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Bernard Marr

Bernard Marr is a world-renowned futurist, influencer and thought leader in the fields of business and technology, with a passion for using technology for the good of humanity. He is a best-selling author of 20 books, writes a regular column for Forbes and advises and coaches many of the world’s best-known organisations. He has over 2 million social media followers, 1 million newsletter subscribers and was ranked by LinkedIn as one of the top 5 business influencers in the world and the No 1 influencer in the UK.

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Artificial Intelligence: What Is Reinforcement Learning – A Simple Explanation & Practical Examples

2 July 2021

Reinforcement learning is one of the most discussed, followed and contemplated topics in artificial intelligence (AI) as it has the potential to transform most businesses. In this article, I want to provide a simple guide that explains reinforcement learning and give you some practical examples of how it is used today.         

What is Reinforcement Learning?

At the core of reinforcement learning is the concept that the optimal behaviour or action is reinforced by a positive reward.

Similar to toddlers learning how to walk who adjust actions based on the outcomes they experience such as taking a smaller step if the previous broad step made them fall, machines and software agents use reinforcement learning algorithms to determine the ideal behaviour based upon feedback from the environment. It’s a form of machine learning and therefore a branch of artificial intelligence.  

Depending on the complexity of the problem, reinforcement learning algorithms can keep adapting to the environment over time if necessary in order to maximise the reward in the long-term. So, similar to the teetering toddler, a robot who is learning to walk with reinforcement learning will try different ways to achieve the objective, get feedback about how successful those ways are and then adjust until the aim to walk is achieved. A big step forward makes the robot fall, so it adjusts its step to make it smaller in order to see if that’s the secret to staying upright. It continues its learning through different variations and ultimately is able to walk. In this example, the reward is staying upright, while the punishment is falling. Based on the feedback the robot receives for its actions, optimal actions get reinforced.

Reinforcement learning requires a lot of data which is why first applications for the technology have been in areas where simulated data is readily available such as in gameplay and robotics.

8 Practical Examples of Reinforcement Learning  

Even though we are still in the early stages of reinforcement learning, there are several applications and products that are starting to rely on the technology. Companies are beginning to implement reinforcement learning for problems where sequential decision-making is required and where reinforcement learning can support human experts or automate the decision-making process. Here are a few:

    1.  Robotics

Reinforcement learning gives robotics a “framework and a set of tools” for hard-to-engineer behaviours. Since reinforcement learning can happen without supervision, this could help robotics grow exponentially.

    2.  Industrial automation

Thanks to the reinforcement learning capabilities from DeepMind, Google was able to reduce energy consumption in its data centres dramatically. Bonsai, recently acquired by Microsoft, offers a reinforcement learning solution to automate and “build intelligence into complex and dynamic systems” in energy, HVAC, manufacturing, automotive and supply chains.

    3.  Enhance predictive maintenance

Machine learning has been used in manufacturing for some time, but reinforcement learning would make predictive maintenance even better than it is today.

    4.  Game playing

Indeed, the first application in which reinforcement learning gained notoriety was when AlphaGo, a machine learning algorithm, won against one of the world’s best human players in the game Go. Now reinforcement learning is used to compete in all kinds of games.

    5.  Medicine

Reinforcement learning is ideally suited to figuring out optimal treatments for health conditions and drug therapies. It has also been used in clinical trials as well as for other applications in healthcare.

    6.  Dialogue systems

Since companies receive a lot of abstract text in the form of customer inquiries, contracts, chatbots and more, solutions that use reinforcement learning for text summaries are highly coveted. Inherent in these tools is they get better over time.

    7.  Personalization

Whether it’s the media you consume, the advertising that’s targeted to you or the goods you should purchase next on Amazon, there are reinforcement learning algorithms at play behind the scenes to create a stellar customer experience.

    8.  Autonomous vehicles

Most autonomous cars, trucks, drones, and ships have reinforcement algorithms at the centre. Wayve, a UK company, designed an autonomous vehicle that learned to drive in 20 minutes with the help of reinforcement learning.

Since significant data sets are required to make reinforcement learning work, more companies will be able to leverage reinforcement learning’s capabilities as they acquire more data. And, as the value of reinforcement learning continues to grow, companies will continue investments in resources to figure out the best way to implement the technology in their operations, services, and products. 

Data Strategy Book | Bernard Marr

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