Written by

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.

Bernard’s latest book is ‘Business Trends in Practice: The 25+ Trends That Are Redefining Organisations’

View Latest Book

Follow Me

Bernard Marr ist ein weltbekannter Futurist, Influencer und Vordenker in den Bereichen Wirtschaft und Technologie mit einer Leidenschaft für den Einsatz von Technologie zum Wohle der Menschheit. Er ist Bestsellerautor von 20 Büchern, schreibt eine regelmäßige Kolumne für Forbes und berät und coacht viele der weltweit bekanntesten Organisationen. Er hat über 2 Millionen Social-Media-Follower, 1 Million Newsletter-Abonnenten und wurde von LinkedIn als einer der Top-5-Business-Influencer der Welt und von Xing als Top Mind 2021 ausgezeichnet.

Bernards neueste Bücher sind ‘Künstliche Intelligenz im Unternehmen: Innovative Anwendungen in 50 Erfolgreichen Unternehmen’

View Latest Book

Follow Me

What is deep reinforcement learning?

2 July 2021

One of the most intriguing areas of artificial intelligence today is the concept of deep reinforcement learning, where machines can teach themselves based upon the results of their own actions. It is one of the areas of artificial intelligence that shows great promise, so let’s look at what it is and explore some real-world applications.

What is deep reinforcement learning?

Deep reinforcement learning is a category of machine learning and artificial intelligence where intelligent machines can learn from their actions similar to the way humans learn from experience. Inherent in this type of machine learning is that an agent is rewarded or penalised based on their actions. Actions that get them to the target outcome are rewarded (reinforced).

Through a series of trial and error, a machine keeps learning, making this technology ideal for dynamic environments that keep changing. Although reinforcement learning has been around for decades, it was much more recently combined with deep learning, which yielded phenomenal results. The “deep” portion of reinforcement learning refers to a multiple (deep) layers of artificial neural networks that replicate the structure of a human brain. Deep learning requires large amounts of training data and significant computing power. Over the last few years, the volumes of data have exploded while the costs for computing power have dramatically reduced, which has enabled the explosion of deep learning applications.

From gameplay to profit-making deep reinforcement learning

The possibilities of deep reinforcement learning came to the attention of many during the well-publicised defeat of a Go grandmaster by DeepMind’s AlphaGo. In addition to playing Go, deep reinforcement learning has achieved human-level prowess in other games such as chess, poker, Atari games and several other competitive video games. It’s taken the technology a bit of time to move from board games to boardrooms for a couple of reasons including: 

  • There needed to be products and services to support deep reinforcement learning. For example, simulation technology helps provide a trial-and-error environment for deep reinforcement learning that is scalable and where mistakes won’t cause real-world damage. Services needed to be available to offer simulation technology for multiple interacting machines.
  • Subject matter experts need an easy-to-use deep reinforcement learning (DRL) interface—rather than be DRL experts—to fully leverage the technology for business problems.

Practical applications of deep reinforcement learning

AI toolkits for training

AI toolkits such as OpenAI Gym, DeepMind Lab and Psychlab are providing the training environment that was necessary to catapult large-scale innovation for deep reinforcement learning. These open-source tools train DRL agents. As more organisations apply deep reinforcement learning to their own unique business use cases, we will continue to see dramatic growth in practical applications.


Intelligent robots are becoming more commonplace in warehouse and fulfilment centres to sort out millions of products and deliver them to the right people. When a robot picks a device to put in a container, deep reinforcement learning helps it gain knowledge based on whether it succeeded or failed. It uses this knowledge to perform more efficiently in the future.


The automotive industry has a diverse and large dataset that will power deep reinforcement learning. Already in use for autonomous vehicles, it will help transform factories, vehicle maintenance and overall automation in the industry. The industry is driven by safety, quality and cost and DRL with data from customers, dealers and warranties will provide new ways to improve quality, save money and have a higher safety record.


Using artificial intelligence, including deep reinforcement learning, to be better investment managers than humans and to evaluate trading strategies is the core objective of Pit.AI.


From determining the optimal treatment plans and diagnosis to clinical trials, new drug development and automatic treatment, there is great potential for deep reinforcement learning to improve healthcare.


The conversational UI paradigm that makes AI bots possible leverages the power of deep reinforcement learning. The bots are rapidly learning the nuances and semantics of language over many domains for automated speech and natural language understanding thanks to deep reinforcement learning.

There is much excitement about the potential for deep reinforcement learning. Since this segment of artificial intelligence learns by interacting with its environment, there is really no limit to the possible applications.

Business Trends In Practice | Bernard Marr
Business Trends In Practice | Bernard Marr

Related Articles

The Five Biggest Healthcare Tech Trends In 2022 | Bernard Marr

The Five Biggest Healthcare Tech Trends In 2022

Wherever we look in the healthcare industry, we can find new technology being used to fight illness, develop new vaccines and medicines, and help people to live healthier lives[...]

The 10 Tech Trends That Will Transform Our World | Bernard Marr

The 10 Tech Trends That Will Transform Our World

What makes the fourth industrial revolution so different from previous industrial revolutions is the convergence and interaction between multiple technology trends at once. In thi[...]

The 5 Biggest Connected And Autonomous Vehicle Trends In 2022

Autonomous driving promises a future where road traffic accidents and speeding tickets are no longer a feature of life.[...]

The Five Biggest Cyber Security Trends In 2022

The changed world we’ve found ourselves living in since the global pandemic struck in 2020 has been particularly helpful to cybercriminals.[...]

The Five Biggest Space Technology Trends For 2022

The past decade has seen a resurgence of interest in space travel and the technological innovation driving it.[...]

The 5 Biggest Biotech Trends In 2022

Steve Jobs once said that the biggest innovations in the 21st century would be at the intersection of biology and technology.[...]

Stay up-to-date

  • Get updates straight to your inbox
  • Join my 1 million newsletter subscribers
  • Never miss any new content

Social Media



View Podcasts