudNativeX Interview: Reinforcement Learning

Mon Apr 5, 2021, in Reinforcement Learning, Talk

Join Lee Razo and Phil Winder for this comprehensive introduction to Reinforcement Learning, an area of machine learning in which problems are tackled with intelligent agents which take actions to maximize a specified reward. Phil (quite literally) wrote the book on this topic and he takes us through the fundamentals of RL, some common use cases as well as tips on how even a small or mid-sized company can get started with and benefit from RL.

The Future of Transportation Infrastructure: Reinforcement Learning

The Future of Transportation Infrastructure: Reinforcement Learning

Wed Mar 17, 2021, by Phil Winder, in Reinforcement Learning, Use Case

The lock-downs endured during the coronavirus pandemic have given many the opportunity to work from home, potentially for the first time. Along with the guilt of failing at home-schooling, trying to work with noisy babies or animals, the lock-down has entirely changed the way in which we travel. When I speak to people about the pandemic, the lack of commute is one of the few positives they can take away from this experience and has led some to even question why they are paying for accommodation in some of the most expensive areas in the UK.

InfoQ Podcast: Phil Winder on the History, Practical Application, and Ethics of Reinforcement Learning

InfoQ Podcast: Phil Winder on the History, Practical Application, and Ethics of Reinforcement Learning

Mon Mar 1, 2021, in Reinforcement Learning, Talk

InfoQ · Phil Winder on the History, Practical Application, and Ethics of Reinforcement Learning Charles Humble, friend and editor of InfoQ, was kind enough to ask me for an interview to talk more about my new book, in podcast format. From the blurb: In this episode of the InfoQ podcast Dr Phil Winder, CEO of Winder.AI, sits down with InfoQ podcast co-host Charles Humble. They discuss: the history of Reinforcement Learning (RL); the application of RL in fields such as robotics and content discovery; scaling RL models and running them in production; and ethical considerations for RL.