From the author of the O’Reilly book on reinforcement learning. Winder.AI delivers reinforcement learning as a service: consulting, development, and production deployment of RL applications across finance, manufacturing, supply chain, and more.
I could always count on Winder.AI to drive their responsibilities forward with only limited oversight—they knew what our goals were and how to achieve them. They also had a very high bar for quality. Everything they touched delivered an impressive result.
We deliver end-to-end reinforcement learning solutions: from identifying real-world RL applications and building simulations, to training agents and deploying production systems. Led by the author of O'Reilly's Reinforcement Learning, our team brings unmatched expertise to every engagement.
Our RL expertise has been trusted by companies across aviation, energy, finance, manufacturing, and technology.
No other consultancy combines published authority, commercial delivery, and decade-long AI experience like Winder.AI.
Our CEO, Dr. Phil Winder, is the author of Reinforcement Learning: Industrial Applications of Intelligent Agents, published by O’Reilly Media. This is the definitive industry guide to applying RL in production. No other RL consultancy can match this level of published expertise.
We have successfully delivered reinforcement learning projects in aviation, finance, energy, manufacturing, cyber security, and supply chain. Our track record spans POCs through to production systems serving real users and generating measurable business outcomes.
We don’t just build RL models in notebooks. We deliver production-grade reinforcement learning systems with proper engineering: cloud deployment, monitoring, CI/CD, and integration with your existing infrastructure. Our MLOps expertise ensures your RL agents run reliably at scale.
Reinforcement learning is one of the hardest disciplines in AI to hire for. Here's why leading organizations choose to work with a specialist RL consultancy.
Hiring a reinforcement learning engineer with production experience is extremely difficult. The talent pool is small, salaries are high, and most RL expertise sits in academia, not industry. A specialist RL consultancy gives you immediate access to a team of experienced RL engineers without the recruitment risk or the long ramp-up time.
Many reinforcement learning startups and in-house teams spend months experimenting without reaching production. Winder.AI has delivered RL systems that run in production across multiple industries. We know which approaches work and which are dead ends, saving you months of trial and error.
Our reinforcement learning as a service model means you pay for outcomes, not headcount. Start with a focused proof of concept to validate the approach, then scale to production only when the business case is proven. No long-term hiring commitments, no idle capacity between projects.
Reinforcement learning applications span every industry where there are sequential decisions, dynamic environments, and measurable business objectives. Winder.AI has delivered real-world RL solutions across a wide range of industries:
RL agents learn to optimize flight scheduling, gate assignments, and crew management in dynamic operational environments. We built a digital twin and RL-based flight scheduling system for a leading aerospace company.
Financial reinforcement learning automates complex strategies across trading, credit risk management, portfolio optimization, and customer lifecycle decisions. RL agents learn adaptive strategies for financial markets that respond to changing conditions in real time. We delivered a production RL system for a UK financial institution that optimized customer journey decisions across millions of interactions.
RL agents learn to balance competing constraints in power generation, grid management, and energy trading. We built an RL solution for Genesis Energy to automate hydroelectric power generation and pricing decisions.
Reinforcement learning in manufacturing automates complex industrial processes that are too dynamic for traditional control systems. RL agents learn to optimize production scheduling, quality control, and resource allocation in real time. We helped CMPC optimize their paper manufacturing process using reinforcement learning, replacing manual process control.
RL agents can proactively discover vulnerabilities by learning to attack systems in controlled environments. We developed an RL-based penetration testing agent that autonomously identifies weaknesses in web application firewalls.
Reinforcement learning for supply chain optimization handles multi-echelon inventory management, demand forecasting, and distribution routing. These problems involve thousands of interdependent decisions that are perfectly suited to reinforcement learning. RL agents learn adaptive supply chain strategies that respond to disruptions and demand fluctuations in real time.
From autonomous vehicle decision-making to traffic signal control and fleet routing, RL delivers adaptive strategies for complex transportation systems.
RL goes beyond single-step recommendations to optimize entire user engagement sequences, maximizing long-term metrics like lifetime value, retention, and satisfaction rather than click-through rate.
Winder.AI is a flexible, independent AI company with deep technical expertise across the full spectrum of reinforcement learning methods and techniques:
The reward function is the single most important design decision in any RL system. We draw on extensive experience to design reward structures that align agent behaviour with your actual business objectives, avoiding common pitfalls like reward hacking and misalignment.
We design and build simulation environments that faithfully represent your real-world problem, including digital twins. Our simulators enable rapid, safe agent training while capturing the dynamics that matter for your domain.
When multiple reinforcement learning agents must interact, cooperate, or compete, multi-agent RL provides the framework. We have experience with competitive, cooperative, and mixed multi-agent scenarios across diverse applications. Our RL agent expertise also underpins our AI agent development practice, where agentic AI and reinforcement learning intersect.
When real-time interaction is impractical, offline RL learns from historical data. This is critical for domains like healthcare, finance, and operations where live experimentation is costly or risky.
Production RL requires safety constraints. We apply constrained optimization, safe exploration, and human-in-the-loop techniques to ensure RL agents operate within acceptable bounds in production environments.
Reinforcement Learning from Human Feedback is the technique behind aligning large language models with human preferences. As RLHF specialists with deep foundational RL expertise, we help organizations implement RLHF pipelines and fine-tune LLMs for domain-specific performance. Our RLHF consulting complements our LLM development and AI agent development services.
The O'Reilly book on reinforcement learning, written by our CEO, Dr. Phil Winder.
Dr. Phil Winder’s book, Reinforcement Learning: Industrial Applications of Intelligent Agents, published by O’Reilly Media, is the definitive guide to applying reinforcement learning in real-world business settings. It bridges the gap between academic RL research and practical industrial deployment.
The book covers:
The book has been adopted by practitioners and organizations worldwide as the go-to resource for industrial RL. Visit the dedicated book website to learn more.
We are also delighted to offer you a complimentary chapter to get started.
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.
Dr. Phil Winder will personally aim to reach back over the coming days to answer any follow-up questions you may have. Whether you are exploring RL for the first time or looking to scale an existing initiative, we are ready to help.
As a reinforcement learning consultancy, we work across all industries and organization sizes. We listen to your needs and provide pragmatic guidance on how to leverage RL for your specific challenges.
Understanding how we deliver reinforcement learning projects for our clients.
Every reinforcement learning project at Winder.AI follows a proven methodology refined through years of commercial delivery:
1. Problem Assessment and Reward Design. We work with your domain experts to understand the decision problem, identify the key variables and constraints, and design a reward function that captures your true business objective. This is the most critical step and draws heavily on the frameworks described in our O’Reilly book.
2. Environment and Simulation Development. We build a simulation that represents your problem domain. This could be a digital twin of a physical system, a model of customer behaviour, or a representation of your operational environment. The simulation must be fast enough for millions of training episodes while remaining faithful to reality.
3. Agent Training and Algorithm Selection. We select and configure the right RL algorithm for your problem. Factors include the action space (discrete vs. continuous), observation space dimensionality, reward structure, and whether the problem is single-agent or multi-agent. We typically evaluate multiple approaches and select the best performer.
4. Evaluation and Validation. We rigorously evaluate the trained agent against baselines, including your current approach, heuristic policies, and other ML methods. We quantify the expected business impact and identify edge cases or failure modes.
5. Production Deployment and Monitoring. We integrate the RL agent into your production systems with appropriate safeguards, monitoring, and rollback capabilities. Our MLOps expertise ensures reliable, observable, and maintainable deployments.
Case study
Our financial client is based in the UK. They specialise in providing services to the finance industry. Their data science team embarked on a project to leverage reinforcement learning within their product offering. Winder.AI, world-leading authors and experts on reinforcement learning, helped them deliver their POC into production. Read on to find out more.
Case study
Genesis Energy is a power generation company in New Zealand that sells electricity generated by hydroelectric and hydrothermal generators to the domestic energy market. Currently, people control the decisions surrounding power generation and pricing. Genesis asked Winder.AI to help them develop a reinforcement learning-powered solution to automate generation and pricing.
New Zealand has the enviable situation of possessing high-altitude lakes refilled with ice melt. Discharging the lake presents an ample kinetic energy store that can be utilised for power generation via a turbine. Hydroelectric power generation is therefore sustainable and low carbon.
Case study
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
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.
Reinforcement Learning
In this video, Dr. Phil Winder presents a talk about the use of reinforcement learning in cyber security to automate penetration testing of web application firewalls.
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.
Answers to common questions about reinforcement learning applications, consulting, RLHF, and how RL is used across industries. 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. Unlike traditional ML which optimizes a single prediction, RL optimizes sequences of decisions towards your actual business objective, whether that’s revenue, engagement, throughput, or cost reduction.
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 its internal model of the world to make better decisions. We cover this in depth in our O’Reilly book.
Winder.AI is led by Dr. Phil Winder, who literally wrote the book on RL: Reinforcement Learning: Industrial Applications of Intelligent Agents, published by O’Reilly. We have delivered RL solutions across aviation, finance, energy, manufacturing, cyber security, and supply chain. No other consultancy combines this depth of published expertise with this breadth of commercial delivery.
RL has been applied successfully across a wide range of industries. We have delivered projects in aviation (flight scheduling), finance (customer journey optimization), energy (hydroelectric power generation), manufacturing (industrial process control), cyber security (penetration testing), and supply chain (inventory optimization). Any industry with sequential decision-making problems and a measurable objective can benefit from RL.
A typical RL proof of concept takes 8 to 12 weeks, which includes environment design, simulation development, agent training, and evaluation. Full production deployments vary depending on integration complexity, but we have delivered end-to-end solutions in as few as three months. We always start with a focused POC to de-risk the approach.
A simulation or digital twin is usually the most efficient path to training RL agents, because it allows the agent to explore millions of scenarios safely and cheaply. However, it is not always strictly required. In some cases, offline RL techniques can learn from historical data. Our team will assess your situation and recommend the most practical approach.
Traditional ML learns to make a single prediction or classification from data. RL learns strategies that unfold over multiple steps, optimizing a long-term objective through trial and error. For example, traditional ML might predict which product a customer is likely to buy next, while RL would learn an entire engagement strategy that maximizes lifetime customer value. We explain this distinction thoroughly in our O’Reilly book.
We are framework-agnostic and select the best tools for each project. Our team has deep experience with Ray RLlib, Stable Baselines, CleanRL, PyTorch, TensorFlow, OpenAI Gym, Gymnasium, and custom simulation environments. We also have extensive experience with cloud platforms including AWS, Azure, and GCP for scalable training and deployment.
Yes. While RL agents typically learn through interaction with an environment, offline RL and batch RL methods allow agents to learn from historical decision data. This is particularly powerful in industries where real-time experimentation is costly or risky, such as finance or healthcare. We will assess your data and recommend the best approach.
Reinforcement learning is fundamental to how large language models are aligned with human preferences. The technique known as RLHF (Reinforcement Learning from Human Feedback) uses RL to fine-tune language models to produce more helpful, safe, and accurate responses. Our deep RL expertise gives us unique insight into this rapidly evolving field.
Reinforcement learning is used across a wide range of real-world applications. In finance, RL optimizes trading strategies, credit decisions, and customer lifecycle management. In manufacturing, it controls industrial processes and optimizes production scheduling. In supply chain management, RL handles inventory optimization and logistics routing. Other real-world applications include flight scheduling in aviation, energy grid management, autonomous driving, robotics control, and personalized recommendations. Any domain with sequential decisions and a measurable objective is a candidate for RL.
Financial reinforcement learning is one of the most active application areas. RL is used in algorithmic trading to learn adaptive strategies that respond to market conditions, in portfolio optimization to balance risk and return over time, in credit risk management to optimize lending decisions across customer lifecycles, and in customer journey optimization to maximize lifetime value. We have delivered RL solutions for UK financial institutions and have deep experience applying RL to financial markets.
RLHF (Reinforcement Learning from Human Feedback) is the technique used to align large language models with human preferences and business requirements. If your organization is deploying LLMs, RLHF can fine-tune models to follow your brand voice, comply with industry regulations, reduce harmful outputs, and improve accuracy on domain-specific tasks. As an RLHF consultancy with deep foundational RL expertise, Winder.AI can help you implement RLHF pipelines, design reward models, and fine-tune LLMs for your specific needs.
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.
