We are living in a world full of remarkable innovations. Thanks to Deep Learning, Artificial Intelligence, Machine Learning, and Data Science, we are able to create technologies that have pushed our potential to a great extent. There is a fair amount of excitement around these concepts, especially about implementing them in our daily lives. Reinforcement Learning was first introduced in the 1950s as a subset of Machine Learning. Since then, it has evolved as a more formalized arena for research and development. In this article, we are going to dive into the concept by discussing real-life applications of Reinforcement Learning. But, before we get there, we first need to understand what reinforcement learning exactly is!
Reinforcement Learning in a nutshell!
Reinforcement Learning (RL) is a Machine Learning technique that trains algorithms with a trial-and-error approach by applying feedback received from its own actions. An algorithm also called an agent, learns to interact with its environment by receiving rewards or punishments based on its performance.
In RL, the algorithm is not programmed with exact steps to perform a task correctly, but it has to evolve on its own through its actions. It is like a child who learns from his experiences. The Agent decides to maximize its rewards or reduce its penalties with the help of dynamic programming. That’s how an AI generally learns on its own to perform various tasks without any human intervention.
Let us get a more clear picture of Reinforcement Learning by understanding some of its practical applications-
Real-life applications of Reinforcement Learning
Deep reinforcement learning in self-driving vehicles is the most trending application of RL. For self-driving cars, Reinforcement Learning helps in motion planning, dynamic paths, trajectory optimization, optimal control, and situational learning strategies in highways.
AWS DeepRacer is a good example of RL in autonomous driving. It is a self-driving racing car that uses Reinforcement Learning algorithms on actual tracks to improve itself. It visualizes the pathways with the camera and uses Reinforcement Learning to understand the control of its hardware system, like accelerator, brakes, and steering wheel.
Yes, Reinforcement Learning is being used to send recommendations to you!
Facebook is using Horizen, an open-source applied reinforcement learning platform for sending notifications to its users, which may include interactions on posts, updates about friends, page recommendations, events suggestions, etc.
Although user preferences are dynamic, the RL recommendation system can constantly track their feedback and accordingly update the suggestions. The recommender system works in the space of continuous interplays of the user and the update along with the features (the way users interact with content) describing the person to be notified. It sends notifications to the users, and in return, is rewarded with better engagement and activities on Facebook.
Reinforcement learning in the healthcare sector provides treatment strategies for the patients by utilizing their past experiences.
RL-based Dynamic treatment strategies (DTRs) use a sequence of decision rules for determining personalized treatment plans based on the patient’s history. It typically includes healthcare sectors of chronic diseases or intensive care, automated medical diagnosis, and other fields.
The input for DTRs is the patients’ data obtained from their clinical reports, and the output is the treatment plan for each stage. DTRs provide treatment to the patient in the form of medicines for a specific period. Moreover, the reward for DTR is based on the improvement or deterioration in the patient’s health.
Have you ever played any game against AI?
Reinforcement learning in games enables AI to play efficiently against a human opponent. Many video games and digital board games use RL to develop AI as your fellow player. It also allows AI to make decisions and modify behavior according to the strength of the opponent.
A well-known example of RL in gaming is AlphaGo and AlphaZero. AlphaGo learned the game of Go itself right from scratch and defeated the world champion of Go, Lee Sedol, just after 40 days of training. The algorithm works with a neural network and black and white pieces of Chess and Go as inputs. The neural network selects the next move to play and also predicts the winner of the game.
Finance and Trading
The biggest concern for traders in finance is the risk factor!
Reinforcement Learning in the finance sector is serviceable for option pricing, portfolio optimization, risk management, market-making, etc.
For example, in portfolio optimization, the platform should help generate an accurate forecast of stocks and other investments. The RL agent needs to create an optimum portfolio by maximizing or minimizing specific factors and considering the limitations.
Deep Q learning, A3C, PPO, and Policy Gradient are some RL algorithms that have been successfully implemented for portfolio optimization.
Reinforcement Learning is becoming more miraculous as science is speeding its research for advancing this field. The above-mentioned uses of RL are just a few of the many. Because of RL, technology is constantly learning by interacting with its environment. RL can be helpful for multiple industries to optimize their processes, monitoring, simulations, maintenance, and the improvement of autonomous systems.
If these examples of reinforcement learning have sparked your curiosity, then you should definitely dive deeper into the subject. Certified Machine Learning courses at SkilloVilla help you comprehend the topic smoothly by providing concept videos and LIVE interactive sessions with the Industry masters. By focusing on industry use cases, you can clearly understand the implementation of the theory in real life.
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