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 they don’t leverage any context, nor do they operate over sequences. For example, a traditional recommendations algorithm will suggest a specific set of products and the algorithms are optimized to improve that 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 towards your organization’s unique goals.

What Is the Value Proposition of Reinforcement Learning

Strategic decisions tend to be the most lucrative choices a business can make and they are typically the most expensive. Reinforcement learning is a technique that we pair with AI consulting to automate strategic decisions.

How Does Reinforcement Learning Work?

The key premise in reinforcement learning is the concept of an environment and a policy. The environment represents the space that the agent will operate within, with all the signals and data it can observe. The policy represents the internal decision model of the agent, which is responsible for choosing what to do within the environment. Over time, the agent learns to update it’s internal model of the world to make better decisions.

Revolutionary Reinforcement Learning Services

Discover how reinforcement learning is changing the way organizations do AI – with one of the world leaders, Winder.AI.

RL Services RL Expertise RL Case Studies FAQ Contact Us

RL Consulting Services - Reinforcement Learning Services

Our next-generation RL services bring automated strategies to your business.

RL Development We’re authors of the book on applying industrial reinforcement learning. Our RL engineers stand ready to develop your projects, e.g. a global aviation company.
RL Consulting Make the right decisions at the right time. Winder.AI’s reinforcement learning guidance can help you plan and design solutions to a variety of problems, e.g. a finance company.
RL POCs De-risk production RL projects with rapid proof of concept projects, e.g. Nestlé.
Simulation Development Simulators enable us to rapidly develop and evaluate RL agents irrespective of the methodology.
Deep RL Development Deep reinforcement learning generates the most advanced RL algorithms.

The World's Best AI Companies Trust Winder.AI

We've worked with hundreds of amazing people, all over the world.

RL Development Expertise - Our Sweet Spot

Enterprise-ready Reinforcement Learning solutions.

Dedicated AI Company: Unlike larger, more general purpose IT agencies, we specialize in developing AI solutions. We’re more flexible, more pragmatic and better suited to integrate AI into your product.
Production Ready: We combine our bespoke AI solutions with cloud-agnostic or on-premise software engineering to produce production-ready large language models.
Collaborative Working: We exist to help companies like yours build better products. We collaborate closely with your internal engineering teams to both upskill and deliver pragmatic results.

RL Solutions - RL Capabilities

Winder.AI is a flexible, decentralized, independent AI company that can deliver full-stack solutions. Reinforcement learning creates intelligent agents for automated strategies.

Reactive, Strategic Solutions: Reinforcement learning provides techniques and algorithms for reactive agents. Optimize for the long term.
Optimizing For Profit: With RL, you’re able to optimize for the thing you really care about, not a proxy metric, like accuracy.
Automate Complex, Multi-Step Scenarios: RL excels at automating multi-step processes, far better than any machine learning solution.
Cope with Adversity: RL provides better strategies for coping with adverse or anomalous conditions.

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Any Library

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Free Book - Reinforcement Learning for Leaders

A free chapter from the book, Reinforcement Learning, by Winder.AI.

We are delighted to offer you a complimentary chapter written by our company CEO, 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 on the dedicated RL book website.

How do I get my copy?

Fill in the form and we will send a free chapter on Practical Reinforcement Learning directly to your inbox, free of charge. Please remember to check spam and junk folders if nothing arrives.

What happens next?

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

As a leader, you wear many ‘hats’ and, like every organization, no matter its size, it has constant challenges around sorting, cleaning and enhancing data. We listen to leaders’ needs and aspirations for their organizations.

We can offer insights and support, no matter the age or size of your organization. We uniquely work with all sectors.

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

Selected Case Studies

Some of our most recent work for our clients. You can find more in our portfolio.

2024Case study

Transforming Legal Research with AI Legal Text Analysis

Legal AI assistant improves legal coding efficiency by automating data analysis, reducing workload, and accelerating research.

2023Case study

MLOps in Supply Chain Management

Interos, a leading supply chain management company, partnered with Winder.AI to enhance their machine learning operations (MLOps). Together, we developed advanced MLOps technologies, including a scalable annotation system, a model deployment suite, AI templates, and a monitoring suite. This collaboration, facilitated by open-source software and Kubernetes deployments, significantly improved Interos’ AI maturity and operational efficiency.

2023Case study

Announcing Stable Audio: A Generative AI Music Service

We’re pleased to announce the release of Stable Audio, a new generative AI music service. Stable Audio is a collaboration between Stability AI and Winder.AI that leverages state-of-the-art audio diffusion models to generate high-quality music from a text prompt.

Recent Reinforcement Learning Articles

Find more articles in our blog.

Jul 26, 2023 Reinforcement Learning

Deep Reinforcement Learning Workshop - Hands-on with Deep RL

This is a video of a workshop about deep reinforcement learning (DRL). First presented at ODSC London in 2023, it is nearly three hours long and covers a wide variety of topics. Split into three sections, the video introduces DRL and RL applications, explains how to develop an RL project, and walks you through two RL example notebooks.

Jan 19, 2022 Reinforcement Learning

Reinforcement Learning Presentation: Cyber Security

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.

May 5, 2021 Reinforcement Learning

Automating Cyber-Security with Reinforcement Learning

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.

FAQs - Frequently Asked Questions

This page provides answers to our most common questions. If you have a query that isn't covered, please get in touch.

: 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 they don’t leverage any context, nor do they operate over sequences. For example, a traditional recommendations algorithm will suggest a specific set of products and the algorithms are optimized to improve that 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 towards your organization’s unique goals.
: Strategic decisions tend to be the most lucrative choices a business can make and they are typically the most expensive. Reinforcement learning is a technique that we pair with AI consulting to automate strategic decisions.
: The key premise in reinforcement learning is the concept of an environment and a policy. The environment represents the space that the agent will operate within, with all the signals and data it can observe. The policy represents the internal decision model of the agent, which is responsible for choosing what to do within the environment. Over time, the agent learns to update it’s internal model of the world to make better decisions.

Start Your RL Development Project Now

The team at Winder.AI are ready to collaborate with you on your rl development project. We tailor our AI solutions to meet your unique needs, allowing you to focus on achieving your strategic objectives. Fill out the form below to get started.

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