Reinforcement Learning for the Real World

The term Reinforcement Learning was given by Richard Sutton, he describes Reinforcement learning (RL) as the “first computational theory of Intelligence.” The Reinforcement learning  develops by interacting with the environment and actions are weightage based on rewards and punishments. RL agents develop policies that maximize rewards.

Reinforcement learning (RL) can be classified into two different types, i.e., Positive Reinforcement learning and Negative Reinforcement learning.

Positive Reinforcement learning: In positive reinforcement learning the positive behaviour of the PL agent is added into the existing ML model to perform this behaviour again and again. It works as a reward for the agent.

Negative Reinforcement learning: In negative reinforcement learning the negative behaviour of RL agents is not added to the existing ML model, hence encouraging them to perform better.

Learning python fundamentals can bring dynamic changes in various sectors, such as Finance, Healthcare, Advertisement, Manufacturing, Marketing, Robotics, and many more.

Let’s look into the various sectors where RL is used in the real world.

  • Reinforcement learning in Robotics: The tremendous work that Reinforcement learning is doing in the field of robotics by training Robots. We all know that robots can perform tasks like a human being can, but one of the major drawbacks is that robots are not able to use common sense to make moral or social decisions. To solve this problem Deep Learning and Reinforcement Learning works together as a Deep Reinforcement learning to make a robot to “Learn How to Learn” model. This is helping robots to solve various complicated problems and make decisions by grasping various objects visible to them.
  • Reinforcement learning in Resource Management for Computer Clusters: To design an algorithm to allocate limited resources to the different tasks is a challenging job. The paper “Resource Management with Deep Reinforcement Learning show how reinforcement learning learns to allocate and schedule resources to all waiting jobs and also minimize the average job slowdown.
  • Reinforcement learning in Healthcare: Healthcare is one of the important sectors for all. The Dynamic Treatment Regimes (DTRs) help the doctor to discover the treatment type, appropriate dose of the medicine and time to take those medicines. DTRs is a sequence-based use case that helps to confirm the current health status of the patient. DTRs is used for personalized health treatment plans, typically for chronic conditions.
  • Reinforcement learning in Marketing: Marketing is all about promoting and selling goods and services. Companies take risks whenever they change their pricing model. It is done by keeping in mind the past data and customer buying patterns. However researchers from New York University have designed unique algorithms that help to predict the customer reaction in advance by simulating the changes in the pricing model, this algorithm is known as Inverse Reinforcement Learning.
  • Reinforcement learning in Gaming: Reinforcement learning is well known these days in gaming because of its mainstream algorithm, which is used to solve different gaming problems. For example, Alpha Go defeated one of the strongest GO players in October 2015. The Alpha Go was designed using Reinforcement learning which was trained by countless human games. It achieved super-human performance by using value network and Monte Carlo tree search (MCTS) in its policy network.
  • Reinforcement learning in Online Recommendation: Reinforcement learning is providing a personalized user experience and bring change to many online companies. The traditional model of online recommendation is not so effective, it can be due to customer bias, unavailability of data, etc, due to this the recommendations get affected. Chinese Nanjing University and Alibaba Group came together to build a reinforcement learning algorithm for the online recommendation which is known as Robust DQN, which works impressive and give results in a real-world environment.
  • Reinforcement learning in Manufacturing: As we know, robots are used in many manufacturing and industrial areas, and these are becoming much more powerful by leveraging reinforcement learning. One of the Japanese companies named Fanuc are working on industry-based robots and now they are actively working on incorporating deep reinforcement learning in their robots. Fanuc is now collaborating with other industry giants such as Cisco, Rockwell Automation and NVIDIA to build intelligent robots based on Artificial Intelligence (AI). Their deep reinforcement learning model trains robots on their own and they can perform most of the basic tasks easily.
  • Reinforcement learning in Reduction of Energy Consumption: One of the biggest tech giants company Google is uniquely using reinforcement learning. Google has multiple data centres that get heated up extremely, so to minimize this problem Google uses AlphaGo built by DeepMind to determine the optional method to design the cooling infrastructure. AlphaGo is providing recommendations that how energy can be used efficiently for cooling data centres. And the results show that they were a 40% reduction in energy requirement and bring a huge reduction in costs.
  • Reinforcement learning in Image Processing: Image processing is one of the important fields where the quality of an image is enhanced to extract useful information from the image. With the help of Deep Neural Network, we can enhance the quality of the image or hide any specific region from the image and these images can be later used for computer vision tasks.
  • Reinforcement learning in Industrial Machine Teaching: The RL is also used to teach a machine how to work. The startup company Bonsal, which was specialized in machine learning and was acquired by Microsoft in 2018. Then Microsoft announced the project called Project Bonsal that was used for Machine Teaching for the autonomous industrial control system. The autonomous industrial machine was trained using reinforcement learning in their simulation environment to make them intelligent to perform operations on their own.

So, this was some amazing use of reinforcement learning in the real world. Although reinforcement learning is being used by a small number of companies. But with the growth in technology, reinforcement learning will be used by many other companies.



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