Yesterday, we hosted the 2022 Future of Data Analytics event, during the medley of talks from some of the industries brightest a portion of the audience showed a lack of understanding about one of the core concepts behind advances in Machine Learning (ML): Reinforcement Learning (RL).
Reinforcement learning is an ML trainng method that allows computers to learn from experience and improve their performance by modifying their behaviour based on feedback. This is done by using a trial and error process, where the system improves its performance each time it receives a new sample of data. In RL, the computer itself decides which actions to take based on its previous experiences. There are two main approaches to RL – supervised and unsupervised learning. The former allows humans to provide input to the computer, while the latter requires no human input and can be used to learn how to operate a system completely autonomously.
When applied to business systems, RL can be used to improve the performance of individual systems and entire networks by making small changes to their behavior over time. For example, with RL, a self-driving car could learn how to drive faster or more efficiently depending on the road conditions it is faced with at any given time. It can also be used to improve the efficiency of industrial equipment by learning how to perform certain tasks better over time. This is generally achieved by collecting feedback from the user and monitoring the performance of the system based on this feedback.
How do RL algorithms train themselves?
Essentially, an RL algorithm uses trial and error to "learn" to perform a task without being explicitly programmed how to do so.
- Input: The input should be an initial state from which the model will start
- Output: There are many possible outputs as there are a variety of solutions to a particular problem
- Training: The training is based upon the input, The model will return a state and the user will decide to reward or punish the model based on its output.
- The model keeps continues to learn.
- The best solution is decided based on the maximum reward.
What is RL's relationship with Machine Learning?
Reinforcement learning is a type of machine learning that uses the principles of operant conditioning, where the system uses rewards for correct behavior to increase performance over time. It is based on the idea that most behaviors are caused by a combination of individual differences and environmental circumstances, and that behavior can be influenced by modifying the environment through positive or negative reinforcement.
This type of machine learning model was originally developed to study animal behavior and has since been used in many fields including robotics, finance, self-driving cars, and gaming. In recent years, it has been increasingly used in business applications to help train systems to make business decisions and improve their performance over time.
For example, one popular application of RL is in the area of image recognition. Many different technologies have been developed that are designed to make automated image analysis easier and more accurate by teaching the systems to learn to recognize objects from images using example data. These algorithms then use this data to train the system to recognize similar objects in new images so that it can make more accurate decisions about how to perform a particular task.
How is RL Used in Business Applications?
One of the biggest areas where companies have been experimenting with the application of RL is in customer service applications. Companies such as Amazon and Netflix have used this approach to develop algorithms that are able to respond automatically to different customer requests by sending them targeted responses based on their preferences. Other companies have also used it to develop a personalized experience for their customers by identifying their preferences and providing a customized shopping experience.
Another common area where businesses have been experimenting with the use of RL is in marketing campaigns. Companies such as Proctor & Gamble have been using this technique to identify the most effective advertising channels for their products by analyzing which messages were most effective at reaching their target customers and optimizing.
How does RL differ from rules-based algorithms?
RL differs from rules-based learning because, unlike rules-based algorithms, RL allows machines to learn about complex scenarios and make decisions based on uncertain or incomplete information. It is also better suited to tackling complex problems where there may be multiple conflicting objectives or situations where action could have unexpected consequences.
Can Artificial Intelligence (AI) cheat the system?
As we noted in a recent talk about the importance of explainable AI, AI can cheat RL algorithms if its actions are not explained by its underlying reasoning process. This can lead to unpredictability and unexpected behavior, which can impact the reliability and effectiveness of a system.
In brief, an explanation is a formal description of how an AI algorithm makes a decision about a particular situation. The goal of an explanation is to enable humans and stakeholders to understand how the AI system arrived at its decision. By providing an explanation for why the AI reached a certain decision, the humans who rely on the output of the system can make more informed decisions about how to use the system moving forward.
Ultimately RL is the bridge between AI and your business. With companies ranging from Toyota, Domino’s Pizza, Google, and Amazon leveraging RL to enhance their products and services, RL is a staple of enterprise today.