Revolutionary Reinforcement Learning Services

Discover how reinforcement learning is changing the way organizations do data science, with the world leaders, Winder Research.

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What is RL?

Reinforcement learning automates strategic decisions that happen over time.

What Is Reinforcement Learning?

As we describe in our book, reinforcement learning (RL) is a sub-discipline of machine learning (ML) that specializes in teaching machines to execute multi-step, strategic decisions.

Traditional ML automates single decisions. But these decisions don’t have any context, nor do they operate over sequences. For example, a traditional recommendations algorithm recommends a single set of products and the algorithms are optimized to improve that single recommendation. But this is the wrong objective. You don’t want to optimize for single placements. You should be optimizing for increased engagement or higher profitability per customer, or whatever your business prioritizes.

RL allows you to train your models to do exactly that; optimize decisions over a period of time towards your organizations unique goal.

What is Reinforcement Learning Not?

RL is Not a Panacea

RL is very good at solving multi-step, sequential decision making problems. For example, YouTube were able to improve video recommendation performance, at the same time as reducing the number of data scientists required to implement such a solution. But industry is awash with “low-hanging fruit” that is best served with simple cloud native or ML solutions.

RL is More Than Games

The vast majority of examples of RL you can find on the internet are based upon OpenAI’s gym. This comes with many pre-baked RL examples, but unfortunately they are focussed towards academia. There are very few examples of industrial problems (except for robotics). But these applications exist. Take a look at our companion site to see many examples of the use of RL in business problems.

RL is Not Artificial Intelligence

Artificial intelligence (AI) is an academic discipline interested in developing algorithms that produce human-like behavior. Although RL lies at the heart of much of this research, it cannot solve all problems for all organizations in an instant. It still requires thorough research and engineering.

How Does RL Help?

Reinforcement learning (RL) helps businesses solve strategic decision making problems and are easily tied towards core business metrics.

RL is often described in terms of the domain. But in our experience developing RL solutions for organizations like Nestle and CMPC, given the right situation, there are a number of generic benefits that only RL can provide:

  • Optimizes the right thing: RL algorithms are directly tied to a business metrics via the reward function.
  • Uses context: The right decision now may not be the best decision in the future. RL can learn that subsequent actions may be different to those initially taken.
  • Learn how, not what: ML typically learns from discrete results; it doesn’t learn how to get there. RL learns how experts achieve a result by learning optimal strategies.
  • Strategies, not decisions: ML produces fixed decisions that do not consider future reactions. They are certainly sub-optimal given the high-level goals of the business. RL learns strategies, which encode how to achieve some future state for the organization. The resulting strategies may surprise you!

Reinforcement Learning Services

Our highly talented team unlocks automated strategies to put your business on autopilot.

Reinforcement Learning Development

Reinforcement Learning Development

Are you looking for world leading experts in reinforcement learning to help you develop your RL project?

Look no further than Winder Research. We literally wrote the book on industrial reinforcement learning and we’re pleased to offer our development services to help develop your products and services.

Reinforcement Learning Consulting

Reinforcement Learning Consulting

Do you need strategic help from an expert in reinforcement learning?

Our reinforcement learning consulting services help you make the right decisions at the right time, saving you a fortune in future sunk costs. Winder Research’s reinforcement learning experts can help you plan and design reinforcement learning based solutions to a variety of industries and problems.

Reinforcement Learning POCs

Reinforcement Learning POCs

Do you have a known problem, but you want to prove viability?

Many of our projects take the form of proof-of-concepts (POCs) where we spend a short amount of time to validate that there is a data-oriented solution to the problem. Organizations love this service to de-risk larger projects.

The World's Best AI Companies

From startups to the world’s largest enterprises, companies trust Winder Research.

Reinforcement Learning for Leaders

Download your free chapter from Phil's book - Practical Reinforcement Learning

A Leaders Perspective of Reinforcement Learning

In this introductory video, Dr. Phil Winder, CEO of Winder Research spends 3 minutes, introducing RL. Watch this video if you want a quick overview of how you can use RL to improve your organizations' efficiencies, growth, and products.

Download a free chapter from our book on industrial reinforcement learning.

Download Your Free Chapter - “Practical Reinforcement Learning”

We are delighted to offer you a complimentary chapter written by our company CEO and Leader, Dr. Phil Winder.

The free chapter will enable you to learn about:

  • What RL problems look like and how RL overcomes these within an organization
  • Proven RL organization implementation processes
  • Top tips for RL pre-production tooling and techniques

You can find out more about the book on the dedicated rl book website.

How do I get my copy?

Fill in the form opposite, and we will send you your free chapter on “Practical Reinforcement Learning” directly to your inbox. Please remember to check Spam and Junk folders if nothing arrives back.

What happens next?

What if you would like to learn more about RL and, or maybe data as a whole?

As a leader, you wear many ‘hats’ and, like every organization, no matter its' size, has daily, weekly, perhaps longer-term challenges around ‘sorting/cleaning/enhancing data. We listen and share in confidence to learn more about leaders’ needs and aspirations for their team, department, and organization.

We understand and have much in the way of insights to offer and can support you and your organization, no matter its' stage in life, shape, sector, or size. We uniquely work with all. (see our website to learn more).

Dr. Phil Winder will personally look to reach back over the coming days and answer any follow-up questions you may have.

Selected Case Studies

Some of our most recent work. Find more in our portfolio.

Using Reinforcement Learning to Attack Web Application Firewalls

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.

Helping Modzy Build an ML Platform

Winder Research collaborated with the Modzy development team and MLOps Consulting to deliver a variety of solutions that make up the Modzy product, a ModelOps and MLOps platform. A summary of this work includes: Developing the Open Model Interface Open-sourcing chassis, the missing link that allows data scientists to build robust ML containers Model monitoring and observability product features MLOps and model management product features The Problem: How to Build An ML Platform Modzy’s goal is to help large organizations orchestrate and manage their machine learning (ML) models.

How To Build a Robust ML Workflow With Pachyderm and Seldon

This article outlines the technical design behind the Pachyderm-Seldon Deploy integration available on GitHub and is intended to highlight the salient features of the demo. For an in depth overview watch the accompanying video on YouTube. Introduction Pachyderm and Seldon run on top of Kubernetes, a scalable orchestration system; here I explain their installation process, then I use an example use case to illustrate how to operate a release, rollback, fix, re-release cycle in a live ML deployment.