Reinforcement Learning Real-world examples


In this post, you will learn about some real-world / real-life examples of Reinforcement learning, one of the different approaches to machine learning where other approaches are supervised and unsupervised learning.

Before looking into the real-world examples of Reinforcement learning, let’s quickly understand what is reinforcement learning.

Introduction to Reinforcement Learning (RL)

Reinforcement learning is an approach to machine learning in which the agents are trained to make a sequence of decisions. The agent, also called as an AI agent gets trained in the following manner:

  • The agent interacts with the environment and make decisions or choices. For training purpose, the agent is provided with the contextual information about the environment and choices.
  • The agent is provided with the feedback or rewards based on how well the action taken by the agent or the decision made by the agent resulted in achieving the desired goal.​​​​​​​​​

The diagram below represents the above. In the diagram below, the agent (software agent) takes an action in the given environment having state s. The environment sends response to the agent in form of reward (r) and the new state information. The state changes as a result of action (a) taken by the agent.

Introduction to Reinforcement learning
Fig 1. Reinforcement learning

How & when to have RL models deployed in the production?

Here is one of the ways in which RL models can be deployed in the production.

The user actions are recorded and stored in the database. The AI agent learns from this recorded data in the batch mode. When the AI agent has learned enough from users’ actions to approximate the recommendation at high accuracy, the agent can be deployed in the production to let it learn by interacting with the end users thereby supporting a positive user experience.

Real-life examples of Reinforcement Learning

Here are some real-life examples of reinforcement learning. Reinforcement learning can be used in different fields such as healthcare, finance, recommendation systems etc.

  • Self-driving cars: Reinforcement learning is used in self-driving cars for various purposes such as the following. Amazon cloud service such as DeepRacer can be used to test RL on physical tracks.
    • Trajectory optimization
    • Motion planning including lane changing, parking etc
    • Dynamic pathing
    • Controller optimisation
    • Scenario-based learning policies for highways
  • Data centre automated cooling using Deep RL: Use deep RL to automate the data center cooling. At regular time intervals, the snapshot of the data centre cooling system, being fetched from thousands of sensors, is fed into the deep neural networks. The deep NN predicts how different combinations of potential actions will impact the future energy consumption. The AI system, then, identifies the actions that will minimise the energy consumption. The most appropriate action is sent to the data centre. The recommended action is verified and implemented.
  • Personalised product recommendation system: Personalise / customize what products need to be shown to individual users to realise maximum sale; This would be something ecommerce portals would love to implement to realise maximum click-through rates on any given product and related sales, on any given day
  • Ad recommendation system: Customise / personalise what Ads need to be shown to the end user to have higher click-through rate
  • Personalised video recommendations based on different factors related to every individual
  • Customised action in video games based on reinforcement learning; AI agents use reinforcement learning to coordinate actions and react appropriately to new situations through a series of rewards.
  • Personalised chatbot response using reinforcement learning based on the behavior of the end user in order to achieve desired business outcome and great user experience
  • AI-powered stock buying/selling: While supervised learning algorithms can be used to predict the stock prices, it s the reinforcement learning which can be used to decide whether to buy, sell or hold the stock at given predicted price.
  • RL can be used for NLP use cases such as text summarization, question & answers, machine translation.
  • RL in healthcare can be used to recommend different treatment options. While supervised learning models can be used to predict whether a person is suffering from a disease or not, RL can be used to predict treatment options given a person is suffering from a particular disease.

There are several cloud-based AI / ML services such as Azure Personalizer that can be used to train reinforcement learning models to deliver personalised solutions such as some of those mentioned above.

Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data Science and Machine Learning / Deep Learning. In addition, I am also passionate about various different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia etc and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc. I would love to connect with you on Linkedin and Twitter.
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