Reinforcement Learning - Winder.AI Blog

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

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

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

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

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.

Solving Three Common Manufacturing Problems with Reinforcement Learning

Thu Feb 18, 2021, by Phil Winder, in Reinforcement Learning, Use Case

Like many industries, manufacturing is experiencing an explosion in both the growth of and access to data. The data is complex and multi-faceted, for example the data may originate from the production line, the environment, through usage, or even from users. When viewed in this light, the explosion is often called “big data” and the effect called smart manufacturing (USA) or industrie 4.0 (Germany). The data must be acted upon to be useful.

Inventory Control and Supply Chain Optimization with Reinforcement Learning

Sun Feb 7, 2021, by Phil Winder, in Reinforcement Learning, Use Case

Inventory control is the problem of attempting to optimize product or stock levels given the unique constraints and requirements of a business. It is an important problem because every goods-based business has to spend resources on maintaining stock levels so that they can deliver products that customers want. Every improvement to inventory control has a direct improvement the delivery of the business. Beginners study tactics, experts study logistics, so they say.

DataTalksClub - Industrial Applications of Reinforcement Learning

Sun Feb 7, 2021, in Reinforcement Learning, Talk

Reinforcement learning (RL), a sub-discipline of machine learning, has been gaining academic and media notoriety after hyped marketing “reveals” of agents playing various games. But these hide the fact that RL is immensely useful in many practical, industrial situations where hand-coding strategies or policies would be impractical or sub-optimal. Following the theme of my new book (https://rl-book.com​), I present a rebuttal to the hyperbole by analysing five different industrial case studies from a variety of sectors.

GOTO Book Club: How to Leverage Reinforcement Learning

Thu Feb 4, 2021, in Reinforcement Learning, Talk

In this episode of GOTO’s book club I speak to Rebecca Nugent, Feinberg professor of statistics and data science at Carnegie Mellon univeristy. We talk, at length, about the application of reinforcment learning, specifically how it could be a way of creating truly personalised teaching curricula. It’s a really interesting discussion and it’s great to get someone of Rebecca’s calibre to bounce ideas off.

A Code-Driven Introduction to Reinforcement Learning

Wed Nov 11, 2020, in Reinforcement Learning, Talk

Notebook link Abstract Reinforcement learning (RL) is lined up to become the hottest new artificial intelligence paradigm in the next few years. Building upon machine learning, reinforcement learning has the potential to automate strategic-level thinking in industry. In this presentation I present a code-driven introduction to RL, where you will explore a fundamental framework called the Markov decision process (MDP) and learn how to build an RL algorithm to solve it.