Why Reinforcement Learning Matters and How It Can Help Your Business


Why Reinforcement Learning Matters and How It Can Help Your Business

Reinforcement learning is a powerful type of machine learning algorithm that is rapidly gaining traction in the business world. This approach to artificial intelligence is particularly well-suited to decision automation, helping companies solve complex problems, improve efficiency and reduce expenses. By using algorithms that learn from experience, reinforcement learning enables businesses to optimise their operations with ease, automate repetitive processes and ultimately achieve better outcomes. In this article, Software Planet Group will explore the potential benefits of reinforcement learning for businesses — from resource allocation to supply chain management — and discuss some real-world examples of how these algorithms are being used today.

What is Reinforcement Learning

What Is Reinforcement Learning?

Reinforcement learning (RL) is a branch of machine learning (ML) that trains machines to learn optimal policies by interacting with an environment through trial-and-error. It’s a bit like teaching a child to ride a bicycle — you may give them some initial guidance, but they have to figure it out on their own. RL has emerged as a powerful tool for solving complex decision-making problems in fields like finance, healthcare, robotics and virtually any other industry you can think of.
Just like other machine learning techniques, such as neural networks, decision trees and clustering, RL algorithms can modify their behaviour based on previous learnt experience. What makes reinforcement learning particularly special is that it involves an agent learning to not only interact with an environment, but how to maximise its rewards over time. By contrast, other types of ML will typically rely on learning patterns or other relationships in data, and do not ever interact with an environment.

How Does RL Work in Practice?

Reinforcement learning is also unique in that it brings together psychology and science, in effect a mathematical implementation of how we think human beings make decisions. With that in mind, RL can work in two different states: exploration and exploitation. During the exploration phase, the algorithm can choose any action. Of course, this is kept within safe boundaries, but it is important to enable the algorithm to make mistakes in order to learn. Depending on the quality of every decision, it will either be rewarded or be given a penalty.

The Role of Rewards in Reinforcement Learning

The goal of RL is to teach a machine to make better decisions based on the rewards it receives for different actions. For instance, a reward could be the number of seconds a 3D model can stand without falling, when the machine is trying to teach it how to walk. In this way, the machine learns to associate certain actions with higher rewards and adapts its decision-making process accordingly.

The Markov Process and Bellman Equation

Reinforcement learning takes its cues from the Markov process and Bellman equation. The former is a mathematical framework describing the evolution of a system over time that is based on its current state and the probability of transitioning to a new one. The latter, on the other hand, expresses the relationship between the value of a state or action and the expected reward that is obtained from it, as well as the value of the next action or state. Let’s break this down into simpler language.

The Markov Process

Imagine that you are playing a video game where you can only see what’s directly in front of you. According to the Markov process, what happens next will depend only on what you’re seeing right now, not on anything that happened before it. This in practice means that knowing the current state of the system will be sufficient to predict its following state, without needing to know its entire history. Reinforcement learning uses this concept to help the computer decide its following action based exclusively on what’s happening right now. The Tesla Autopilot is a great example of a product that uses the Markov process to make decisions (e.g. the road curves to the right, so the best possible action at the moment would be to turn the vehicle right and follow it).

Bellman equation

The Bellman equation, on the other hand, is used to estimate the value of a particular action in a given state. It’s like a recipe for making decisions, like when you are trying to make a cake, but do not know which ingredients to use or even how long to bake the cake for. Similarly, the Bellman equation is able to tell you what to do each step of the way. It helps the computer figure out what actions to take — and when to take them — based on what it expects to happen next. This is a fundamental concept in reinforcement learning, as the computer learns to maximise its long-term rewards by considering the rewards it receives for every action and the probability of moving to a different state.

What Are Some Popular Examples of RL in the Real World?

RL has been applied to a variety of real-world scenarios, from recommendation systems and finance to game playing and robotics.

Type Description


Reinforcement learning is used in robotics to teach robots to perform complex tasks, such as walking, running, grasping and manipulating physical objects. This makes it a promising tool for creating more versatile and advanced robots that are able to operate in unpredictable environments.


RL is also used to create intelligent agents that can learn to play games through trial and error. There are many examples of RL applications in gaming. The most recent of these is Project Paidia — a collaboration between gaming studio Ninja Theory and Microsoft Research Cambridge. In it, team-based game Bleeding Edge became a test bed for not only improving existing game AI, but replacing traditional bots and NPCs. With the help of reward signals, the team is utilising reinforcement learning to emulate collaborative human behaviour.

Inventory management

RL algorithms can be trained to optimise inventory levels by dynamically adjusting order quantities and reorder points based on lead times and demand patterns. This can help to minimise stockouts and excess inventory, which in turn leads to better customer service and lower costs.

Energy management

These algorithms are also being utilised to optimise energy usage in supply chain operations, including warehouses and distribution centres. By learning from historical energy consumption patterns and external factors such as the weather, they can adjust lighting and temperature settings — to minimise energy waste whilst maintaining a comfortable working environment.


Reinforcement learning is employed in the finance industry to trade stocks and bonds and other financial instruments. With the ability to analyse massive amounts of data, RL can uncover patterns that are often missed by human analysts, helping investors make more informed decisions.


In advertising, RL is often used to optimise marketing campaigns by learning which ads are the most effective at driving user engagement and growth. For instance, a company might utilise reinforcement learning to optimise their email marketing efforts. The algorithm could start by sending out emails with a variety of subject lines, images and content, before tracking user engagement metrics like open rates, click-through rates and conversions. Based on the feedback received from these metrics, it could then adjust its approach for future campaigns.

Quality control

Reinforcement learning algorithms can also optimise QA processes by learning from historical defect data and adjusting sampling rates and inspection criteria accordingly. This may help reduce the risk of defective products reaching the market and improve overall product quality.


RL is commonly employed in healthcare to create personalised treatment plans for patients that are based on individual health data. Recently, for instance, a group of researchers used reinforcement learning to identify high-risk treatments for patients suffering from chronic kidney disease.


Beyond autonomous vehicles, reinforcement learning can be used to optimise schedules and transportation routes, taking into account important factors such as traffic, weather and delivery deadlines. This can help reduce transportation expenses and improve delivery times.


E-commerce companies like Amazon can also utilise reinforcement learning to suggest products to customers based on previous purchases and browsing history. The algorithm then learns from the customer's feedback and adjusts its recommendations accordingly.


Without a doubt, RL is a powerful tool for any business. It enables machines to learn from their experiences and improve their actions, and has a wide range of potential applications in fields like finance, customer service and supply chain management. By harnessing the power of reinforcement learning today, companies are able to improve their decision making processes — to increase efficiency, reduce expenses and substantially increase their profits.

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